White Papers

Read 12 Requirements for a Modern Data Architecture in a Hybrid Cloud World to learn the key characteristics to look for in a modern data platform: -Spanning on-premises and cloud infrastructures -Handling data at rest and in motion -Managing the complete data life cycle -Delivering consistent data security, governance and control -Providing data-driven insight and value


Join us for a demonstration of the Cloudera Data Platform (CDP) experience. The industry’s first enterprise data cloud brings together the best of Cloudera and Hortonworks. CDP delivers powerful self-service analytics across hybrid and multi-cloud environments along with sophisticated and granular security and governance. In this webinar, we will highlight key use cases and demonstrate unique CDP capabilities including intelligent migration, adaptive scaling, and cloud bursting.


Read this white paper to learn: - Why you need a data strategy first and a hybrid cloud one second - Why you must see cloud as infrastructure, not data architecture - Why open source is crucial to success - How to balance business and IT needs - How to keep sensitive data secure and compliant


An enterprise’s IoT initiative is only as good as its ability to harness the value of the data captured from its IoT devices in real-time. The enterprise will require an IoT data management solution to see true ROI with their IoT initiatives.


Edge computing moves compute to where the data is generated, eliminating the need for it to travel to an external datacenter for analysis and removing many of the barriers to gaining – and acting on – real-time data.


A Gartner survey found that 80% of respondents using public cloud are using more than one cloud service provider. This report assesses the shift to multicloud architecture, challenges being faced by data and analytics leaders, and the growing need for database management technology that enables integration of data across multiple clouds.


This report focuses on relational analytical databases in the cloud, because deployments are at an all time high and poised to expand dramatically. The cloud enables enterprises to differentiate and innovate with these database systems at a much more rapid pace than was ever possible before. The cloud is a disruptive technology, offering elastic scalability vis-à-vis on-premises deployments, enabling faster server deployment and application development, and allowing less costly storage. For these reasons and others, many companies have leveraged the cloud to maintain or gain momentum as a company.


In early 2018, Actian released Actian Avalanche, a high-performance cloud data warehousing (CDW) service. EMA believes this release signals the maturing of the CDW market and a shift towards high-performance CDW. As data volumes, users, and complexity of use cases expand, the limitations of early market offerings will continue to be overcome. This white paper provides an EMA update on the state of the CDW market and provides insight into how Actian Avalanche meets new challenges.


This white paper covers the multiple analytics disciplines of data for an enterprise data cloud. To be able to process and stream real-time data from multiple endpoints at the edge, while predicting key outcomes and applying machine learning on that same data set. To be able to take advantage of public cloud infrastructure for its elasticity and self-service capabilities. And to be able to do all of this on an open platform, where consistent security and governance policies are applied everywhere the data lives and analytics run.


This white paper explores the risks of legacy enterprise content management (ECM) systems, how to reduce infrastructure and support costs, why modern alternatives provide a competitive advantage, and the top traits of leading-edge solutions. This paper helps CIOs and senior enterprise architects determine if legacy systems are causing scalability, performance, or other content management problems and why legacy ECM replacement or strategic modernization is critical now, not later.


From the rise of cloud computing, machine learning and automation, to the impact of growing real-time and self-service demands, the world of database management continues to evolve. Download this special report to stay on top of new technologies and best practices.


Welcome to the era of the digital enterprise, where digital is your journey and cognitive is your destination. As business leaders, you are under growing pressure to use information to its fullest potential, delivering new customer experiences as fuel for business growth. The digital economy is changing the way we gather information, gain insights, reinvent our businesses and innovate both quickly and iteratively.


This paper outlines what readers should consider when making a strategic commitment to a database platform that will act as a bridge from legacy environments to the cloud.


In this report, we'll look at how avoiding Cloud is actually a barrier to growth for many companies. We'll also analyze how businesses that are leaders in IT infrastructure are getting the best out of both their public and on-premise systems to build a hybrid cloud enterprise that brings all the benefits of both models while limiting or removing the concerns.


In this report, we'll analyze the many challenges that organizations face when it comes to building and managing modern IT infrastructure. We'll also look at how some businesses are taking advantage of a hybrid cloud and on-premise approach, which comes with significant benefits.


As the foundation for most critical business decisions, today’s data environment is not just a vital piece of IT infrastructure but also a key component of corporate strategy. However, as the user base has expanded and data has become more diverse, our methods for storing, managing, and organizing data must adapt. This report explores a new breed of data warehouse that can operate in a world of legacy on-premises systems while exploiting the potential of cutting-edge technologies and deployment styles.


In September 2018, IBM commissioned Forrester Consulting to evaluate the state of data and analytics strategies. Forrester conducted an online survey of 253 US and EMEA enterprises to explore this topic. We found that effective data management has become more complicated in recent years. Enterprises look to fast data solutions to help, but challenges prevent them from achieving the necessary functionalities to reap business benefits.


Organizations must find new ways to take advantage of big data. To overcome the limitations of traditional data warehouses, many have begun incorporating data lakes into their data management strategy.


IBM Db2, Oracle Database, MySQL, and Microsoft SQL Server continue to be dominant enterprise-ready databases, each supported by various hardware from established vendors. This paper presents a cost/benefit case for two industry-leading database platforms for analytics workloads—IBM Power Systems with AIX running IBM Db2 11.1 with BLU Acceleration, and Oracle Exadata Database Machine configured with Oracle Linux and Oracle Database 12c Release 2


This paper presents a cost/benefit case for two leading enterprise database contenders—IBM DB2 11.1 for Linux, UNIX, and Windows (DB2 11.1 LUW) and Oracle Database 12c—with regard to delivering effective security capabilities, high-performance OLTP capacity and throughput, and efficient systems configuration and management automation.


The days of data being narrowly defined as highly structured information from a few specific sources is long gone. Replacing that notion is the reality of a wide variety of data types coming from multiple sources, internal and external, to an organization. All of it is in service of providing everyone from IT, to line-of-business (LOB) employees, to C-level executives with insights that can have an immediate and transformative impact.


The IBM Db2 solution is a multi-workload relational database system that can improve business agility and reduce costs by helping you better manage your company’s core asset: data. And now, this system is available on demand as a cloud service, which provides numerous advantages.


In order to exploit the diversity of data available and modernize their data architecture, many organizations explore a Hadoop-based data environment for its flexibility and scalability in managing big data. Download this white paper for an investigation into the impact of Hadoop on the data, people, and performance of today's companies.


The Internet of Things (IOT), artificial intelligence (AI), social media and mobile applications are driving an increase in data volume, velocity and variety. To capitalize on this trend and obtain faster actionable insights, organizations are deploying Apache Hadoop.


The life of an enterprise architect is becoming busy and difficult. Before the era of big data, the enterprise architect “only” had to worry about the data and systems within their own data center. However, over the past decade there were revolutionary changes to the way information is used by businesses and how data management platforms support the information available from modern data sources.


The single data warehouse repository simply could not support any and all analytics anymore. A new architectural concept called the Extended Data Warehouse architecture (XDW) has taken the place of the single repository idea. It accommodates the new forms and volumes of data, the need for different sub-environments for varying analytical requirements, and the immensely innovative technologies available today.


One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.


Cloudera DataFlow (CDF) is a scalable, real-time streaming data platform that collects, curates, and analyzes data so enterprises can gain key insights for immediate actionable intelligence. CDF addresses some of the key use cases of an enterprise from data ingestion, stream processing to real-time streaming analytics. With the recent merger with Hortonworks, the new Cloudera has emerged as an Enterprise Data Cloud company with a vision that spans from the Edge to AI.


Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.


Businesses of all kinds – large and small, young and old, and in modern and traditional industries – are employing software in more ways than many could have imagined even a decade ago. Software applications and services support the myriad ways their customers prefer to interact with them and enable internal constituents to meet critical business goals.Enterprise IT Ops teams supporting complex IT stacks need to embrace AI and Machine Learning driven automation – and become autonomous over time. Download this report to learn why.


In less than one year's time, regulators in California will start enforcing the requirements of the California Consumer Privacy Act (CCPA). Security and privacy professionals must repurpose their GDPR programs to comply with CCPA and address privacy globally. This report outlines the main steps companies must take today to kick off their preparation for CCPA.


A holistic data protection strategy that includes encryption can reduce data breaches and privacy concerns. Stephanie Balaouras, Research Director at Forrester Research discusses the importance of a data encryption, how to get started on your data encryption strategy; why the cloud is a major use case for encryption; and why the savviest companies prioritize data privacy not only for compliance, but with customers' best interests in mind.


Hybrid cloud has arrived. Whether companies have intended to move to hybrid cloud or not, the hybrid cloud reality is here, and while the advantages are numerous, so are the challenges. From data governance to scaling real-time applications, this white paper explains the main issues enterprises typically face with hybrid cloud.


Mainframe technology is struggling to keep up with the data demands of today’s businesses. And yet, many businesses continue to rely on mainframes in spite of growing concerns around security, capacity, and performance. This white paper explains how adopting a data modernization strategy that incorporates your mainframe can save you millions of dollars in operating costs.


DataStax Enterprise and Apache Kafka are designed specifically to fit the needs of modern, next-generation businesses. With DataStax Enterprise (DSE) providing the blazing fast, highly-available hybrid cloud data layer and Apache Kafka™ detangling the web of complex architectures via its distributed streaming attributes, these two form a perfect match for event-driven enterprise architectures.


Data management challenges have evolved drastically over the last decade, leading most companies to rethink how they manage their data. The need for more powerful and far more flexible databases resulted in the birth of the NoSQL database Apache Cassandra™. Read this white paper to learn how Cassandra has evolved and how it works.


Apache Cassandra™ comes with the typical benefits of any NoSQL database, and much more. When enterprises need something easily scalable and ready for today’s hybrid cloud environments, it’s hard to find a database better suited for the job than Cassandra. From performance to availability to hybrid cloud readiness, this ebook explains the five main benefits of Cassandra.


This white paper explores how GPU databases enable the rapid analysis of massive volumes of data, resulting in fast, accurate, up-to-the-minute dashboards from which valuable new insights can be extracted. It also explains how you can accelerate your existing BI pipeline to gain fast, unrestricted, ad-hoc access to your organization’s full scope of data, even when data grows exponentially. Download this whitepaper to learn: • Why raw data is much more powerful than pre-aggregated and cubed data • Why distributed databases struggle with complex JOINs - and the secret to performing them with ease • How GPU-acceleration offers value-added SQL capabilities that scale with your business


Data analytics is no longer the luxury of organizations with large budgets that can accommodate roving teams of analysts and data scientists. Every organization, no matter the size or industry, deserves a data analytics capability. Thanks to a convergence of technology and market forces, that’s exactly what’s happening. Download this special report to dive into the top technology trends in analytics today and why 2019 is becoming a year of transformation.


AIOps platforms enhance IT operations through greater insights by combining big data, machine learning and visualization. I&O leaders should initiate AIOps deployment to refine performance analysis today and augment to IT service management and automation over the next two to five years.


IT Ops reporting is broken. And it needs to be fixed so IT executives can digitally transform operations and NOC managers can easily analyze and report on operational KPIs. Unified Analytics solves this, giving IT leaders visibility into IT Ops performance. Get the whitepaper to learn more.


Companies are digitally transforming applications and infrastructure, but IT Ops is stuck in the past. Autonomous Operations is a new category of enterprise software powered by machine learning that intelligently automates incident management for large and complex enterprise IT environments.


IT operations is a battle with complexity—and IT operations and NOC teams are losing. Tool proliferation and IT operations noise drive up headcount and force IT operations teams into a reactive mode that increases costs and decreases customer satisfaction. This executive brief examines the role AI and ML play in enabling autonomous operations to cut through IT noise and identify root causes before they impact service.


Learn why more intelligent device manufacturers are turning to Vertica to analyze large volumes of IoT data for predictive maintenance solutions. This webcast will preview a predictive maintenance demonstration that shows how Vertica core technology and in-database Machine Learning enables equipment manufacturers to accurately pinpoint maintenance issues before they occur.


Watch this webcast to hear how Philips Research is moving towards zero unplanned downtime of medical imaging systems using remote monitoring and predictive analytics, powered by Vertica. Philips collects and processes data from devices to identify potential problems, reduce the likelihood of costly downtime and minimize impact on patients.


Read this case study to learn how Optimal Plus helps suppliers improve product quality, yield, throughput, and performance using analytics of semiconductor and electronics manufacturing data. Optimal Plus fosters uptime of plant assets by enabling models of historical data vs. streaming data and algorithms that predict equipment faults.


Read this report to find out how the Vertica Analytics Platform addresses your IoT data challenges by blending the performance of massively parallel processing (MPP) with SQL analytics and key IoT-enabling functionality to enable you to derive greater value from your growing machine data volumes.


TigerGraph's new eBook "Native Parallel Graph: The Next Generation of Graph Database for Real-Time Deep Link Analytics," discusses what developers need to learn and leverage the power of scalable and MPP graph analytics for the most complex business problems. Learn how the world's most innovative companies such as Alipay, Uber, Wish, and Zillow are using real-time graph analytics to gain deeper insights and better outcomes.


When it comes to choosing a graph database, speed is one of the most important factors to consider. How fast can you query? How quickly will data load? How fast are graph traversal response times? This benchmark study examines the data loading and query performance of TigerGraph, Neo4j, Neptune, JanusGraph, and ArangoDB.


In this report, hear 451 Research’s Voice of the Enterprise: Internet of Things, Workloads and Key Projects shows that analytics is critical to the success of Internet of Things (IoT) projects and that processing of IoT data is increasingly being carried out at the edge. With DataFlow, Cloudera already had a differentiated offering for processing and analyzing data in motion.


Cloudera Fast Forward Labs is a research subscription service that applies emerging machine learning techniques to practical business problems. Paired with advising services and working software prototypes, Cloudera Fast Forward Labs will keep you ahead of the machine learning curve.


Read this analyst report for an in-depth and un-bias view of the analytic data warehouse infrastructure market. Dresner Advisory Services examines topics such as performance, security, on-premises versus cloud, to advanced analytics with big data and more.


Cloud Hadoop/Spark (HARK) platforms accelerate insights by automating the storage, processing, and accessing of big data. In our 25-criterion evaluation of HARK providers, we identified the 11 most significant ones — Amazon Web Services (AWS), Cloudera, Google, Hortonworks, Huawei, MapR, Microsoft, Oracle, Qubole, Rackspace, and SAP — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise architecture (EA) professionals select the right one for their needs. Note: Cloudera and Hortonworks completed their planned merger on January 3, 2019, and will continue as Cloudera. This Forrester Wave reflects our evaluation of each company's independent HARK platforms prior to the completion of the merger.


Apache NiFi is an integrated data logistics and simple event processing platform.It provides an end-to-end platform that can collect, curate, analyze and act on data in real-time, on-premise, or in the cloud with a drag-and-drop visual interface.


Hybrid cloud is an obvious strategy, and despite cloud migration interest, it’s here to stay. Cost of change and compliance are the two biggest proof points for hybrid’s continued relevance, but no single deployment model serves all use cases. This report serves as a primer on multicloud and hybrid cloud to help infrastructure and operations (I&O) pros and leaders understand hybrid definition, current status, best practices, and how hybrid has advanced over the past few years.


While the earliest work in artificial intelligence began decades ago, only more recently with the emergence of big data and increased computing power has it become a reality. Mike Olson, Cloudera’s Chief Strategy Officer, explores the history of artificial intelligence, analyzes previous and current challenges, and outlines the future path to industrialized AI. Read this Cloudera eBook to separate the hype from real capabilities and learn what is practical today for organizations that want to use this technology.


Big data can produce a lot of value, but only if you know how to claim it. When you make big data analytics available to everyone, you create the conditions for faster, smarter innovation. The next big idea that transforms your business can now come from anyone in any line of business – not just your data scientists.


As long as databases continue to evolve, so too will our role as a DBA. There’s nothing wrong with plugging away at the same DBA duties you’ve known all these years. But eventually trends like DevOps, multi-platform databases and the cloud will cause those duties to change. The sooner you can identify and pursue the opportunities each trend brings, the sooner you can move past the zombie stage of database administration and on to the high-value tasks that turn DBAs into true partners in the business.


Organizations today are under tremendous pressure to quickly deploy new software and updates to their production applications in order to cope with intensely competitive markets and the rapidly evolving technology landscape. To meet this challenge, more and more organizations are turning to DevOps, a set of practices that emphasize collaboration and communication between development, operations and other functional areas to enable the building, testing and release of software in a rapid and reliable fashion.


Sure, there’s some doom and gloom out there about the future of the DBA. But in this session, you’ll learn how to adapt, evolve, survive and even thrive in a changing database world. You’ll get to see real-world statistics on the evolving role of the DBA, common DBA concerns and valuable insights for career success. You’ll learn how the trends of DevOps, cloud, NoSQL, big data and more will shape the future.


This technical brief outlines the top five complications faced by DBAs amid the rush of new database technologies in recent years. For each challenge it provides background, context and the benefits Foglight for Databases brings in addressing the challenge.


We’re bringing blocking locks into the interrogation room in this final installment of our webcast series. Blocked SQL statements are a common cause for database performance issues. When an application is poorly written, lock issues can impact the application’s performance. Having the right tool to diagnose lock issues is essential. We’ll show you how Foglight Performance Investigator makes it easy to resolve the root cause of lock issues, bringing law and order back to your database environment.


In this session, we’re setting up a coding crime lab to investigate plan change. Because running SQL statements with the right execution plan is crucial for database performance. But in some cases, due to various reasons, such as object changes and statistics changes, the database may pick the wrong execution plan. We’ll explore how Foglight Performance Investigator can simplify execution plan analysis and provide powerful clues that help DBAs and developers better understand execution plans.


You’re the Sherlock Holmes of IT, always in demand to solve performance mysteries. But even Sherlock needed Watson to do the footwork for him. In the same way, you need smart tools to keep your database environment running efficiently, so you can spend less time on menial tasks and more time on strategic initiatives. In this first episode, we’ll explore the Foglight® Performance Investigator analytics toolset and how it’ll make you the ultimate database detective.


Foglight SQL PI enables DBAs to address these challenges with visibility into database resources, proactive alerts, advanced workload analytics, change tracking and more. Armed with these tools, DBAs can get a complete picture of their environment to find and fix performance issues before they put the database at risk.


With 90% of the world’s data having been created in the past two years, conventional extract-transform-load (ETL) technology simply can’t keep up, and data pros are increasingly having to retrofit traditional batch-based ETL processes in an attempt just to keep pace. With next generation ETL technology like data streaming and data beaming though, businesses can now seamlessly move operational data to real-time analytics environments and digital customer touchpoints.


In today’s increasingly digital world, data has become a key factor in business success, with how you process it now a critical question facing many. To support a truly data-driven business, data-driven leaders need the very latest information on data management technology. In this e-guide, we’ll examine the current and projected challenges with ETL and other forms of data processing, before exploring the next generation of ETL and learning how it overcomes the traditional limitations. We’ll also look at the problems faced by data pros, like data engineers, data operations teams and data analysts, and the ways you, as a data-driven leader, can help them find new, enterprise grade solutions to data management.


Supplying business leaders with the insights for real-time decision-making is no easy feat. In most enterprises, key operational data is spread across hundreds of different systems, so a common practice is to extract and move all relevant data to a central location for analysis (for example, an enterprise data warehouse / data lake / enterprise data hub). But since most operational systems write all their data to a relational database, one of the main challenges to enabling real-time analysis is how to capture changes from the relational databases in real-time?


Regulation of data privacy, rights of individuals around their personal data, and personal data security is evolving rapidly with the introduction of the GDPR, the pending launch of CCPA, and other coming regulations. For organizations to thrive in this climate, they will need to adopt a data privacy approach that is flexible enough to support ongoing regulatory changes with minimal operational disruption.


Right now, we are experiencing a perfect storm of factors changing the competitive dynamics of many industries. A confluence of technologies — including cloud, artificial intelligence, machine learning, and the Internet of Things — are helping companies aggregate, process, analyze, and act upon ever-growing volumes, variety, and velocity of data. There are also new business models emerging to disrupt incumbents with well-established ways of solving problems in their sectors.


The pressure on companies to protect data continues to rise. In this year’s Cyber Security Sourcebook, industry experts shed light on the ways the data risk landscape is being reshaped by new threats and identify the proactive measures that organizations should take to safeguard their data. Download your copy today.


Get complimentary access to the Forrester report to learn how the top 12 vendors stack up.


What it is. Why you need it. And how to find the right one.


In this TDWI report you’ll get a checklist of six tactics that demonstrate how your company can get value from your analytics.


In a world where customers "crave self-service," having the technology in place to allow them to do this—and do it swiftly, efficiently, and correctly—is critical to satisfying customers.


Data warehouses are poised to play a leading role in next-generation initiatives, from AI and machine learning, to the Internet of Things. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to JSON data and automation. Download this special report for the top trends, emerging best practices and real-world success factors.


Database deployment projects are inevitably time constrained. That often leaves little room for considering the many factors that make projects safer and more sustainable – such as reviewing vendor documentation, properly configuring you environment, performing reasonable testing and calling on consultants or trainers as needed.


Interested in learning how much you could save by moving off Oracle Enterprise? This total cost of ownership (TCO) analysis is for you.


This white paper provides a comprehensive overview of the high availability features and options available within MariaDB Platform – everything from replication with automatic failover to topologies for multiple data centers.


"Digital transformation can only bring value if it supports what the business is trying to achieve. Viewing information as a single entity, connected through technology, is crucial to positioning modern organizations to cope with the challenges they face is a rapidly changing business environment."


In today's world of frequent account takeovers, credential compromises, and automated attacks, it's more important than ever to protect your customers. That's why Okta and Shape Security have partnered to offer a comprehensive identity-first security strategy that is ideally suited to prevent both automated and targeted identity attacks.


APIs are often created as needed in an ad-hoc fashion. API usage grows organically from small, tightly-controlled groups within the organization to external partners and third-party developers. As adoption of the API increases, managing access can represent a serious security risk. Learn how to solve these challenges by reading this infographic.


APIs are often created as needed in an ad-hoc fashion. API usage grows organically from small, tightly-controlled groups within the organization to external partners and third-party developers. As adoption of the API increases, managing access can represent a serious security risk. Learn how to solve these challenges by reading this datasheet.


The ability for knowledge graphs to gather information, relationships, and insights – and connect those facts – allows organizations to discern context in data, which is important for extracting value as well as complying with increasingly stringent data privacy regulations. Download this special report to understand how knowledge graphs work and are becoming a key technology for enterprise AI initiatives.


With a few clever steps in technology architecture – focusing on mature, proven solutions instead of buzzy emerging technologies, organizations can deliver timely, essential data to business analysts, BI teams, and data scientists in an agile way. Read this knowledge brief to learn how you can champion this effort.


Read this report by Eckerson Group - DataOps: Industrializing Data and Analytics to learn various the principles, use cases and software solutions that contribute to effective DataOps initiatives. Also learn how to avoid the common pitfalls with implementing DataOps. Then see how Attunity fits into your DataOps initiatives.


Enterprises have to choose among the best tools, be it on premise, cloud-based, single purpose etc. They need to have a future-proof data integration strategy, as well as a view of present data integration use cases that make sense from a financial perspective.


Understand why you need a hybrid integration platform to address integration challenges in a Cloud-First World. Also learn what are the 4 essential elements that make a hybrid integration platform.


For IT professionals like you, data integration is a critical starting point in your information architecture plan. Having a solid understanding and knowing the crucial pieces of the puzzle in advance will help you be better prepared for your organizations digital journey. Read this eBook to better prepare for your digital journey.


Watch this webcast to hear how Catch Media enables content owners and distributors to achieve 50%+ increase in engagement through the segmentation of end consumers into distinct audiences that can be targeted in near real-time with messaging and recommendations personalized to their content consumption behavior.


Find out how Vertica’s in-database machine learning supports the entire predictive analytics process with massively parallel processing and a familiar SQL interface, allowing data scientists and analysts to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.


Maxcom Telecommunications needed to scale their analytics in order to respond to changing government regulations, improve fraud detection management, and provide superior customer service. Find out how Maxcom was able to perform analytics 60x faster and reduce fraud-related costs by 85% using Vertica Analytics Platform.


Watch Emon Haque, Senior Product Manager at Anritsu, discuss how Vertica enables Anritsu to deliver advanced predictive network analytics for Telecom Service Providers, allowing them to improve customer experience and optimize service performance for subscribers in real time at lower cost.


Find out how SysMech uses an entirely new approach to network management based on metadata from every element of the network. Leveraging Vertica, SysMech can give telcos on-the-spot information and operational intelligence to optimize their networks.


Read this Mobile World Live whitepaper to find out how carriers can take advantage of ultra-connectivity and the era of 5G with new data management platforms, using predictive analytics and machine learning to correlate network, user, device and application activity for greater customer gains and operational efficiencies.


Data lakes help address the greatest challenge for many enterprises today, which is overcoming disparate and siloed data sources, along with the bottlenecks and inertia they create within enterprises. This not only requires a change in architectural approach, but a change in thinking. Download this special best practices report for the top five steps to creating an effective data lake foundation.


Organizations continue to struggle with integrating data quickly enough to support the needs of business stakeholders, who need integrated data faster and faster with each passing day. Traditional data integration technologies have not been able to solve the fundamental problem, as they deliver data in scheduled batches, and cannot support many of today’s rich and complex data types. Data virtualization is a modern data integration approach that is already meeting today’s data integration challenges, providing the foundation for data integration in the future.


With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. Data virtualization transcends the limitations of traditional data integration techniques such as ETL by delivering a simplified, unified, and integrated view of trusted business data.


Data-heavy apps are complex. Though Kubernetes is the de-facto standard for microservices and statelessapplications, the game changes withstateful data-heavy applications. Buildingupon more than 15 years of experience ofrunning production workloads at Google,Kubernetes brings incredible agility toyour private clouds. Kubernetes providesseveral building blocks needed to build aproduction-ready private cloud. By design,it is not a turnkey solution because it is designed to provide the flexibility to its users to select their preferred components.


This white paper provides a useful guide, exploring some of today’s leading enterprise cloud search solutions, unpacking their key features, and highlighting challenges that can arise in their deployment.


You’ve heard the stories [or shall we say nightmares] about Oracle audits. And you have always been fearful of what an Oracle audit might reveal should your organization be the subject of an audit. And rightfully so, as there are many missteps one can take throughout the audit process that can take your audit from bad to worse. Outlined in this guide are the best kept secrets for dealing with an Oracle audit at every step of the process.


Organizations know they need to be more data-driven but many feel unprepared (and possibly unbudgeted) to implement an intensive full data science-driven analytics platform. What if you are ready to get more value from your data but aren't sure what options are available? And what about data integration? Learn more about successful models that you can implement to up your analysis game, including empowering your current data and business analysts to meet their own reporting and data prep needs.


Trends show you’re far from alone in your increasing adoption of open source databases like MySQL, PostgreSQL and flavors of NoSQL. By one estimate, open source database management systems (OSDBMS) will account for almost three-fourths of new, in-house applications by 2018, and about half of existing relational DBMS will be in some state of conversion by then.


Read this e-book for walk-throughs, implementation guidelines and links to videos that show how to use Toad® for Oracle Developer Edition and Toad Intelligence Central to automate database development processes, and realize the full promise of agile: the ability to release software in prompt response to market changes.


Many organizations are now embracing the cloud in order to help reduce their operational costs, but the notion of migrating an Oracle 12c multi-tenancy database from on-premise to the Oracle Cloud may seem like a daunting proposition. After all, it will require a dramatic shift in the way you do your job – from performance testing, to administration, to management and ongoing maintenance. In the cloud!


If you use Oracle technologies, you may be relying on Oracle Enterprise Manager (OEM) to manage your clouds, applications and databases. But if you’re responsible for multiple environments with hundreds of databases as well as Oracle Real Application Clusters (RACs) and Oracle Exadata, then OEM alone will not enable you to do your job effectively.


Managing data environments that cross over from on-premises to public cloud sites requires different approaches and technologies than either traditional on-premises data environments or fully cloud-based services. Following the eight rules outlined in this special report will help data managers stay on track. Download today.


Check out our 1-minute whitepaper to learn about the most common methods used to execute account takeover attacks and what an organization can do to protect against them.


Read our datasheet to learn about Okta's Adaptive Multi-Factor Authentication product and how it can protect against various forms of consumer account takeover and fraud. Take a look at the technical components of Okta's account prevention stack and how Okta Adaptive MFA enables seamless, friction-free user experiences.


As you and your team look to invest in modern data preparation solutions, understanding how to evaluate the new technologies can be difficult. There are a slew of new vendors entering the market, new end user requirements to meet, and new features coming on line. How do you choose the right data preparation solution?


Learn how to simplify the migration and upgrade process so you can avoid the risk, downtime and long hours normally associated with it – especially with a database upgrade.


A guide to picking the right data warehouse infrastructure for analytics service providers


Applying IoT analytics to real business opportunities requires designing an IoT data architecture that handles massive numbers of devices and data streams, can rapidly adapt to change, and can deliver and apply analytics results where they are needed—all while maintaining proper security and data governance.


The growing emphasis on digital transformation is encouraging more organizations to adopt initiatives driven by the Internet of Things (IoT). In this eBook, we will discuss the challenges of implementing data-driven IoT, and solutions for addressing the challenges across multiple industries.


Must you scrap your enterprise data warehouse (EDW) for a data lake? Dr. Barry Devlin of 9sight Consulting says No! In this white paper, he shows that enterprise data lakes and data warehouses are complementary and are essential elements of a new EDW architecture. Data warehouse optimization enables each to serve a different set of business and technical needs and work together to help you get the most value from your business data.


Learn how to drive machine learning projects forward from exclusive Gartner research. According to a Gartner Data Science Survey conducted at the end of 2017, effective data science teams use portfolio management techniques and significant numbers of KPIs to plan for data science projects. In this latest research, uncover the findings and lessons Gartner learned from hundreds of data science survey inquiries and explore best practices in deploying, launching, and running machine learning projects, understand how machine learning technologies are different from traditional software engineering approaches, and diiscover key improvements to the data science capabilities of an organization.


The cloud is fundamently changing the way companies think about deploying and using IT resources. What as once rigid and permanent can now be elastic, transient, and available on-demand. Learn how Cloudera's modern data platform is optimized for cloud infrastructure.


Learn how Cloudera Enterprise provides a new kind of analytic database designed to tap into the full value of your data. As an adaptive, high-performance, analytic database, it opens up BI and exploratory analytics over more data—using the skills analysts already rely on—to derive instant value.


In this Economist special report, it will argue that by enabling companies to become more efficient and make far more accurate forecasts, AI will dramatically and fundamentally change the way they work. The report will analyze the effect of different kinds of artificial intelligence (such as computer vision and speech recognition), as well as applications such as human resources, where it will change the way companies recruit, hire and retain staff.


What if you could deliver answers to your employees and customers - quickly, precisely – every time? How would that drive productivity and satisfaction? Artificial intelligence makes this possible, leveraging technologies such as Machine Learning, Natural Language Processing (NLP), and Text Analytics. Download the 5-minute guide now.


The potential for Machine Learning and Deep Learning practitioners to make a breakthrough and drive positive outcomes is unprecedented. But how do you take advantage of the myriad of data and ML tools now available at our fingertips? How to streamline processes, speed up discovery, and scale implementations for real-life scenarios?


For data engineers looking to leverage Apache Spark™'s immense growth to build faster and more reliable data pipelines, Databricks is happy to provide The Data Engineer's Guide to Apache Spark. This eBook features excerpts from the larger Definitive Guide to Apache Spark that will be published later this year.


Apache Spark™’s ability to speed analytic applications by orders of magnitude, its versatility, and ease of use are quickly winning the market. With Spark’s appeal to developers, end users, and integrators to solve complex data problems at scale, it is now the most active open source project with the big data community.


In the latest report, Forrester evaluated 10 top data preparation solutions against 18 criteria, grouped into categories of Current Offering, Strategy and Market Presence. See how each data prep provider, including the Sponsor Trifacta, compares and who received the highest scores.


This demo demonstrates how machine learning empowers database administrators to advance their skills, deliver better customer experience, and solve complex IT problems quickly and easily before customers are impacted.


Businesses around the world are rushing to enable digital transformation initiatives and provide users with consumer-like digital application experiences. Competitive pressures, the impact of disruptive technologies, and constantly increasing user expectations make speed of delivery and quality of service top priorities. A new breed of comprehensive and automated system management solutions is needed to ensure these qualities in complex, high-scale, hybrid IT environments. This IDC Vendor Spotlight examines requirements driving organizational strategies for IT operations analytics (ITOA). The paper considers how Oracle Management Cloud incorporates big data analytics and machine learning technologies with integrated automation to help customers manage IT environments now and in the future.


Oracle Management Cloud (OMC) presents a new approach to systems and security management and provides organizations with a more modern way to manage and secure fast-changing environments. Customers were particularly impressed with OMC’s unified data platform, automation, and machine learning, which dramatically reduced manual effort and ongoing customizations of the management suite.


Today’s enterprises depend more and more on business-critical applications and websites. Understanding how your database affects your application performance is vital to achieving business goals.


When you look back at most outages and customer slowdowns, you often can tie these back to a series of events that started early in the lifecycle of an application.


Today’s enterprises depend more and more on business-critical applications and websites. Understanding how your database affects your application performance is vital to achieving business goals.


Modern developers are faced with the task of operating in an exceedingly competitive business environment.


There are lots of open source database options out there for you. People often wonder how they will know which is the right fit for their needs.


There are lots of open source database options out there for you. You can choose from a relational database, like MySQL or PostgreSQL; or a NoSQL choice like MongoDB. You can run the database on premises, in the cloud, or a hybrid of both.


MySQL is one of the most widely used relational databases, powering nine out of ten websites around the world. With this level of adoption, it must be a good fit for your needs, right? Not so fast.


MongoDB is the most popular NoSQL database option. How do you know when it makes sense to use it over another database?


PostgreSQL, most commonly referred to as Postgres, is the fourth most popular database in the world. It has a primary focus on data integrity and has the ability to be customized through extensions.


Today, guaranteeing the performance of mission-critical applications, websites and services is one of the most important aspects of business success.


Businesses rely on the availability of their applications and underlying data. In fact, 93% of companies responding to a recent survey state rate data as critical to their business success.


Application performance problems can often go overlooked. Despite quick response time, businesses may not realize the impact a SQL statement has on the server.For example, a SQL statement running in an application may have a half-second response time but use 70 percent of all the CPU on a server. This is where DBI Software—which offers performance monitoring, tuning, and trending tools to help businesses improve performance and conserve resources—can make a difference.


SharePoint has become a vital tool for thousands of organizations, and the platform continues to improve with each iteration. Any business serious about increasing efficiency and productivity ought to consider migrating to SharePoint 2016. Of course, completing the upgrade is only part of the journey. Like any tool, SharePoint is best used in the hands of experts who know how to get the most out of it: installing it correctly, fine-tuning its performance, planning how to use it most effectively, and providing help when it breaks down. That’s why more and more companies are choosing to turn to an experienced and knowledgeable SharePoint managed services provider such as Datavail.


With SharePoint Server 2019, Microsoft wanted to offer its customers the fully tricked-out hybrid computing configurations they wanted while enhancing the fundamental functionality of both the on-prem and cloud options within those structures. By doing so, the company is supporting the expanding constellation of scenarios experienced by customers by leveraging their existing IT investments with the enhancements provided by cloud computing solutions.


The data lake was supposed to make getting data, of all sources and sizes, easier for everyone. And, in theory, it does. The problem is that simply implementing a data lake, dumping your data into it, and giving everyone access doesn’t make it a business asset—it makes it a liability. As expectations for your data lake continue to rise across your enterprise, producing business value is imperative.


Leading organizations pursuing digital transformation are turning to big data and cloud deployments to drive agile development and innovation. Data lakes, Internet of Things initiatives, artificial intelligence, machine learning experiments, and self-service analytics programs — to name a few — are all moving into the cloud. Yet even companies that are “all in” on the cloud often choose to retain certain data and related assets on-premises because of privacy or other regulatory requirements. Trust is a core concern in any data initiative, yet governance and assurance of compliance have never been more challenging now that organizations have data and assets spread across the cloud and on-premises data centers.


In this paper, we describe the solutions developed to address key technical challenges encountered while building a distributed database system that can smoothly handle demanding real-time workloads and provide a high level of fault tolerance. Specifically, we describe schemes for the efficient clustering and data partitioning for the automatic scale out of processing across multiple nodes and for optimizing the usage of CPUs, DRAM, SSDs and networks to efficiently scale up performance on one node.


To create a truly friction-free shopping experience, wouldn’t having the ability to deliver the right offer, price or purchase approval in the fastest time possible be ideal? Given low online switching costs, ecommerce providers must offer a great experience to attract and keep customers. These decisions need to happen in milliseconds, supporting diverse applications while processing all the appropriate data available.


There are constant challenges in the payments industry ranging from preventing fraud, verifying identities, and decreasing risk. Other items to grapple with include scaling a solution to deal with rapid growth and meeting strict SLAs while, of course, containing costs. Plus, to remain competitive, new solutions will require development, such as those that deliver value from customer payment data.


The global payments industry is being disrupted by digitalization. A confluence of trends in technology, business, global regulations, and consumer behavior is redefining how payment transactions are executed. The industry is witnessing rapid innovation growth across the value chain, not to mention disintermediation and fragmentation.


Using NoSQL does not necessarily involve scrapping your existing RDBMS and starting from scratch. NoSQL should be thought of as a tool that can be used to solve the new types of challenges associated with big data. Download this white paper to understand the key issues NoSQL can help enterprises solve.


Read the White Paper titled “Five Signs You May Have Outgrown Cassandra (and What to Do About It)” to determine whether your Cassandra infrastructure is hampering your ability to be agile, to compete, and to bring new products and services to market cost-effectively.


Data lakes have emerged as a primary platform on which data architects can harness Big Data and enable analytics for data scientists, analysts and decision makers. Analysis of a wide variety of data is becoming essential in nearly all industries. Read this whitepaper to discover what other data-driven companies are doing to cost-effectively address analytics use cases such as fraud detection, real-time customer offers, market trend/pricing analysis, social media monitoring and more.


Need some magic in your data integration processes? Learn how Attunity is changing the game with innovation and a modern, end-to-end data integration platform. The solution eliminates time-consuming scripting, is easy to use and implement, and has a zero-footprint design that enables real-time, automated data movement for today’s distributed enterprise. Download this fun e-Guide to discover the magic today!


It is no longer sufficient to wait for available data to be batch-extracted, transported or processed. Today’s data-driven organizations have shifted to continuously streaming data into their analytic platforms. This enables them to maximize the potential of that data and, therefore, the value that can be delivered to customers. People throughout the organization – including enterprise and data architects, developers, analysts, data scientists and businesspeople – must have access to the most current data possible to glean the most useful insights and make the most impact. Download this report to learn best practices for automating your data pipeline and enabling real-time data for improved analytics and more agility and efficiency.


For the past decade, most companies have resisted implementing a data warehouse (DW) in the cloud, largely due to concerns about security. In addition, few have had experience using cloud-based applications. But times have changed. Read this report to learn why companies are flocking to the cloud for data warehousing and business intelligence/analytics.


Even if you haven't deployed flash in your infrastructure yet, chances are, in the next year it's likely to happen. To help you get started, we developed a comprehensive, vendor-neutral Flash Storage Buyer's Guide. The purpose of this guide is to provide a basis for evaluating and selecting the best flash storage solution for your business.


Smart storage solutions, such as all-flash systems, provide companies with cloud-like productivity, efficiency, and economics.


The data, gathered from more than 2,300 business leaders across the globe, explores the vast potential – and challenges – businesses must address to unlock data intelligence with AI.According to the new survey from MIT Technology Review Insights: • 82% of business and IT leaders believe AI will have a positive impact • 83% believe AI is important for analytics, greater efficiency and reducing human error • 79% say there are legal and ethical implications of AI that still need to be clarified


Apache Spark is seen by data scientists and analysts as a preferred platform to manage and process vast amounts of data to quickly find insight and knowledge from big data frameworks. However, the programming effort required to build pipelines in Spark often creates a barrier to its successful adoption. View and learn how users of KnowledgeSTUDIO for Apache Spark, a wizard-driven productivity tool for building Spark workflows, have overcome these challenges.


According to Gartner, APIs will be the most common attack vector by 2022. Unfortunately, we’re already seeing the leading edge of that as the sheer volume of business-critical capabilities are provided by under-protected APIs. Therefore, without a deliberate, focused effort on protecting your systems now, that timeline may be optimistic.


Technology has revolutionized how we interact with the world around us. The critical systems we rely on are pervasive in every imaginable format and on every connected device. Unfortunately, our security practices haven’t kept pace with this evolution and every day people and companies fall victim to identity theft, data breaches and irate customers and employees. In this talk, we’ll look at the industry approaches to authentication and authorization, the tradeoffs that come with each strategy, and how we can, and why we must do better. By changing our mindset and building on a reliable foundation of well-established standards, we can protect our systems and users now and into the future.


Whether you call it enterprise content management, refer to it by its ECM acronym, or prefer the newer moniker content services, one thing is clear: Managing information becomes more complex all the time. To succeed, consider the multiple formats in which your data appears, how fast it’s growing, and where it might be spread around within your organization. A plan for managing your content is an essential element in maximizing its value.


Today’s digital economy demands that next-generation cloud applications be built for scale, developer agility, geo-distributed topologies, always-on availability, mixed data types and even catastrophic failure. As organizations look to build, deploy, and scale these next-generation customer-centric applications, MongoDB databases are becoming a defacto standard as a database of choice. However, no database should be rolled into production until a reliable and enterprise grade backup and recovery strategy is in place. While native backup tools like mongodump and OpsManager exist for MongoDB, here are 6 pitfalls to keep in mind as you compare your options.


Due to the distributed nature of non-relational databases, traditional backup and recovery solutions are unable to meet these new data protection requirements, which include: cluster-consistent and online backup, granular recovery, restore to different topology for staging and test/dev and scale-out software only product for high-availability. This overview showcases 3 existing data protection solutions for MongoDB based on their value and deployment costs. Amazon Web Services (AWS) will be used as the deployment environment, but the same arguments hold true for any other on-premises or cloud environment.


Enterprises value their applications and data and are struggling to find next-generation data protection solutions to help them recover from data loss scenarios. This paper compares the 3 existing data protection solutions for Cassandra database (Apache and DataStax versions) based on their value and deployment costs. Amazon Web Services (AWS) will be used as the deployment environment, but the same arguments hold true for any other on-premises or cloud environment.


Getting to a modern data architecture is a long-term journey that involves many moving parts. Most organizations have vintage relational database management systems that perform as required, with regular tweaking and upgrades. However, to meet the needs of a fast-changing business environment, data executives, DBAs, and analysts need to either build upon that, or re-evaluate whether their data architecture is structured to support and grow with their executive leaderships’ ambitions for the digital economy. Download this special report for the key steps to moving to a modern data architecture.


This is a checklist of questions you need to ask before beginning you data integration project. Before embarking on a Data Integration project, you can overcome initial inertia with this easy-to-follow worksheet that breaks down a seemingly overwhelming project into manageable steps.


This whitepaper outlines what Data-as-a-Service is, and how this new approach can solve the problems of analytics on the ever-growing data landscape.


What’s the best way to tackle big data analytics in your organization? In this ebook, we cover the pros and cons of various approaches, and discuss how to effectively pursue a self-service data strategy.


Self-service data means that business users can answer their own questions. It's a more productive approach, but very difficult to achieve. In this whitepaper, learn the the key reasons why progress toward self-service analytics stalls, and how to address them.


SharePoint, Microsoft’s web application framework, is an incredibly powerful tool that can integrate an organization’s content, manage documents, and serve as an intranet or internet website. But it’s difficult to recruit, hire, and train the people needed to operate SharePoint at best-practice levels of support around the clock. In this white paper, we describe seven strategic tasks a managed services provider will undertake to ensure your organization has a superlative SharePoint implementation.


The world of data management has changed drastically – from even just a few years ago. Data lake adoption is on the rise, Spark is moving towards mainstream, and machine learning is starting to catch on at organizations seeking digital transformation across industries. All the while, the use of cloud services continues to grow across use cases and deployment models. Download the sixth edition of the Big Data Sourcebook today to stay on top of the latest technologies and strategies in data management and analytics today.


Deep learning is driving rapid innovations in artificial intelligence and influencing massive disruptions across all markets. However, leveraging the promise of deep learning today is extremely challenging. The explosion of deep learning frameworks is adding complexity and introducing steep learning curves. Scaling out over distributed hardware requires specialization and significant manual work; and even with the combination of time and resources, achieving success requires tedious fiddling and experimenting with parameters.


Gaining the advantage in the years to come means going to the edge. Businesses are discovering their future lies in the ability to leverage strategic edge analytics, now possible through the surge in compute intelligence closer to where data is created, leveraging volumes of data being generated through interactions with cameras, sensors, meters, smartphones, wearables, and more. In conjunction, processor, storage and networking capabilities to support local embedded analytics on these devices and across them through peer-to-peer interactions (on local or nearby mezzanine or gateway platforms) is also increasing.


Understand the value of hybrid cloud management. According to Gartner, by 2021, 75% of enterprise customers using cloud-managed infrastructure as a service (IaaS) and platform as a service (PaaS) solutions will require multi-cloud capabilities, up from 30% in 2018. When choosing a modern enterprise data platform, ensure that it delivers a true uniform managed service across different cloud platforms. Also, understand the key attributes for an optimal hybrid cloud management platform in this exclusive Gartner research.


Most organizations involved in advanced analytics are using big data to feed their AI projects. Many analytics teams are familiar with data in Hadoop and Spark, but are often much less fluent in legacy data sources, such as data from relational databases, enterprise data warehouses and applications running on mainframes and high-res server platforms. Download this white paper to learn why you need to incorporate legacy data in your analytics, AI and ML initiatives and more about the steps you’ll need to take to create a data supply chain for legacy data.


The move by IBM to discontinue support for the Netezza product line has IBM customers facing a hard choice. On one hand, they can undertake a lengthy and complex migration to the IBM DB2 mainframe product — one that is far more complex and costly than Netezza users are accustomed to.


Exclusively through Cloudera OnDemand, Cloudera Security Training introduces you to the tools and techniques that Cloudera's solution architects use to protect the clusters our customers rely on for critical machine learning and analytics workloads. This webinar will give you a sneak peek at our new on-demand security course and show you the immense scope of Cloudera training. From authentication and authorization to encryption, auditing, and everything in between, this course gives you the skills you need to properly secure your Cloudera cluster.


The General Data Protection Regulation (GDPR) went into effect on May 25, 2018, and this has immediate implications for handling data in your big data, machine learning, and analytics environments. Traditional architectural approaches will need to be adjusted to be compliant with several of the provisions. The good news is that Cloudera can help you!


Cloudera Enterprise 6.0 provides a major upgrade to our modern platform for machine learning and analytics with significant advances in productivity and enterprise quality. We have tuned compute resources to maximize performance and minimize total cost of ownership (TCO).


In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications - without onerous administration.


Data warehousing is alive, but perhaps not alive and well. Legacy data warehouses must modernize to fit gracefully into modern analytics ecosystems. They play an important role in data management as an archive of enterprise history and a source of carefully curated and highly integrated data for a broad scope of line-of-business information needs. To continue filling that role well, they must evolve both architecturally and technologically. Yet in many instances, data warehouse evolution is stalled due to uncertainty about what, how, and when to change. This report provides guidance to break the logjam and begin moving to data warehouses that are agile, scalable, and adaptable in the face of continuous change. It describes how patterns of architectural restructuring, cloud migration, virtualization, and more can be used to combine data warehouses with big data, cloud, NoSQL and other recent technologies to resolve many of today’s data warehousing challenges and to prepare for the futur


Many of the world's largest companies rely on Cloudera's multi-function, multi-environment platform to provide the foundation for their critical business value drivers—growing their business, connecting products and services, and protecting their business. Find out what makes Cloudera Enterprise different from other data platforms.


In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications - without onerous administration.


Accelerate insights from analytics with managed cloud services. Enterprises require fast, cost-efficient solutions to the familiar challenges of engaging customers, reducing risk, and improving operational excellence to stay competitive. The cloud is playing a key role in accelerating time to benefit from new insights. Managed cloud services that automate provisioning, operation, and patching will be critical for enterprises to leverage the full promise of the cloud when it comes to time to value and agility.


Nik Rouda’s whitepaper on the many reasons why your choice of data platform matters. Furthermore, Nik discusses how it will serve as the foundation for all your analytics application innovation. Your choice will ultimately determine the success of all your business and operational goals related to insights. The right choice will enable you to ask bigger questions. Cloudera’s definition of data platform is an integrated set of capabilities and functions that drive analytics and data management. This should be oriented around how diverse groups use data to gain insights. A modern approach combines different analytics disciplines into one unified and flexible platform. This approach makes it easier for BI analysts, data scientists, data engineers, admins, and other knowledge workers to get new insights and innovate in their business.


This ESG Lab Review documents the recent analysis of Cloudera Navigator Optimizer with a goal of validating its ability to enable organizations to identify and offload SQL workloads from legacy data marts and costly enterprise data warehouses (EDWs) to a modern analytic database built on Hadoop, while optimizing existing workloads already on Hadoop.


This Checklist Report drills into some of the emerging data platforms that modern data-driven organizations are embracing. The goal is to accelerate users’ understanding of new data platforms so they can choose/use the ones that best support the new data-driven goals of their organizations.


Learn EMA's best practices for driving toward analytic initiatives in the cloud and the key to cloud success.


Insurers have long struggled with data silos. Getting the right information at the right time is a challenge. Cloudera provides a new paradigm for breaking data silos. For the first time, insurers can blend and analyze data from any source, in any amount and for all types of workloads. The Insurance industry is undergoing a digital transformation, in which big data, machine learning and IoT are playing a central role.


Deep learning is a tool that enterprises use to solve practical problems. In this paper, we provide an introduction to deep learning and its foundations, introduce you to Cloudera’s unified platform for data and machine learning and show you four ways to implement deep learning and offer six practical tips to help your organization move forward with deep learning.


As an early entrant to the telematics service provider (TSP) market, Octo Telematics has established a global market-leading position over the last decade. To further drive the IoT insurance market and build on its market position, Octo Telematics needed to develop an IoT and telematics platform with the functionality, flexibility, and scale to support the next evolution of IoT-based insurance propositions. Download the report to learn how Octo Telematics implemented a next-generation IoT and telematics platform.


As companies scramble to protect digital assets from a new generation of threat actors, big data and machine learning are yielding critical threat intelligence. A new IDG Research survey finds greater visibility is essential.


Harvard Business Review's research and client engagement experience has shown that companies with the greatest value from IoT to date are the best at dealing with how products are performing for customers. In this article, learn four key elements of using IoT to get the ultimate truth on product performance.


IoT projects are far beyond the pilot stage and have spurred IT leaders to implement hybrid strategies, processing some IoT data at the edge of the enterprise, while sending much of it to a central hub for deep analytics, according to a recent IDG Quick Pulse survey. As competition heats up, the companies that can find the right balance between edge and hub are likely to fare best. Download the results of the IDG survey.


Data is reshaping the face of infrastructures, businesses, politics, and economics. Like oil, data is extracted, refined, valued, bought, and sold. With it, comes new rules and regulations, and perhaps more. Will battles be fought over data? This engaging Economist article takes an in-depth look.


This report will provide guidelines for evaluating and improving data science practices within large organizations. It will also provide details about necessary product capabilities that customers can use to make a well-informed product selections. Learn more about: The barriers preventing companies from realizing the full potential of data science What operationalizing data science means and why it is critical for success Eight best practices and product features for operationalizing data science across the enterprise How to build organizational support and multi-disciplinary teams for implementation How to construct a business plan to optimize the return on investment from data science.


How Cloudera and Navistar deliver IoT-enabled predictive maintenance, vehicle diagnostics and management, and route optimization to help fleet and truck owners minimize vehicle downtime.


The adoption of new databases, both relational and NoSQL, as well as the migration of databases to the cloud, will continue to spread as organizations identify use cases that deliver lower costs, improved flexibility and increased speed and scalability. As can be expected, as database environments change, so do the roles of database professionals, including tools and techniques. Download this special report today for the latest best practices and solutions in database performance.


Data today is truly dynamic. More than one billion people are active on social networks, and the number of collected devices is expected to be 50 billion by 2020. The data generated by those devices is staggering, and it leaves companies grappling for the best choice in a sea of technological innovation.


Read this document to learn how businesses can extract data directly from SAP ERP, CRM, and SCM systems and analyze data in real-time, enabling business managers to quickly make data-driven decisions.


Pure Storage introduced a converged infrastructure platform known as FlashStack that is built upon trusted hardware from Cisco and Pure Storage. This guide delves into a reference architecture for deploying a VMware Horizon View 6.2 VDI environment on a FlashStack Converged Infrastructure.


Learn how St Luke’s achieved a 234% ROI on a VDI deployment with all-flash storage from Pure Storage.


This ESG Lab Validation report documents hands-on testing and validation of the Pure Storage FlashArray//m storage system. The goal of the report is to prove that Tier-1 application workloads, such as desktop virtualization, databases, and email can be run on a shared, consolidated storage array without compromising service levels.


VDI has changed dramatically in the capabilities it offers to end-users and IT and how it’s benefits can be maximized with costs that are kept in check. When designing a VDI ecosystem, storage is a key consideration that administrators must closely examine to get the greatest success out of their deployment.


The promised benefits of virtual desktop infrastructure (VDI) – including simplified management, enhanced security and reduced costs – are very attractive to both IT managers and senior executives. But these benefits are not guaranteed, nor are they necessarily achieved overnight. Some organizations find that their first attempt at virtualization causes as many problems as it solves, particularly when it comes to disappointing end-user performance, unexpected management complexity, and high costs. Or, VDI may function well at the start, but fails to scale larger over time.


Data isn't just about storage and retrieval anymore. Today, it’s all about how you use the data you’re storing and if you’re storing the right data. The right mix of data and the ability to analyze it against all data types is driving global markets towards digital transformation. Read this eBook to learn the top 10 reasons why all-flash storage can help your organization maximize data value for your Oracle database and analytics deployments.


All-flash storage arrays have become a breakthrough technology for Oracle databases, enabling levels of performance that are unattainable with spinning disk drives.


Data (and database management systems, like Oracle) have never been more critical for competitive advantage. All modern IT initiatives – cloud, real time analytics, Internet of Things – intrinsically need to leverage more data, more quickly. All-flash storage is a next generation infrastructure technology that has the potential to unlock a new level of employee productivity and accelerate your business by reducing the amount of time spent waiting for database and applications.


Oracle DBA’s can spend massive amounts of time trying to pinpoint the cause of DB performance issues. The more complicated the infrastructure stack, the more difficult it is to figure out the root cause of issues. Luckily, Oracle has in-built mechanisms to help DBA’s quickly identify performance bottlenecks – the Automated Workload Repository (AWR). This short guide provides a crash course into how to quickly analyze AWR reports to identify performance issues, as well as possible infrastructure solutions that can help ensure that those problems are eliminated for good.


Performance Gains, Surprising Survival of an Array-Killing Scenario & Post-Migration DBA Life. Tech pros seek insights and share unvarnished opinions in independent forums all over the web. That’s where this Real Stories project & research started. This report is drawn entirely from Pure Storage Real Users’ words, observations and experiences. All Stories are used with permission.


In the current age of digital transformation, SAP HANA has become the gold standard for businesses seeking the benefits of real-time analytics. The platform offers a level of service and innovation that drives revenue and provides valuable insight, but for many companies, the switch to SAP HANA is simply too costly. Pure Storage FlashStack can help.


Data today is truly dynamic. More than one billion people are active on social networks, and the number of collected devices is expected to be 50 billion by 2020. The data generated by those devices is staggering, and it leaves companies grappling for the best choice in a sea of technological innovation. Read this White Paper to learn more about the Pure Storage SAP solution for Big Data, and what it means for your organization, including how to: Manage and process large volumes of data within the HANA framework with data tiering on Pure Storage Achieve scalability and reduced TCO without compromising security and user-friendly data consumption Use AI to enable SAP customers to take advantage of new data types.


Many customers attempt to reduce the resources and cost required to “keep the lights on” for their existing SAP landscapes. Providing more business value and increase innovation is the ultimate goal. Making Real-Time Business the Reality is the first step into this direction. This white paper will provide guidance and solutions focusing on technical and business value.


The top 10 reasons why you want Smart Storage Solutions to modernize your traditional SAP and SAP HANA environments.


IDC interviewed seven organizations about their experiences using Pure Storage FlashArrays to support enterprise applications from SAP SE (hereafter referred to as SAP). Organizations tell IDC that after transitioning these workloads to Pure Storage FlashArrays, they realized significantly improved storage performance and increased productivity on the part of both IT personnel and developers and are enjoying significantly lower costs. IDC calculated that these participating organizations will achieve an average annual net benefit of $4.06 million per organization ($167,881 per 100 Pure Storage users), which would lead to a three-year return on investment (ROI) of 472%.


How can you modernize and deliver on-demand services while keeping your existing SAP landscape optimized and your risks minimized? Read this document to learn the six incremental steps to SAP HANA implementation.


Virtual Desktop Infrastructure (VDI) is high on the mind of nearly every organization today. Companies are transforming their business with secure, highly responsive end-points to more of their users around the world.


Abstract: With real-time streaming analytics there is no room for staging or disk. Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Kafka™, Spark™ and other technologies. Join GridGain System’s Director of Product Management and Apache Ignite PMC Chair Denis Magda for this 1-hour webinar as he explains how to: • Optimize stream ingestion from Kafka and other popular messaging and streaming technologies • Architect pre-processing and analytics for performance and scalability • Implement and tune Apache Ignite or GridGain and Spark together • Design to ensure performance for real-time reports


Once you've put in-memory computing in place to add speed and scale to your existing applications, the next step is to innovate and improve the customer experience. Join us for part 2 of the in-memory computing best practices series. Learn how companies build new HTAP applications with in-memory computing that leverage analytics within transactions to improve business outcomes. This is how many retail innovators or SaaS innovators have succeeded. This webinar will explain with examples on how to: • Merge operational data and analytics together, so that analytics can work against the most recent data • Improve processing and analytics scalability with massively parallel processing (MPP) • Increase transaction throughput using a combination of distributed SQL, ACID transaction support and native persistence • Synchronize data and transactions with existing systems


It's hard to improve the customer experience when your existing applications can't handle the ever-increasing customer loads, are inflexible to change and don't support the real-time analytics or machine learning needed to improve the experience at each step of the way. Join us for part 1 of the in-memory computing best practices series. Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience. This webinar will explain with examples: • How to start with Apache Ignite as an In-Memory Data Grid (IMDG) deployed on top of RDBMS or NoSQL database • How to keep data in sync across RAM (Apache Ignite) and disk (RDBMS/NoSQL database) • How to leverage from Apache Ignite distributed SQL and ACID transaction for IMDG scenarios • How to move further and start to build HTAP applications, real-time analytics, and


Learn some of the best practices companies have used to increase performance of existing or new SQL-based applications up to 1,000x, scale to millions of transactions per second and handle petabytes of data by adding Apache® Ignite™ or GridGain®. Apache Ignite is (an in-memory computing platform OR an in-memory distributed data store and compute grid) with full-fledged SQL, key-value and processing APIs. GridGain, built on Apache Ignite, is the commercial version of the in-memory computing platform. Many companies have added Apache Ignite or GridGain as a cache in-between existing SQL databases and their applications to speed up response times and scale. In other projects, they've used the solution as its own SQL database. This session will dive into some of the best practices for both types of projects using Apache Ignite. Topics covered include: • Adding Apache Ignite in between existing databases and apps without any changes to the apps • How auto-loading of SQL schema, data p


Apache Ignite native persistence is a distributed ACID and SQL-compliant store that turns Apache Ignite into a full-fledged distributed SQL database. It allows you to have 0-100% of your data in RAM with guaranteed durability using a broad range of storage technologies, have immediate availability on restart, and achieve high volume read and write scalability with low latency using SQL and ACID transactions. GridGain, built on Apache Ignite, is the commercial version of the in-memory computing platform. Learn how to get native persistence up and running, and tips and tricks to get the best performance. In this webinar, Valentin Kulichenko, GridGain’s Lead Architect, will explain: • What native persistence is, and how it works • Show step-by-step how to set up Apache Ignite with native persistence • The best practices for configuration and tuning


In this webinar, Denis Magda, GridGain Director of Product Management and Apache Ignite PMC Chairman, will introduce the fundamental capabilities and components of a distributed, in-memory computing platform. With increasingly advanced coding examples, you’ll learn about: • Collocated processing • Collocated processing for distributed computations • Collocated processing for SQL (distributed joins and more) • Distributed persistence usage


In this webinar, Denis Magda, GridGain Director of Product Management and Apache Ignite PMC Chairman, will introduce the fundamental capabilities and components of an in-memory computing platform, and demonstrate how to apply the theory in practice. With increasingly advanced coding examples, you’ll learn about: • Cluster configuration and deployment • Data processing with key-value APIs • Data processing with SQL


To realize the benefits of IoT, you need to choose the right architecture and set of technologies that can process large data streams, identify important events and react in real-time. Many companies who have succeeded with IoT have solved their challenges around speed, scalability and real-time analytics with in-memory computing. Across these deployments some common architectural patterns have emerged. This whitepaper explains some of the most common use cases and challenges; the common technology components, including in-memory computing technologies; and how they fit into an IoT architecture. It also explains how Apache® Ignite™ and GridGain® are used for IoT.


Many companies have succeeded with their digital transformations by taking an evolutionary approach, rather than ripping out and replacing their existing applications and databases. This white paper will tell you how. It provides an overview of in-memory computing technology with a focus on in-memory data grids (IMDG). It discusses the advantages and uses of an IMDG and its role in digital transformation and improving the customer experience. It also introduces the GridGain in-memory computing platform, and explains GridGain’s IMDG and other capabilities that have helped companies add speed and scalability to their existing applications.


Apache Ignite is an open source in-memory computing platform that provides an in-memory data grid (IMDG), in-memory database (IMDB) and support for streaming analytics, machine and deep learning. This paper covers in detail Apache Ignite architecture, integrations, and key capabilities. You will also learn about GridGain, the leading in-memory computing platform for real-time business, and the only enterprise-grade commercially supported version of Apache Ignite.


Distributed-caching products such as Redis can help with the individual performance bottlenecks of some applications and their databases if companies are willing to code and change applications. But Redis cannot help with new initiatives where real-time analytics, continuous machine and deep learning are needed to make recommendations or automate decisions. And, end users expect personalized, real-time responsiveness in their interactions with companies. This white paper explores the challenges faced by companies that have either used Redis and run into its limitations, or are considering Redis and find it is insufficient for their needs. This paper will also discuss how the GridGain in-memory computing platform has helped companies overcome the limitations of Redis for existing and new applications, and how the GridGain® in-memory computing platform has helped improve the customer experience.


New business demands – from digital transformation to improving the customer experience – are overwhelming existing SQL infrastructure. Increased interactions through new Web and mobile apps and their underlying APIs are creating massive volumes of queries and transactions that are overloading existing databases. Improving the customer experience requires performing real-time analytics and automation during transactions and interactions, not after. Traditional data warehouses and other related tools cannot address these needs. And they don’t support the new analytical approaches, from stream processing to artificial intelligence, needed for these new initiatives. The good news is that several companies have successfully implemented these new approaches to real-time analytics with the Apache Ignite and GridGain in-memory computing platforms. Learn more now.


Apache Ignite™ and GridGain® provide the most extensive in-memory data management and acceleration for Spark. Ignite is an open source in-memory computing platform that provides an in-memory data grid (IMDG), in-memory database (IMDB) and support for streaming analytics, machine and deep learning. GridGain® is the leading in-memory computing platform for real-time business and the only enterprise-grade, commercially supported version of Ignite. GridGain and Ignite provide the ideal underlying in-memory data management technology for Apache Spark because of its in-memory support for both stored “data at rest” and streaming “data in motion.” Learn how this makes many Spark tasks simple, including stream ingestion, data preparation and storage, stream processing, state management, streaming analytics, and machine and deep learning.


If you're looking to bring greater awareness to data risk management practices within your organization and among your C-suite, don't miss this podcast, moderated by Paula Musich, of Enterprise Management Associates, and featuring Dan Goodes and Nev Zunic, both of IBM Security.


While security leaders have improved communication with business stakeholders, a gap still exists between what is said and what the business hears. The lack of communication skills continues to cost security and risk management leaders a voice in strategic planning and a seat at the executive table.


Navigating the threat landscape in 2018 is complicated, not only by the ever-changing tactics of attackers, but also by the looming enactment of the European Union’s General Data Protection Regulation. As security practitioners attempt to steer clear of such complications, they will have to find ways to interact effectively with executives and boards of directors who are increasingly taking a more proactive role in understanding the risks associated with their organizations’ digital assets. The effort to better manage those data risks requires greater coordination across organizational boundaries, an examination of what constitutes the company’s crown jewels, where they exist, and how they are handled across the organization. With those insights, security practitioners can more effectively prioritize their protections (and budgets) instead of trying to boil the ocean and protect everything.


Data security presents a complex challenge to organizations. The value of sensitive data, and particularly customer data, has increased exponentially over time, but with it comes an increase in potential liability and exposure. Successful enterprise security and compliance strategy needs to balance out: the rapid growth of data within organizations’ environments; the complexity of regulations and compliance across industries; and the threat of internal and external attacks. Additionally, companies struggle to understand how to proactively monitor and control user access privileges, and they often lack the visibility into what data is at risk, which can lead to potentially devastating security threats. Companies seek to safeguard their structured and unstructured data and support compliance across a variety of environments: on-premises, off-site, in a private, public, or hybrid cloud, on the mainframe, or in a big data environment.


This helps security and privacy professionals understand five core GDPR requirements and two related changes they need to start tackling now.


Data has always been a critical resource for organizations. Today, however, data is the true lifeblood for the enterprise, and has earned its position on the list of crucial assets upon which organizations depend. In fact, it’s not uncommon to hear someone use the term currency with respect to their data, demonstrating that data rivals finances in importance. As a result, entire industries have arisen around protecting and managing that data. At the same time, new types of data-centric workloads have emerged. As the use of data continues its expansion in both volume and velocity, new applications have been developed to deal with the proliferation. They include Hadoop, MongoDB, Couchbase, and Hortonworks, but there are a great many more as well. These types of tools enable organizations to store, manage, and analyze vast quantities of data, searching for insight that can propel the business forward.


Mozenda helps synthetic lubricant pioneer AMSOIL Inc. compete against much larger brands such as Mobil, Pennzoil, Shell, Castrol and Valvoline. From ecommerce operations and product merchandising, to retail planning, ready access to unstructured web data helps Amsoil regularly solve a variety of strategic and tactical challenges.


View the highlights of IDC research exploring the benefits that Red Hat Enterprise Linux and Microsoft SQL Server offer during platform consolidation.


Applications are driven by data—and their ability to scale and adapt depends on the database management system (DBMS) that drives them. That underlying DBMS must be able to process transactions quickly and reliably and, for large analytic tasks, ingest huge and diverse data sets with low latency. Microsoft SQL Server is one such DBMS. In “Data Management Platform Consolidation with Enterprise Linux,” IDC explains why running SQL Server on Red Hat® Enterprise Linux® provides workload-optimized performance, streamlined consistency across modern IT environments, and newfound agility with container deployments. Using real-world success stories as examples, this in-depth IDC whitepaper reveals how SQL Server and Red Hat Enterprise Linux is a high-performance combination for consolidating current-and new-generation applications and their respective data management stacks.


In an increasingly competitive digital economy, businesses depend on applications more than ever. Modern business and consumer applications operating across native, web, and mobile platforms rely on fast access to data. To meet business requirements for reliability and availability, databases that support these applications must deliver high performance and increased stability on a security-focused foundation. Together, Red Hat and Microsoft deliver a highly available and reliable foundation for database operations that meets modern digital business needs. Learn how Microsoft SQL Server 2017 on Red Hat®Enterprise Linux® delivers data reliability and availability for critical workloads.


Organizations are increasingly using data to run applications, inform processes, and gain insight. To support these initiatives, applications and users need fast, reliable, secure access to the right data at all times. Together, Red Hat® Enterprise Linux® and Microsoft SQL Server 2017 provide the flexibility, performance, and security needed for modern database operations.


A lot has happened since the term “big data” swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop and NoSQL are now household names, Spark is moving towards the mainstream, machine learning is gaining traction and the use of cloud services is exploding everywhere. However, plenty of challenges remain for organizations embarking upon digital transformation, from the demand for real-time data and analysis, to need for smarter data governance and security approaches. Download this new report today for the latest technologies and strategies to become an insights-driven enterprise.


The goal of streaming systems is to process big data volumes and provide useful insights into the data prior to saving it to long-term storage. The traditional approach to processing data at scale is batching; the premise of which is that all the data is available in the system of record before the processing starts. In the case of failures the whole job can be simply restarted. While quite simple and robust, the batching approach clearly introduces a large latency between gathering the data and being ready to act upon it. The goal of stream processing is to overcome this latency. It processes the live, raw data immediately as it arrives and meets the challenges of incremental processing, scalability and fault tolerance.


In this white paper we examine how to build a stream processing application using a sample “live betting” application called JetLeopard. The JetLeopard sample application is built using Hazelcast Jet.


Get complimentary access to this year’s Gartner Magic Quadrant for Metadata Management Solutions.


On May 25 you either celebrated success or covered your eyes while time ran out. Either way, we're all living in a post-GDPR world. Let’s be prepared.


It may feel like it’s time for you to break up with data governance. But don’t worry, we’ll help you find romance once again.


Description: When it comes to choosing a graph database, speed is one of the most important factors to consider. How fast can you query? How quickly will data load? How fast are graph traversal response times? This benchmark study examines the data loading and query performance of TigerGraph and Amazon Neptune.


Early generation graph technologies have been unable to support real-time analytics on data that continues to increase in both volume and complexity. This white paper details how Native Parallel Graph (NPG) differs from early generation Native Graph Technologies, and introduces TigerGraph - the world’s first and only NPG system. TigerGraph is a complete, distributed graph analytics platform that supports web-scale data analytics in real-time, delivering incredible loading speed, fast query execution and real-time update capabilities.


Deep-link analytics has been out of reach until now. This white paper discusses how how and why graph analytics has evolved and the benefits of being able to perform subsecond queries of big data and stream over 2B daily events in real-time to a graph with 100B+ vertices and 600B+ edges on a cluster.


In recent years, the demand for faster, more efficient data access and analytics at end-users’ fingertips has fueled the emergence of enterprise data lakes (EDLs) in biopharma. EDLs are repositories designed to hold vast amounts of raw data - unstructured and structured - in native formats until needed for business. But implementing an EDL is just a start. How can biopharma companies unlock its full value to support R&D, Manufacturing, and other functions?


How the investment in data wrangling pays for itself. As companies look to adopt a data preparation platform, establishing a framework for measuring ROI is key to understanding its impact. In this guide, we share five stories from Trifacta customers across a range of industries that each took a different approach to measuring ROI and finding value in a data preparation platform.


A new generation of applications is emerging. The speed and enormous scale these apps require has exposed challenging gaps in legacy database technologies. Aerospike has pioneered a modernized architecture that’s simple, cost-effective and that delivers speed at scale for mission-critical applications.


One-Minute Whitepaper 99% of IT leaders rate CX as a top priority or very important initiative. IT leaders in every industry are watching and responding to the rise of CIAM. They know that this renewed emphasis on the consumer is reshaping their organizations and their priorities. Understand recent survey insights on the importance of digital transformation and the role of IT.


A Guide to Building and Securing APIs It is incumbent on today’s API builders to be smart, informed and proactive. In this new guide to building and securing APIs, explore the role of API Gateways and learn best practices for protecting data in transit, managing API credentials, and handling authentication and authorization.


Public cloud adoption is the no. 1 priority for technology decision makers investing in big data. However, most enterprise architects are not ready for the disruption that this shift will cause. You have no time to waste. read this report to understand the speed at which the public cloud will unseat on-premises big data technology and solutions. this report will also help accelerate your plans, explain your options, and provide a four- step method for evolving your big data road map.


Explore the synergy between AWS and Altus to help you securely standardize on a combination of public cloud and a data management platform-as-a-service. Avail up to $2000 in AWS cloud credits and a 90 day free trial of Altus. *Must work with AWS and Cloudera account managers on POC to be eligible for offer.


Read this Forrester report to gain a better understanding of the revolution that is Deep learning.


Digital transformation amplifies the importance of your IT infrastructure and software delivery mechanisms. This foundational shift to embrace technologies is the key business challenge facing most CIOs. Read this white paper to learn why digital transformation cannot succeed without a system of intelligence.


To drive productivity and innovation across their IT operations, organizations turn to business analytics. Successful businesses require a system of intelligence for IT that includes pre-built dashboards, IT business models, and robust integrations. This eBook delves into top use cases on how a unified system of intelligence supports digital transformation.


Organizations need to drive productivity and innovation across their IT operations, but it’s an impossible task using reporting-only applications. Analytics applications, however, provide the means for in-depth investigations. Read this eBook to learn five key differences between reporting and analytics applications, and the roles each play in enhancing IT service.


Building cognitive applications that can perform specific, humanlike tasks in an intelligent way is far from easy. From complex connections to multiple data sources and types, to processing power and storage networks that can cost-effectively support the high-speed exploration of huge volumes of data, and the incorporation of various analytics and machine learning techniques to deliver insights that can be acted upon, there are many challenges. Download this special report for the latest in enabling technologies and best practices when it comes to cognitive computing, machine learning, AI and IoT.


A new generation of technologies is emerging to help enterprises realize their data integration and governance goals, from advances in real-time data processing and cloud computing, to novel self-service capabilities in commercial tools and platforms. Download this report to understand the latest developments and strategies for success today.


Digital transformation (DX) initiatives enable the rapid creation of externally facing digital products, services, and experiences while aggressively modernizing the internal IT environment. A true digitally transformed company will have aggressively modernized its legacy (or core IT) environments to redefine processes and capabilities for both internal and external purposes, making the company able to act on its innovative impulses and to respond to changing business needs in near real time.


Are you thinking about moving your Oracle databases to the cloud or making the transition to Database as a Service (DBaaS)? With cloud computing vendors offering more services at lower prices, the barriers to spinning up cloud resources are diminishing. But there are few black-and-white questions in technology and even fewer in business, which is why smart companies look at all the shades of grey in an innovation like the cloud-based database before they commit on a large scale.


This study, sponsored by Quest Software, includes the views and experiences of 285 IT decision makers, representing a fairly broad sample of company types and sizes. The survey found that databases continue to expand in size and complexity, while at the same time, more enterprises are turning to cloud-based resources to keep information highly available.


As your organization’s data becomes more and more critical, you need a way to ensure it’s never compromised by unscheduled downtime – due to a system crash or malfunction – or scheduled downtime – due to patches or upgrades to Oracle, the operating system, or applications, and storage replacement.


Migrating data from one platform to another requires a lot of planning. Some traditional migration methods are easy to use, but they only work for migrations on the same platform. Quest® SharePlex® can replicate data across platforms, from Oracle to SQL Server, with next to no downtime, offering a flexible, low-cost alternative.


The process of migrating and upgrading hardware, operating systems, databases and software applications has become inextricably linked to risk, downtime and weekends at the office for most DBAs and system administrators who perform them. Want to simplify the migration and upgrade process so you can avoid the risk, downtime and long hours normally associated with it? Read this e-book!


If you use Oracle technologies, you may be relying on Oracle Enterprise Manager (OEM) to manage your clouds, applications and databases. But if you’re responsible for multiple environments with hundreds of databases as well as Oracle Real Application Clusters (RACs) and Oracle Exadata, then OEM alone will not enable you to do your job effectively.


As the largest dental benefits system in the U.S., Dentegra depends heavily on software applications to support the orchestration of core business processes such as contracts management, customer onboarding, and claims processing. Moving to the cloud is part of Dentegra’s long-term digital strategy to improve scalability and time to market across its application portfolio. Download the case study to learn more.


Learn how Dentegra uses Delphix on AWS to increase data agility and protection.


Need a proven blueprint to fast-track application development in your healthcare organization? With triple-digit growth, 3,000+ databases and over a petabyte of data, Molina Healthcare needed a way to accelerate application development and drive digital transformation. Success meant slashing time to provision new dev and test environments in half, putting self-service data access in the hands of application teams and doing it all without taking an eye off data security and HIPAA compliance.


Metro Bank, the UK’s first new high street bank in more than 100 years, is committed to providing customers with unparalleled levels of service and convenience. Metro Bank’s focus on state-of-the-art IT has helped the company provide industry-first innovations, such as 20-minute paperless account opening and temporary card freezing.


Read the new Gartner 2018 Magic Quadrant for Data Integration Tools Report to better understand which featured data integration technologies we believe could best help you achieve your data goals and help you overcome your data integration and management challenges. You will also learn about emerging trends and technology to prepare for, and each of the 15 vendors’ competitive differentiators, based both on their ability to execute and their completeness of vision. Download this report now and start benefiting from real-time analytics and fast-moving business opportunities sooner!


In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used.


This whitepaper presents the architecture of Griddable.io’s smart grids for synchronized data integration. Smart transaction grids are aimed at building and operating highly available and scalable infrastructure for synchronized data integration between various data sources and destinations on private and public clouds.


Containers and microservices are the environments of choice for most of today’s new applications. However, there are challenges. Bringing today’s enterprise data environments into the container-microservices-Kubernetes orbit, with its stateless architecture and persistent storage, requires new tools and expertise. Download this report for the most important steps to getting the most out of containerization within big data environments.


À ses débuts, Metro Bank était la première grande banque à avoir vu le jour au Royaume-Uni au cours du siècle passé. Depuis sa création, elle s’efforce d’offrir à ses clients des niveaux de service et de commodité sans précédent. Misant sur des systèmes informatiques de pointe, la société propose des innovations à l’avant-garde du secteur, tels qu’une ouverture de compte totalement électronique en 20 minutes ou encore un blocage de carte temporaire.


“Boeing Employees’ Credit Union” (BECU) ist die größte Kreditgenossenschaft in Washington und die viertgrößte in den Vereinigten Staaten mit mehr als 17$ Milliarden in Vermögen und über eine Million Mitglieder. BECU ist eine digital fokussierte Institution mit über 40 Bargeldlosen Filialen und nur wenigen traditionellen Filialen.


The world of data management in 2018 is diverse, complex and challenging. The industry is changing, the way that we work is changing, and the underlying technologies that we rely upon are changing. From systems of record, to systems of engagement, the desire to compete on analytics is leading more and more enterprises to invest in expanding their capabilities to collect, store and act upon data. At the same time, the challenge of maintaining the performance and availability of these systems is also growing. Download this special report to understand the impact of cloud and big data trends, emerging best practices, and the latest technologies paving the road ahead in the world of databases.


From automated fraud detection to intelligent chatbots, the use of knowledge graphs is on the rise as enterprises hunt for more effective ways to connect the dots between the data world and the business world. Download this special report to learn why knowledge graphs are becoming a foundational technology for empowering real-time insights, machine learning and the new generation of AI solutions.


Fast Data Solutions are essential to today’s businesses. From the ongoing need to respond to events in real time, to managing data from the Internet of Things and deploying machine learning and artificial intelligence capabilities, speed is the common factor that determines success or failure in meeting the opportunities and challenges of digital transformation. Download this special report to learn about the new generation of fast data technologies, emerging best practices, key use cases and real-world success stories.


Cognitive computing is such a tantalizing technology. It holds the promise of revolutionizing many aspects of both our professional and personal lives. From predicting movies we'd like to watch to delivering excellent customer service, cognitive computing combines artificial intelligence, machine learning, text analytics, and natural language processing to boost relevance and productivity.


GDPR is coming, and with it, a host of requirements that place additional demands on companies that collect customer data. Right now, organizations across the globe are scrambling to examine polices and processes, identify issues, and make the necessary adjustments to ensure compliance by May 25th. However, this looming deadline is just the beginning. GDPR will require an ongoing effort to change how data is collected, stored, and governed to ensure companies stay in compliance. Get your copy of the GDPR Playbook to learn about winning strategies and enabling technologies.


Today, more than ever, data analysis is viewed as the next frontier for innovation, competition and productivity. From data discovery and visualization, to data science and machine learning, the world of analytics has changed drastically from even a few years ago. The demand for real-time and self-service capabilities has skyrocketed, especially alongside the adoption of cloud and IoT applications that require serious speed, scalability and flexibility. At the same time, to deliver business value, analytics must deliver information that people can trust to act on, so balancing governance and security with agility has become a critical task at enterprises. Download this report to learn about the latest technology developments and best practices for succeeding with analytics today.


Data lake adoption is on the rise at enterprises supporting data discovery, data science and real-time operational analytics initiatives. Download this special report to learn about the current challenges and opportunities, latest technology developments, and emerging best practices. You’ll get the full scoop, from data integration, governance and security approaches, to the importance of native BI, data architecture and semantics. Get your copy today!


Now that Oracle has formally announced the deprecation of Oracle Streams, Oracle Database Advanced Replication, and Change Data Capture in Oracle Database 12c, what’s the best alternative? Read this technical brief to find out why SharePlex is the best and most comprehensive solution for all your future data-sharing needs.


As data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes. Answering this call is a new generation of hybrid databases, data architectures and infrastructure strategies. Download today to learn about the latest technologies and strategies to succeed.


Dentegra Gruppe, Inc. (Dentegra), mit Sitz in San Francisco, ist eine Holdinggesellschaft für eine Gruppe von Unternehmen unter gemeinsamer Leitung, die zusammen das größte zahnärztliche Leistungssystem in den USA bilden.


Die Metro Bank, die erste neue Bank in Großbritannien seit mehr als 100 Jahren, hat es sich zur Aufgabe gemacht, ihren Kunden einen beispiellosen Service und Komfort zu bieten. Die Fokussierung der Metro Bank auf modernste IT hat dem Unternehmen dabei geholfen, branchenweit erste Innovationen wie die papierlose Kontoeröffnung in 20 Minuten und das temporäre Einfrieren von Karten anzubieten.


The adoption of new database types, in-memory architectures and flash storage, as well as migration to the cloud, will continue to spread as organizations look for ways to lower costs and increase their agility. Download this brand new report for the latest developments in database and cloud technology and best practices for database performance today.


The world of data management in 2017 is diverse, complex and challenging. The industry is changing, the way that we work is changing, and the underlying technologies that we rely upon are changing. From systems of record, to systems of engagement, the desire to compete on analytics is leading more and more enterprises to invest in expanding their capabilities to collect, store and act upon data. At the same time, the challenge of maintaining the performance and availability of these systems is also growing. As a result, two trends will continue to dominate data management discussions this year. The adoption of new “big data” technologies and the movement to the cloud. Download this report to learn about the key developments and emerging best practices for tackling the challenges and opportunities in databases today.


Read this document to learn how businesses can extract data directly from SAP ERP, CRM, and SCM systems and analyze data in real-time, enabling business managers to quickly make data-driven decisions.


Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility, and the ability to innovate through better collaboration, visibility, and performance. However, as data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes. Download this special report to gain a deeper understanding of the key technologies and strategies.


The Internet of Things represents not only tremendous volumes of data, but new data sources and types, as well as new applications and use cases. To harness its value, businesses need efficient ways to store, process, and ana¬lyze that data, delivering it where and when it is needed to inform decision-making and business automation. Download this special report to understand the current state of the marketplace and the key data management technologies and practices paving the way.


Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level where data is captured, stored, and processed. This transformation is being driven by the need for more agile data management practices in the face of increasing volumes and varieties of data and the growing challenge of delivering that data where and when it is needed. Download this special report to get a deeper understanding of the key technologies and best practices shaping the modern data architecture.


Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, the reality is most IT departments are struggling just to keep the lights on. A recent Unisphere Research study found that the amount of resources spent on ongoing database management activities is impacting productivity at two-thirds of organizations across North America. The number one culprit is database performance.


Since its early beginnings as a project aimed at building a better web search engine for Yahoo — inspired by Google’s now-well-known MapReduce paper — Hadoop has grown to occupy the center of the big data marketplace. Right now, 20% of Database Trends and Applications subscribers are currently using or deploying Hadoop, and another 22% plan to do so within the next 2 years. Alongside this momentum is a growing ecosystem of Hadoop-related solutions, from open source projects such as Spark, Hive, and Drill, to commercial products offered on-premises and in the cloud. These next-generation technologies are solving real-world big data challenges today, including real-time data processing, interactive analysis, information integration, data governance and data security. Download this special report to learn more about the current technologies, use cases and best practices that are ushering in the next era of data management and analysis.


The value of big data comes from its variety, but so, too, does its complexity. The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information. While companies are investing in initiatives to increase the amount of data at their disposal, most are spending more time finding the data they need than putting it to work. Download this special report to learn about the key developments and emerging strategies in data integration today.


When asked recently about their top reasons for adopting new technologies, the readers of Database Trends and Applications all agreed: supporting new analytical use cases, improving flexibility, and improving performance are on the short list. To compete in our global economy, businesses need to empower their users with faster access to actionable information and a better overall picture of their operations and opportunities. At the forefront of this journey to create value from data is in-memory processing. Download this special report to learn about the latest developments surrounding in-memory data management and analysis.


Download this special report to guide you through the current landscape of databases to understand the right solution for your needs.


From fraud detection to ad targeting, supply-chain optimization to campaign forecasting, the key use cases for big data require a successful analytics program. Businesses are investing heavily in initiatives that will increase the amount of data at their fingertips. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months according to a recent study from Unisphere Research. However, many businesses are spending more time finding needed data rather than analyzing it. To compete on analytics, the right mix of people, processes and technology needs to be in place to generate value. Download this special report to learn about the key technology solutions and strategies for succeeding with big data analytics today.


Today, the world of decision-making, along with the data sources and technologies that support it, is evolving rapidly. The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. A new data warehousing architecture is emerging, along with a new generation of technologies and best practices, to support the requirements of big data and the need for faster decision-making. To learn about the new technologies and strategies paving the way, download this special report today.


The “pie-in-the-sky” days of big data may be over, but the urgency for businesses to compete on analytics is stronger than ever. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months based on a recent study from Unisphere Research. The conversation around big data is shifting, from why to how. How can businesses harness the bits and bytes of data being captured inside and outside their enterprise to improve, empower and innovate? To learn about the key big data success stories today, download this special report.


The hottest term today—the “Data Lake”—is currently coming off the hype cycle and into the scrutiny of pragmatic IT and business stakeholders. As with all big concepts that have transformed the industry, from the early days of data warehousing and business intelligence, to the growth of cloud computing and big data, best practices are ultimately proven to deliver the benefits promised. To clarify the ambiguities surrounding the concept of the Data Lake, Unisphere Research and Database Trends and Applications combined forces with Radiant Advisors to publish a comprehensive report, “The Definitive Guide to the Data Lake.” By combining an analysis of fundamental information management principles with existing customer implementations of big data and analytics, this report explains how current data architectures will transform into modern data platforms. Download your copy today. Sponsored by industry-leaders Hortonworks, MapR, Teradata and Voltage Security


From hybrid databases that can process structured and unstructured data - and run transactions and analytics - in the same location, to hybrid data architectures that bring together both established and new database approaches to address the requirements of different data sources, workloads and applications, the reality that most organizations are facing today is that the world of big data is a multifaceted one. To be successful, organizations need speed, scale, flexibility and agility. At the same time, they need ways to keep down costs and complexity. To learn about the key technologies and approaches to hybrid databases and data environments, download this special report from Database Trends and Applications.


Today, there are more things connected to the Internet than people on the planet. From home appliances and cars, to light bulbs and livestock, if you can attach a sensor to it, it can be become part of a universe of physical objects able to communicate and interact digitally. According to estimates, this universe is on track to exceed over 25 billion devices by 2020, not including PCs, tablets and smartphones.


Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level, where data is captured, stored, and processed. This transformation is being driven by the need for more agile and flexible data management processes in the face of increasing volumes and varieties of data.


Whether Hadoop becomes the de facto data management platform of the future or simply a key component in a hybrid architecture comprised of numerous technologies, one thing is for sure: Hadoop adoption is growing. In fact, a recent survey conducted using subscribers of Database Trends and Applications found that 30% have deployed Hadoop at their organization while 26% are currently considering or planning for its adoption within the next 12 months.


Ask the average DBA how they spend the majority of their time and the answer is almost always going to be “performance tuning.” Optimal performance is a constantly moving target. Database transactions and volumes are constantly growing. Business applications are increasing in sophistication with greater user requirements. To stay competitive, companies want speed, scalability, high-availability and cost-efficiency. The challenge, of course, is getting there. Many IT departments are researching new technologies to address these issues, from database monitoring tools, to new types of databases, to virtualization and cloud solutions. In a recent study conducted over 285 organizations across North America, database performance monitoring was ranked the top area ripe for automation. This same study found that migrating or upgrading databases was the top area for investment, followed closely by virtualization and cloud.


Data integration is a crucial part of the equation for any business interested in fully harnessing its information resources. However, data integration challenges are multiplying in step with the growing complexity of data environments. Most organizations today are dealing with an ever-expanding array of data sources and users with varying requirements. Therefore, it is no surprise that integration projects are topping the priority list. In fact, a brand new study conducted over the readers of Database Trends and Applications found that 38% of companies polled had integration projects in production while 30% were planning or piloting projects. Download this special report to learn about the key developments in the marketplace and new solutions helping companies overcome challenges.


In-memory computing is currently racing toward the mainstream and revolutionizing the way enterprises leverage data to support their business requirements along the way. How big is this revolution? Nearly 75% of IT stakeholders at organizations across North America surveyed by Unisphere Research believe that in-memory technology is important to enabling their organization to be competitive. To succeed in today’s economy, businesses need faster data processing, fresher data, and more cost-effective data systems. Download this special report to learn the ins and outs, as well as the key products available in the marketplace.


When it comes to databases, businesses have more choices than ever today. From the longstanding RDBMS, to the growing camps of NoSQL and NewSQL databases, the landscape is becoming increasingly specialized and best-of-breed. This transformation mirrors the plethora of challenges IT departments across industries face today: the need to handle larger data volumes, the need to handle new data types, the need to deliver data faster, the need to support more application users, and the need to operate more cost-effectively, to name a few. Download this special report to read about the current state of the marketplace and learn about the new technologies that are helping businesses address these challenges.


Real-time information processing, a concept that has been around for a long time, has been in vogue lately. One reason for its popularity is the fact that real-time capable technology and online services have become very affordable, even for small businesses. Another factor is that real time has the attention and interest of the boardroom and executive suite. The idea of being able to instantaneously sense and respond to threats and opportunities has a lot of appeal for business leaders vying for an edge in a fiercely competitive global economy. With technology chipping away at the time it takes to gather relevant and accurate data, there’s less need for bureaucratic, hierarchical decision-making structures. Emerging technologies are now becoming part of the enterprise scene—such as in memory technology, cloud, mobile, and NoSQL databases—are bringing more real-time capabilities to the fore.


Business intelligence and analytics has undergone a revolutionary shift over the past few years, a transition that is still working its way through enterprises and their processes. Nowhere is this more evident than in the rapidly changing roles and expectations of information workers—those managing the data, as well as those consuming it.


Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study revealed that 37% of organizations are now using or considering adopting a cloud database. Elastic scalability, high availability, flexible capacity planning, and self-service provisioning are among the key, sought-after benefits. While traditional concerns about data security and compliance still have some enterprises watching from the sideline, for many enterprises, the advantages of cloud databases are becoming harder and harder to ignore.


Since the 1980S, companies have invested millions of dollars in designing, implementing, and updating enterprise data warehouses as the foundation of their business intelligence systems. The founding principle of the data warehouse was simple: a single version of the truth to support corporate decision making. Today, the world of decision making, along with the data sources and technologies that support it, is evolving rapidly. The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. The unstructured and semistructured data that companies are collecting from social media, remote sensors, web traffic, and other sources needs to be integrated and combined for analysis to produce valuable insights for better decision making.


Listening to the pundits, you can be forgiven for thinking that the unstructured, “cloudified,” out-of-network data tsunami is poised to sweep through and shake enterprises out of their comfortable, relational worlds. But there’s more to the story than that. Enterprises still, and will likely continue to, rely on relational database systems as their transactional workhorses. These systems continue to evolve and adapt to today’s new data realities. Many relational database and data warehouse environments are opening to unstructured data, running in clouds, and supporting caches that enable real-time— or near real-time—decision making.


The next generation of databases and data platforms is coming into full fruition to help enterprises more effectively store, process, analyze and deliver value from Big Data. This report hones in on the key challenges and opportunities ahead, and provides in-depth information on leading-edge technologies and solutions. Download your copy today to stay ahead of the latest developments in NoSQL, NewSQL and Hadoop.


This DBTA Thought Leadership Series discusses new approaches to planning and laying out tracks and infrastructure, moving to a real-time analytics requires new thinking and strategies to upgrade database performance. What are needed are new tools, new methodologies, new architectures, and a new philosophy toward managing data performance.


Today’s 24/7 enterprises require a well-designed, next-generation data integration architecture. Why is data integration so difficult? For many organizations, data integration has been handled as a dark art over the years, implemented behind the scenes with ad hoc scripts, extract, transform, and load (ETL) operations, connectors, manual coding, and patching. Often, front-end applications to get at needed data are built and deployed one at a time, requiring considerable IT staff time, as well as creating a waiting period for business decision makers. This one-off, manual approach to data integration will not work in today’s competitive global economy. Decision makers need information, at a moment’s notice, that is timely and consistent. However, they are challenged by their organizations’ outdated data integration systems and methods. Often, information may be delayed for weeks, if not months, by the time it takes to develop handcoded scripts to deliver requested reports. The process


Hadoop is marching steadily into the enterprise, but key challenges remain, from manual coding demands to a lack of real-time capabilities and the time it takes to bring a Hadoop project into production. At the same time, brand-new startups and veteran software companies alike are delivering new offerings to the marketplace to make it easier to deploy, manage, and analyze Big Data on Hadoop. From data integration and business intelligence tools to integrated analytical platforms and a new wave of SQL-on-Hadoop solutions, the common goal is to help companies unleash the power of Hadoop for Big Data analytics. Download this special report to learn about the key solutions. Sponsored by MarkLogic, RainStor, Tableau, Qubole, Karmasphere, Appfluent, and Hadapt.


UNSTRUCTURED DATA: Managing, integrating, and Extracting Value While unstructured data may represent one of the greatest opportunities of the big data revolution, it is one of its most perplexing challenges. In many ways, the very core of big data is that it is unstructured, and many enterprises are not yet equipped to handle this kind of information in an organized, systematic way. Effectively capturing and capitalizing on unstructured data isn’t just a technical challenge, it represents an organizational challenge. A flexible and agile enterprise environment—supported and embraced by all business units—will elevate unstructured data processing and analysis to a position in which it can help drive the business. This Thought Leadership Series is sponsored by Objectivity and Database Plugins


THE IDEA OF THE REAL-TIME ENTERPRISE is straightforward: Increase your organizational responsiveness through automated processes and raise organizational effectiveness and competiveness. If your organization can fulfill orders, manage inventory, resolve customer issues, and implement strategies to address changing circumstances faster and more efficiently, your organization is going to be more successful. However, for most enterprises, this is still an unrealized objective. Increasing data volumes, data varieties, and business demands are now stretching the limitations of traditional data management technologies and intensifying the challenge of integrating and analyzing data in real-time. Consequently, many organizations are looking beyond their current IT infrastructures. Download this report to learn about the leading technologies enabling organizations to deliver data across the enterprise in real-time. Sponsored by Oracle, SAP, Objectivity, JackBe and BackOffice Associates.


Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study found that nearly one-third of organizations are currently using or plan to use a cloud database system within the next 12 months. Download this complimentary report, sponsored by NuoDB, GenieDB, 10gen, Cloudant, Progress DataDirect, Clustrix, Objectivity and TransLattice, to gain a deeper understanding of the different types of cloud databases, their unique benefits and how they are revolutionizing the IT landscape.


BIG DATA, a well-used term defining the growing volume, variety, velocity, and value of information surging through organizations-has become more than a buzz phrase thrown about at conferences and in the trade press. Big Data is now seen as the core of enterprise growth strategies. Business leaders recognize the rewards of effectively capturing and building insights from Big Data, and see the greatest opportunities for Big Data in competing more effectively and growing business revenue streams. As the amount and variety of data grows, so do the skills required to capture, manage and analyze this data. This specialized issue of Best Practices from Oracle, Attunity, Couchbase, HiT Software Inc, Progress DataDirect, LexisNexis, Confio and Objectivity focus on a more formidable challenge: making Big Data valuable to the business. Complimentary from DBTA.


The appeal of in-memory technology is growing as organizations face the challenge of Big Data, in which decision-makers seek to harvest insights from terabytes and petabytes worth of structured, semi-structured and unstructured data that is flowing into their enterprises. This special thought leadership series provides context and insight on the use of in-memory technology, and detailed explanations of new solutions from SAP, Tableau Software, Tibco Spotfire, JackBe, Terracotta and MemSQL. Complimentary from DBTA.


Is your organization’s systems and data environments ready for the Big Data surge? If not, you are not alone. A recent study conducted among Independent Oracle User Group members by DBTA’s Unisphere Research finds that fewer than one-in-five data managers are confident their IT infrastructure will be capable of handling the survey of Big Data. This special Best Practices section from DBTA provides context and insight on the need to address this issue now, and detailed explanations of new technologies for dealing with Big Data from Aster/Teradata, MarkLogic, Akiban, Progress/Data Direct, Infinitegraph, HP-Vertica and Denodo. Complimentary from DBTA.


To compete in today’s economy, organizations need the right information, at the right time, at the push of a keystroke. But the challenge of providing end users access to actionable information when they need it has also never been greater than today. Enterprise data environments are not only growing in size, but in complexity - with a dizzying array of different data sources, types and formats. The September 2012 Best Practices in Data Integration, Master Data Management, and Data Virtualization report examines the data integration challenges and opportunities that Big Data is currently presenting data-driven organizations.


With the rise of big data, the database and data management tools market is in a state of flux, the likes of which have not been seen in this sector before. Companies are now awash in big data, and end users are demanding greater capability and integration to mine and analyze new sources of information. As a result, organizations are supplementing their relational database environments with new platforms and approaches that address the variety and volume of information being handled. In this special section in Database Trends and Applications analyst Joseph McKendrick brings you up on current thinking and strategies users and vendors are pursuing to extract value from large, often unwieldy data stores. This is followed by nine separate sponsored content pieces focusing on in-memory, real-time data integration, data virtualization, BI, columnar databases, NoSQL and Hadoop.


The rise of Big Datais challenging many long-held assumptions about the way data is organized, managed, ingested, and digested. However, for many organizations, Big Data is still a new frontier that they have only begun to explore. "Many organizations leave their data to pile up; they are aware of it as a resource but haven't analyzed it. They don't know what's useful and what's worthless." This fourteen-page section from the March edition of Database Trends and Applications is an invaluable resource that provides multiple perspectives on the chief challenges our readers face and the solutions that will enable organizations to begin tapping into the power of Big Data assets.


Key extracts from Database Trends and Applications from the December print edition focus on "Data Security and Compliance".


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