White Papers

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.

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.

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.

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.

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.

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.

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 challenges of cloud-Based analytics today & how Cloudera delivers a better cloud experience. A cloud-based analytics platform needs to be easy, unified, and enterprise-grade to meet the demands of your business. Nik Rouda discusses how Cloudera complements popular cloud services, such as Amazon Web Services (AWS) and Microsoft Azure, and offers the unified platform to organize, process, analyze, and store data at large scale...anywhere.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

Data—dynamic, in demand and distributed—is challenging to secure. But you need to protect sensitive data, whether it’s stored on-premises, off-site, or in big-data, private- or hybrid-cloud environments. Protecting sensitive data can take many forms, but nearly any organization needs to keep its data accessible, protect data from loss or compromise, and comply with a raft of regulations and mandates. These can include the Payment Card Industry Data Security Standard (PCI DSS), the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the European Union (EU) General Data Protection Regulation (GDPR). Even in the cloud, where you may have less immediate control, you must still control your sensitive data—and compliance mandates still apply.

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.

Organizations may be looking to improve their audit and analytics results by retaining larger amounts of data over longer time horizons, doing so can also result in increased risk, cost and impact to performance. How can organizations work toward solving these challenges while also drawing deeper insights from the data itself? This paper describes the roadblocks that organizations may face as they seek to take their data security and compliance efforts to the next level while juggling multiple priorities.

Critical data and information — including customer data, intellectual property, and strategic plans — are key to organizations’ competitiveness, but a breach of this data can have disastrous consequences. Though data security has long been the purview of IT and security teams, the market is shifting, and business executives must take notice. Our study found that CEOs and the board of directors (BoD) are the most accountable to external stakeholders when data is compromised. As evidenced by recent, highly publicized executive departures following a breach, their jobs are literally at risk. Other disastrous consequences include incident response costs, General Data Protection Regulation (GDPR) fines, plummeting stock prices, and shattered reputations. One thing is clear: A data risk management program is critical.

Guardium offers a holistic approach to protecting structured or unstructured data, including personal data, across a range of environments. The adaptable, modular Guardium platform can help compliance teams analyze risk, prioritize efforts and respond to events across their data repositories. Guardium tools can analyze data usage patterns to help rapidly expose and remediate risks with advanced, automated analytics and machinelearning, while supporting centralized management and smooth integration. Beyond initial compliance, Guardium helps enterprises continuously conform to evolving GDPR needs with its ability to adapt to new users and expanding data volumes, and with data classification support for multiple EU languages.

As data volumes continue to expand across databases, file systems, cloud environments and big- data platforms, and as compliance retention requirements lengthen (now up to five years for some regulations), there is increasing stress on IT organizations to address significant data management and storage requirements for data security solutions. As a result, the capacity and processing power needed to support today’s data security objectives has risen dramatically—and it will only continue to rise.

In response to increasingly complex cyberattacks, security pros devote resources to granular aspects of their networks. This is understandable and necessary to a degree, but it’s also a great way to lose sight of your ultimate goal: protecting customers and empowering the business. Zero Trust networks accomplish the dual tasks of deep, continuous data inspection across the network and lean operation and oversight — tasks that seem mutually exclusive in traditional networks. This report highlights the eight most significant ways Zero Trust boosts security and your business.

Today, the cyber-security attack surface continues to expand even as network perimeters vanish. Cyber-attackers have evolved from pranksters into organized criminals whose sole focus is separating you from yourmoney, your data, or both. But fear not, breaches can be avoided–if you know what not to do. This Battle Card highlights some common mistakes other organizations have made.

This paper looks at five of the most prevalent – and avoidable – data security missteps organizations are making today, and how these “epic fails” open them up to potentially disastrous attacks. Is your data security practice all that it should be? Read on to see if your organization’s data security practices are sound enough to face the pressure of today’s threat landscape.

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

IBM Security Guardium can help take the pain out of regulatory compliance so that compliance can be a valuable tool and not a hassle.

Database security is a broad section of information security that concerns itself with protecting databases against compromises of their integrity, confidentiality and availability. It covers various security controls for the information itself stored and processed in database systems, underlying computing and network infrastructures, as well as applications accessing the data.

As cloud computing becomes pervasive, security fundamentals remain the same: secure and protect data and support compliance.

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.

Think you know data governance? Think again. Download the white paper to see why.

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

This report, developed by the Economist Intelligence Unit, explores the challenges – and opportunities – for data governance both globally and across industries.

Are you building a solid defense for GDPR? Adding an offense can take your strategy from good to great.

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.

Conventional approaches to data governance that focus solely on operating models and org. charts no longer work.

Learn how your data lake can deliver on its potential instead of turning into a data swamp.

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.

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, and Titan.

Learn how to get started with Apache Spark™ 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 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?

The rise of the self-service analytics era has called for a more flexible, iterative approach to data preparation. But as you and your team look to invest in modern data preparation solutions, understanding how to best evaluate these technologies can be difficult. In Ovum’s first Decision Matrix report on self-service data preparation, analyst Paige Bartley takes a comprehensive look at the eight major data preparation vendors by assessing each company’s technology and execution.

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.

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.

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.

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.

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.

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.

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.

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.

If you're planning on migrating your on-premises production database to the cloud, while keeping test or development environments refreshed and in sync, this on-demand webcast is for you.

Wondering where Oracle’s decision to deprecate Streams leaves you? SharePlex delivers more flexibility and productivity in one affordable solution.

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!

Over the past couple years, the use of open-source database management systems has dramatically increased – so much, in fact, that experts are predicting a dramatic market shift from commercial, closed-source DBMSs to OSDBMSs within the next few years. With 31 percent year-over-year market growth since 2013, the numbers speak for themselves. During this webcast, you’ll see how our newest Toad product, Toad Edge, is addressing this new space. You’ll see how we’re supporting your organization’s commitment to open source RDBMS with proven commercial tooling to help you ramp up on MySQL and ensure quicker time to value. Toad Edge represents the next generation of Toad tooling – lightweight, flexible and extensible – simplifying MySQL development and administration, whether running in a Windows or Mac OS X environment.

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.

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.

Need an easier way to keep up with business and customer demands? In this educational session, you’ll learn how to integrate database changes with your DevOps strategy. And before you say, “That’s only for application development” – don’t worry. You’re about to see how to extend those same time-saving concepts to database teams.

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!

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.

The EU General Data Protection Regulation (GDPR) will go into force on May 25, 2018. Every organization — regardless of its location — doing business with EU customers will need to make changes to its oversight, technology, processes, and people to comply with the new rules. But where should you start? This report helps security and privacy professionals understand five core GDPR requirements and two related changes they need to start tackling today.

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.

Data diversity is king. The future of enterprise data management involves unifying types and sources of data, not just bigger amounts. But Data Scientists cite data variety as their biggest obstacle in analytics. Fortunately, Knowledge Graphs provide a schema-flexible solution based on modular, extensible data models that evolve over time to create a comprehensive and unified solution.

Enterprise data is the world's most strategic asset going forward, while on the ground it's painful, diverse, heterogeneous, and distributed. A Knowledge Graph is the only realistic way to manage enterprise data in full generality, at scale, in a world where connectedness is everything.

Data lakes add value, and their use improves organizations’ operations and business performance. To realize their value, business users need to be able to access and analyze the information available easily and efficiently. Download this ebook from Ventana Research to learn how enterprises are choosing tools and instituting processes to get the maximum value from their data lake.

Data has never been bigger. But is it providing more insight? While companies have tried to manage their immense data by storing it in data lake clusters, business people often have little access to these clusters. Too often, business users have to work with limited engineering resources to have any chance at discovering key insights and actionable information. Read this comprehensive eBook to learn how new self-service technologies can link business users directly to the Hadoop or cloud-powered lake. By allowing business users to access and analyze their data at a click of a button—no coding required—companies can get faster, better insights, which helps them stay ahead of the competition.

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.

Channel proliferation, global/local balancing acts, and increasingly informed customers are just some of the challenges marketers and web content strategists face today. In this forest of new changes and demands, there’s a relatively easy win - aligning strategies and leveraging content from technical communication for marketing purposes.

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.

View the demo of IBM Security Guardium Analyzer.

IBM® Security Guardium® Analyzer, a software-as-a-service offering, can help compliance managers, data managers, and IT managers get started on the GDPR journey by locating GDPR-relevant personal data in on-premises and cloud databases; classifying it; identifying vulnerabilities; and helping users understand where to get started to try and minimize risk.

Driven by the need to remain competitive and differentiate themselves, organizations are undergoing digital transformations and becoming increasingly data driven, leading to the proliferation of modern applications like IoT and Customer 360 built on massively scalable NoSQL data platforms including Hadoop.

Driven by the need to remain competitive and differentiate themselves, organizations are undergoing digital transformations and becoming increasingly data driven, leading to the proliferation of modern applications like IoT and Customer 360 built on massively scalable NoSQL data platforms including Couchbase.

Driven by the need to remain competitive and differentiate themselves, organizations are undergoing digital transformations and becoming increasingly data driven, leading to the proliferation of modern applications like IoT and Customer 360 built on massively scalable NoSQL data platforms including Cassandra.

The original data lake’s architecture has two severe drawbacks. One relates to the physical nature of the data lake which may kill the big data project entirely because it can be “too big” to copy to a central environment. The other relates to the restricted usage of the data lake investment – it’s designed exclusively for data scientists. Download this whitepaper and learn how data virtualization facilitates a multi-purpose data lake by allowing a broader and greater use of the data lake investment without minimizing the potential value for data science or for making it a less flexible environment. Multi-purpose data lakes are data delivery environments architected to support a broad range of users, from traditional self-service BI users to sophisticated data scientists.

The role of open source software in modern infrastructure is expanding – the operating system, the middleware, and now, the database. In fact, many organizations are implementing open source mandates and/or strategic initiatives to evaluate open source software and limit the use of proprietary software. It reduces costs, supports the shift from capital expenses to operating expenses and enables enterprises to benefit from community collaboration and innovation.

To modernize data and analytics environments, Fortune 500 companies worldwide rely on change data capture (CDC) technology. To learn more, download this book to understand how this critical technology works, why CDC is needed, and what your peers have learned from their CDC implementations. This practical guide is ideal for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC.

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.

This informative CITO Research paper talks to the pervasiveness of hybrid data, discusses three use cases and the business impact of embracing a hybrid data management strategy.

Business leaders recognize that their companies, industries, and markets are being disrupted by nimble digital players that are leveraging real-time information to respond to customers, predict trends, and manage operations. To compete in this new environment, they need to engage with customers, partners, and employees with speed and agility, to understand what is happening in their environments as they change, and to be able to employ cognitive technologies to predict and get ahead of trends. Emerging real-time data solutions, including those based on open source projects such as Apache Spark and Apache Kafka and commercial offerings, many of which are supported in the cloud, are enabling just about any business to be transformed into a real time enterprise. Download this special white paper to understand the challenges, use cases, best practices and expert tips.

Data is driving modern business. Supplied with the right data at the right time, decision makers across industries can guide their organizations toward improved efficiency, new customer insights, better products, better services, and decreased risk.

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.

You are not alone if your databases are running on an older version of SQL server. Upgrading a database is a complex project that must be planned and executed carefully. Because of the cost of the project, many organizations must build a business case for the upgrade project as part of the project funding process. Read this white paper to learn the nine (9) reasons that will give you content for your business case to help get your upgrade project moving.

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.

This report was designed to give business and technology people the information they need prior to deciding where to focus modernization. Discussing data warehouse infrastructure and highlighting the components that are currently high priorities for data warehouse modernization.

Unleashing the true power of the web can be a daunting task if you are not equipped with the right tools. A one-size-fits all solution is not going to bring you the results you need. In order to harness its power, you will need a precise self-service solution which will deliver the exact content you need and filter out all the rest. Read this ebook to: 1) Understand why custom content aggregation is a more effective and relevant method than traditional aggregation; 2) Understanding how to enrich existing products and services with content monitoring; and 3) learn about 3 success stories from leading companies who are gaining an edge on competition through news aggregation and content monitoring.

Many of today’s largest organizations use the mainframe as part of their IT infrastructure, but did you know that anything that occurs on the mainframe can also be used to gain insight on the health and security of your mainframe infrastructure? Being able to collect operational intelligence via log information is vital to answering some of the most critical questions.

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.

Explore the link between data governance and analytics, and discover why good data governance is essential to analytics success.

Short-term investment to build pipeline is a shortsighted strategy. Learn how privacy investments can strengthen your brand.

AI is revolutionizing technology, but it’s still up to humans to drive decisions.

Learn how to defeat your data disasters, no spider activation bite required.

Pressure is mounting on organizations to support information capture and access, and process interaction beyond the corporate walls. However, recent AIIM research finds that 82% of those polled continue the tradition of accessing content via corporate file shares and virtual drives. Download this paper sponsored by ASG Technologies to learn how cloud applications can be leveraged to enhance and extend business processes beyond their corporate walls and support the increasing use of mobile devices.

The scope of what constitutes enterprise content has expanded dramatically over the past several years and the explosive volume has many consequences, perhaps the most important of which is that information workers struggle to locate and consume various types of content across corporate systems. Integrating content throughout an organization and making it available to employees in the application of their choice, securely and easily, is a critical first step to controlling enterprise content. This playbook provides an outline for a comprehensive plan to achieve enterprise content integration efficiently, and shares guidance on how to do so without any programming.

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!

Spend less time managing DB2 and more time on new projects and strategic initiatives. If you’re a Toad for DB2 user, you’ve experienced its powerful automation and administration capabilities. But are you truly getting the most out of your investment?

Join guest speaker IDC research director Melinda Ballou and Quest® Toad® product manager John Pocknell as they discuss the state of DevOps and the value it provides for continuous application delivery. This session will highlight the advantages of bringing database development into the DevOps pipeline. You’ll learn how this prevents bottlenecks from occurring when application releases require database changes. You’ll also get to see how new tooling helps integrate Oracle database development processes to accelerate DevOps momentum.

eHarmony leads the online dating industry through innovation. To support the company’s promise to build deep connections and lasting relationships for its clients, the IT team makes every effort to deliver a focused, welcoming, and intuitive online dating experience across all device platforms. eHarmony strives to provide the best matches for singles by constantly improving its two key applications—Singles and Matching. To accelerate new releases, eHarmony needed to streamline and improve its QA process, and deliver test data faster. Download this case study to learn how.

Data security is now a top imperative for businesses, and responding to regulatory pressure is a key hurdle to overcome. Yet all too often, businesses are forced to choose between locking down data for security purposes, or making that data easily accessible to consumers. Why not do both? Download this white paper to learn how real companies are eliminating data friction while keeping confidential data safe and secure.

Data anchors applications. Using the laws of physics as an analogy: data has a lot of mass, which means it takes a lot of energy to move around. Data entropy is ever-increasing as it spreads across silos, which also makes it more difficult to store and protect. And data has a high coefficient of friction. It’s often the most restrictive force in application projects. It’s a huge issue and it’s been this way for a very long time. Download this white paper to learn how real companies are transforming data management across the application lifecycle.

If modernizing your organization’s data warehouse strategy is an important imperative for your data-driven digital transformation, download the workbook today.

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.

The role of the DBA is evolving faster than ever. Increasingly, DBAs are expected to do things faster and cheaper, regardless of the database platform. According to TechValidate research, as many as 80 percent of DBAs support at least two different database platforms, and more than 40 percent support three or more. And the trend toward diversification shows no sign of slowing; at least 30 percent of DBAs expect their company to add a new database platform over the next 12 months.

Foglight for SQL Server complements SCOM by delivering the predictive performance diagnostics and deep details that DBAs need to really understand and resolve performance issues. This paper shows how.

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.

An Oracle® audit can be time-consuming, expensive, and stressful. This paper describes the best practices, third-party expertise, and tools that not only make an Oracle audit less stressful, but also eliminate Oracle back-license costs, back-support costs, and audit fees.

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.

Why use NoSQL? Innovative companies like AT&T, GE, and PayPal have successfully transitioned from relational to NoSQL for their critical web, mobile, and IoT applications. By understanding where to introduce NoSQL, how to model and access the data, and how to manage and monitor a distributed database, you can do the same.

New technologies are rapidly accelerating digital innovation, and customer expectations are rising just as fast. For nearly every industry this means customer experience has quickly become the next competitive advantage. In response, many organizations are switching to NoSQL databases to deliver the extraordinary experiences customers demand.

Businesses have traditionally run on two types of technology platforms, analytical and transactional. But neither was designed to handle the increasingly complex sequence of real-time interactions required by today’s business applications. As a result, leading businesses are quickly moving toward a new third kind of platform – the “system of engagement” – especially for their customer-facing apps.

The Couchbase Engagement Database is built on the Couchbase Data Platform with the most powerful NoSQL technology available for unmatched flexibility, performance, and availability at any scale. Many leading companies are making the move from Oracle to Couchbase to get the best performance and highest availability possible from their mission-critical business applications across regions and data centers.

Today’s web, mobile, and IoT applications need to operate at increasingly demanding scale and performance levels to handle thousands to millions of users. Terabytes or petabytes of data. Submillisecond response times. Multiple device types. Global reach. Caching frequently used data in memory can dramatically improve application response times, typically by orders of magnitude.

Today’s consumers rely on their mobile apps no matter where they are. If your apps are sluggish and slow or don’t work at all when there’s no internet connection, your customers will start looking for other apps that work all the time. To help you provide the always-on experience customers demand, database solutions like Couchbase Mobile have added synchronization and offline capabilities to their mobile database offerings.

Nearly every industry today is in the midst of a digital disruption driven by the unprecedented acceleration of new technology. This unstoppable transformation is redefining the rules of success and driving customer expectations higher every day. This Couchbase report reveals that to remain competitive – or even to remain in existence – businesses have no choice but to deliver consistently amazing customer experiences powered by the agile, responsive, and scalable use of data.

Introducing the Engagement Database – and why it’s a vital part of your digital transformation. Digital transformation is changing everything for customers. Their interactions. Their transactions. And most importantly, their expectations. Yesterday’s experience won’t bring customers back today. And yesterday’s transactional and analytical databases certainly can’t deliver the exceptional experiences those customers will demand tomorrow. Get your Engagement Database whitepaper now to learn: • What an Engagement Database is • Why engagements and interactions trump transactions in the digital economy • Why transactional and analytical databases can’t keep up • How to evaluate and adopt the best engagement database for your business You’ll also find out how Couchbase designed the world’s first Engagement Database with unparalleled agility, manageability, and performance at any scale. Get your whitepaper now and learn how Couchbase is built to deliver customer experience

Read this Forrester report to gain a better understanding of how Deep Learning will disrupt customer insights professionals in their mission to understand and predict customer behaviors.

With 15,000+ employees and annual revenues exceeding $4 billion (USD), Experian is a global leader in credit reporting and marketing services. The company is comprised of four main business units: Credit Information Services, Decision Analytics, Business Information Services, and Marketing Services.

Digital transformation is rapidly changing our lives and influencing how we interact with brands, as well as with each other. The digitization of everything, particularly the widespread use of mobile and sensor data, has significantly increased user expectations. This rapid adoption of newer technologies—mobile, digital goods, video, audio, IoT, and an app-driven culture—has resulted in new ways to engage customers with improved products and services. At the heart of this transformation is how organizations use data and insights from the data to drive competitive advantage. Gaining meaningful customer insights can help drive customer loyalty, improved customer experience, revenue and reduce cost.

Ponemon Institute is pleased to present the findings of Big Data Cybersecurity Analytics, sponsored by Cloudera. The purpose of this study is to understand the current state of cybersecurity big data analytics and how Apache Hadoop based cybersecurity applications intersect with cybersecurity big data analytics.

How a comprehensive, 360-degree view of customers based on a spectrum of data can enrich actionable insights. This TDWI checklist focuses on six strategies for advancing customer knowledge with big data analytics. It begins with the all-important first step: gaining as close to a complete, 360-degree view of customers as possible. Big data platforms that implement open source Apache Hadoop technologies enable organizations to assemble data for a 360-degree view. The checklist explores how to expand the impact of big data analytics while applying governance to ensure proper care of customer data. Taken together, the six strategies will help you apply big data analytics to attracting and retaining your organization’s most valuable asset: its customers.

The techniques of NLP and text analytics overlap a great deal. The differences mainly lie in the problem that each tries to solve. In the search world, natural language processing analyzes user inputs (queries) to understand their intent. It allows a user to communicate with a machine in a way that is natural for the user, which, of course, is not natural for the machine.To accomplish this, NLP operates on data so that a computer can understand a document—and the relationships it may infer—in the same way a user understands it. It’s wise here to remember that infer means to “make an educated guess.” This is where NLP and text analytics use many of the same methods.

Whether it’s through a contact center or self-service web portal, the support function is often where customers engage with your company. And in either context, giving answers quickly increases satisfaction and reduces churn. With AI-powered search at the core of your customer support systems, you can make the support experience much better for customers and easier for support staff.

Right now, we're surrounded by examples of machine learning, such as Google’s page ranking system, photo tagging on Facebook, customized product recommendations from Amazon, and automatic spam filtering on Gmail. Download this quick guide to learn how machine learning works and how businesses can use it to expand their analytics capabilities.

The unified digital workplace removes “friction” from the everyday activities that consume the time and effort of knowledge workers. AI-powered search is the lubricant that makes a frictionless digital workplace possible. It’s not marketing hype. It’s real. And it can transform your organization.

Massive declines in the cost of storage and computation have finally made cognitive computing economical. With the emergence of these methods from academia, organizations now have access to tools, solutions, and platforms that can deliver a better experience finding and discovering new insights. The focus has shifted to accelerating time-to-value in the deployment of cognitive search.

It used to take years for the improvements in search technology that emerged from academic research to filter down to commercial enterprises. Not any longer. Now it’s often a matter of months, which has accelerated the pace of change in and adoption of cognitive search. Cognitive search can speed innovation in the life sciences while increasing productivity and lowering cost.

Over the past several years, HVR has worked with a variety of customers as they adopt the cloud, specifically the AWS cloud. We created this guide to share best practices uncovered in working with them on their cloud data integration projects. With this guide, we hope to provide key considerations when architecting a data integration solution that enables a cloud solution for today and the future.

While the Internet of Things (IoT) may still be unfamiliar to many consumers, businesses are well aware of its potential. More than 90% of participants in our research said that IoT is important to their future operations. Most said they view IoT as very important to speed the flow of information and improve responsiveness within business processes and nearly half are using IoT in their analytics and business intelligence functions. In implementing IoT systems, however, organizations face challenges. In particular, many struggle to maximize the value of IoT event data.

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 to costeffectively address analytics use cases such as fraud detection, real-time customer offers, market trend/pricing analysis, social media monitoring and more.

The changing nature of data integration demands a new approach. With the Attunity data integration platform, users benefit from a simple, automated, drag-and-drop GUI design that enables very high-volume universal data replication and ingestion as well as real-time, continuous data integration and loading: an approach that’s so easy, you could say it’s magic.

Over the last 10 years, major trends in technology have shaped and reshaped the ongoing role of the DBA in many organizations. New data types coupled with emerging applications have led to the growth of non-relational data management systems. Cloud technology has enabled enterprises to move some data off-premises, complicating the overall data infrastructure. And, with the growth of DevOps, many DBAs are more deeply involved with application and database development. To gain insight into the evolving challenges for DBAs, Quest commissioned Unisphere Research, a division of Information Today, Inc., to survey DBAs and those responsible for the management of the corporate data management infrastructure. Download this special report for insights on the current state of database administration, including new technologies in use and planned for adoption, changing responsibilities and priorities today, and what’s in store for the future.

The General Data Protection Regulation (GDPR) goes into effect on May 25, 2018. Are you ready? If you’re like most organizations, the answer is probably no. But with fines up to 2-4% of global revenue for non-compliance, the pressure is on to comply.

Conquering data governance may seem like a superhuman task. But when you activate these five super powers, it gets a whole lot easier.

A new year means new challenges and opportunities (& budgets!). Learn how you can get ready for the year ahead by downloading the Collibra e-book, 7 Data Predictions for 2018.

For data to be actionable, it must be discoverable. But too often, business users spend too much time wandering the data aisles searching for the information they need. It’s time to end the data search grind.

You’re already a data expert, so why do you need to become a data governance expert too? Because the business of data is changing. It’s no longer about building a better data warehouse. It’s about making sure your data can deliver value to the business. Learn how to be the expert your organization needs to turn your data into a strategic asset.

Dentegra Group, Inc. (Dentegra), dont le siège social est basé à San Francisco, est une société de portefeuille qui constitue le plus grand réseau de soins dentaires des États-Unis.

Boeing Employees’ Credit Union (BECU) est la plus grande coopérative de crédit de l’État de Washington et la quatrième plus grande des États-Unis, avec plus de 17 milliards de dollars en actifs et plus de 1 million de membres. BECU est une institution à vocation numérique, avec plus de 40 branches dématérialisées et seulement [2] [peut-être plus] centres financiers offrant une expérience traditionnelle au guichet.

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.

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.

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.

The world of data management is changing rapidly, from the way we work, to the underlying technologies we rely upon. Since the term “big data” swept the world off its feet, Hadoop, NoSQL and Spark have become members of the enterprise landscape, data lakes have evolved as a real strategy and migration to the cloud has accelerated across service and deployment models. From systems of record, to systems of engagement, data architecture is an important as ever and the traditional models aren’t working anymore. Download this report for the key technologies and trends shaping modern data architectures, from real-time analytics and IoT, to data security and governance.

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.

The desire to compete on analytics is driving the evaluation of new technologies on a large scale. Many businesses are currently starting down the path to modify their existing environments with new platforms and tools to better connect the dots between the data world and the business world. However, to be successful, the right mix of people, processes and technologies needs to be in place. This requires an analytical culture that empowers its users and improves its processes through both scalable and agile systems. Download this special report to learn about about the key technologies, strategies, best practices and pitfalls to avoid in the evolving world of BI and analytics.

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.

Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.

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".