Inside the report, organizations share their:
Number and types of Kubernetes workloads deployed
Concerns and challenges around adopting Kubernetes
Multi-cloud adoption timelines
Serverless adoption strategies
Analytic vs. transactional database needs & usage
Production environment preferences
This new book from O’Reilly will teach developers, architects, and devops teams how to build, optimize, and manage applications that run on CockroachDB. It includes specific guidance for anyone transitioning from a monolithic database (e.g., MySQL or PostgreSQL) to a distributed architecture, as well as practical examples for anyone more familiar with NoSQL systems.
HPE commissioned Forrester Consulting to evaluate the current state of HPC and AI infrastructure. Forrester conducted an online survey with 464 AI and/or HPC, global decision-makers and practitioners.
The study revealed that combining AI and HPC enhances the value of both, leading to increased business agility, innovation, and competitive differentiation, with shared infrastructure being a critical component of unifying both disciplines.
Mark III Systems is a leading digital and IT transformation solutions provider with a long, rich history of providing “full stack” technology solutions to enterprises and service providers across North America.
Since 2016, the State of AI and Machine Learning Report has given insight into the adoption, implementation and scale of AI across industries. This large-organization study of senior business leaders and technologists details where organizations are within their AI journey — from the types of data they leverage to the tools they use and budgets they have.
Overall, this report will help you understand the broader context of your AI work, what your peers are experiencing, and what dials to turn for AI success.
Download the report to learn:
How AI budgets have increased YoY
Why many are accelerating their AI strategy as a result of COVID-19 in 2020 and into 2021
Why companies are reporting a higher commitment to data security and privacy
Many companies ingest more than a petabyte of data but struggle to turn it into high-quality training data for machine learning algorithms that fuel core business. When coupling AIOps with professional services to guide the entire AI-creation journey, AI and ML will increase efficiencies and innovation around volume, quality, and speed requirements for training data.
Download the AIOps for Business Leaders white paper to learn about:
How AIOps with training data can power your tech stack, which translates to scalable AI
Why machine learning and the big data flywheel will create momentum that takes programs from experiment, to program, to core product
Key data-labeling resources for AIOps
There's a saying of garbage in, garbage out. It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms: the training data itself. Your model is only as good as the data it's trained on.
Pulling from over 20 years of experience, we break down how to approach training data, starting with raw data and annotating and labeling it so that it can be used to power the most ambitious projects. It's how we help some of the most innovative companies in the world. This guide will give you a few of the lessons we’ve learned along the way.
In the Essential Guide to Training Data we’ll cover everything you need to know about creating the training data necessary to drive successful machine learning projects, including:
Why having a lot of big data isn’t the same as having labeled data
How to determine which labels to use to evaluate your success
Where to find some great open datas
Your business relies on data. And your users want it fast. IT and data teams are seeing a dramatic increase in data demands, on top of a massive explosion in data volume and extensive array of new capabilities.
In an increasingly competitive and noisy business landscape, Managed Service Providers (MSPs) need to stand out – and not just for the expertise of their IT consultants. Security, compliance, and time-to-productivity issues are very much on every customer’s mind, and the MSPs that come out on top in today’s marketplace will be those that deliver on those issues.
Even as employees return to the office, most organizations say they’ll continue to facilitate remote work through a hybrid workforce. This is an unprecedented period – one ripe with opportunities to harness global talent, but also serious IT risks. That’s why CISOs everywhere need a clear, effective plan for scaling and securing a blended workforce of both in-office and remote employees.
Cinchy CEO and Co-Founder, Dan DeMers, and Concentra Bank COO and SVP, Neal Oswald, share thought-provoking insights on why Dataware and Data-Centricity are key components of successfully unlocking a business and technical advantage.
Many of today’s popular data strategies provide limited ROI because they don’t change how applications manage data. Trends such as data marts, data warehouses, data lakes, master data management, etc. all have limitations and require costly integration with applications.
There is a major shift away from app-centric approaches toward modern data-centric architectures. Why?
Data management is changing. It’s no longer about standing up databases and populating data warehouses; it’s about making data the constant fuel of the enterprise, accessible to all who need it. As a result, organizations need to be able to ensure their data is viable and available. Download this special report for the key approaches to developing and supporting modern data governance approaches to align data to today’s business requirements.
In a head-to-head comparison by GigaOm, Immuta’s attribute-based access control (ABAC) required 75X fewer policy changes than Ranger’s role-based access control (RBAC) to accomplish the same security objectives. The downstream impact of this is enormous, with the attribute-based approach increasing cloud ROI by more than $300,000.
In this report, you’ll learn:
How Immuta compares to Apache Ranger
What’s so special about ABAC, in both simple and advanced access control scenarios
How data access policy changes translate to time and opportunity costs
Ready to deliver trusted data to your analytics platform in real time – and save millions of dollars in the process? Qlik commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Qlik’s Data Integration platform. In this study, you can read the key findings, drill into the details, and get a framework for evaluating the potential for savings in your own organization.
AI and machine learning adoption is growing faster than ever. However, many ML initiatives and use cases fail despite the high level of investment. Read this ultimate guide to understand the requirements for success, deployment strategies, and selection criteria for scaling ML use cases for the enterprise.
It finally happened: Your CIO has told you to prepare for the cloud migration of your organization’s databases. The digital transformation process is lengthy and riddled with the risks of moving your on-premises databases to the Microsoft Azure SQL database. Where do you start?
Quest offers a variety of information and systems management tools, explained in our white paper that will guide you along the migration path.
In this paper, we’ll explore these technical challenges in more detail, and then we’ll present reasons why Foglight® for Databases by Quest is the best choice for a monitoring solution that can help organizations overcome their challenges and move forward with transformation initiatives.
CIOs and senior IT leaders are business enablers tasked with delivering products and services to customers and employees. Many of today’s products and experiences are built on data, but little attention is often paid to the databases that make them possible. Quest Software offers tools that maximize database performance and identify issues before they impact users.
By properly using indexing, you'll make your databases run efficiently, allowing for fast data retrieval. But identifying and building the correct indexes isn't always a straightforward process. This MSSQLTips white paper will help by showing you how to overcome common indexing challenges.
Download this special research report today to learn about the latest trends in SQL Server environments, including the evolving data landscape, pressing challenges and the increasing movement towards cloud databases amongst the members of PASS, the world’s largest community of data professionals leveraging the Microsoft data platform.
Are you under pressure to release changes faster? Without the right continuous integration tools, it’s hard to keep up. But what if you could easily integrate database development and change management into your DevOps pipeline? The result would be continuous database operations. With the expert advice in this tech brief, you'll learn how to use end-to-end solutions to bring database into DevOps. You’ll see how continuous integration tools can help you reduce risk and accelerate release cycles.
Migrate your on-premises Oracle databases to leading cloud service providers using Quest® tools and safely minimize downtime, ensure data integrity, manage costs, monitor and optimize performance and perform ongoing replication.
The five SQL query optimization tips in this e-book comprise a method for tuning your SQL Server queries for higher speed and better performance. By monitoring wait time, reviewing the execution plan, gathering object information, finding the driving table and identifying performance inhibitors, database professionals like you can improve performance in your database environment.
Database professionals agree – SQL Server performance tuning is hard. And on top of that, it never stops because complex database environments are always changing with upgrades, application updates and queries. It often feels like as soon as you get one query optimized, there’s another one right behind it that’s eating CPU time or clogging
memory or otherwise slowing down the entire database. Then, add to that, the instances when the latest SQL Server version itself has made performance worse instead of making it better as promised.
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.
Today’s emerging architecture is built to change, not to last. Flexible, swappable systems, designed to be deployed everywhere and anywhere, and quickly dispensed at the end of their tenure, are shifting the dynamics of application and data infrastructures. The combination of containers and microservices is delivering a powerful one-two punch for IT productivity. At the same time, the increasing complexity of these environments brings a new set of challenges, including security and governance, and orchestration and monitoring. Download this special report for guidance on how to successfully leverage the flexibility and scalability that containers and microservices offer while addressing potential challenges such as complexity and cultural roadblocks.
With volumes of data continuing to grow rapidly, organizations feel more and more pressure to scale their log management systems to support business operations.
In 2021, ChaosSearch conducted a research project to better understand how customers are managing their ever-growing volumes of log data, and how they are harnessing it to drive their daily operations.
Download this report to learn key insights and findings, including:
Best practices for log data management, and how organizations assess their performance for each
Real-life customer use cases
Top challenges of log data management
Key investment areas this year
Surprising uses of log data management
Each day, the average enterprise’s cloud applications, containers, compute nodes, and other components throw off thousands or even millions of tiny logs. Each log is a file whose data describes an event such as a user action, service request, application task, or compute error.
Cloud operations (CloudOps) teams that study those logs can maintain stability by optimizing performance, controlling costs, and governing data usage. They can stay agile by responding to events that require speed, scale, or innovation. But this requires new approaches to log analytics pipelines. This whitepaper explains:
What CloudOps and log analytics mean
Why traditional pipelines for log analytics break down
How to streamline or re-architect these pipelines
Are open-source data solutions, like the ELK Stack, really as simple and cost-effective as they are purported to be?
Download this white paper to understand how costs are generated and can quickly mount, including: deploying your infrastructure, managing ongoing operations, scaling the stack as data grows, and building support plans.
The ChaosSearch Data Platform is the first viable alternative to ELK, delivering the same level of advanced log management and analytics while solving the underlying cost, complexity, scalability, and reliability issues inherent in ELK stack environments today.
Ready to embrace a better way?
This paper provides the details you need to build a rock-solid business case for switching from your existing ELK stack to an environment built for maximum efficiency.
Calculating the TCO of your existing ELK environment
Projecting the cost benefits of a shift to an innovative, cloud-based Data Platform
Constructing the business case to switch to ChaosSearch
Finding the factors or combinations of factors driving repeatable business is critical for building a successful new business. But with only one analyst monitoring and analyzing a company’s data, how can any business surface those insights in time to act? Fractory, the one-stop-shop for on-demand manufacturing services, faced this exact issue. Learn why Fractory turned to Sisu to get a complete view of the drivers of their e-commerce platform and identify where to act to drive repeatable business.
Global power generation companies are facing major disruptions as the means by which power is created, maintained, and distributed evolve. In the face of these changes, power generation companies must be willing to adapt to new opportunities and trends to remain operable and avoid real existential risk. The organizations that act now to supercharge their operations with data won’t just keep up, but stay ahead of the pack.
Verdantix and Cognite explain how to solve DataOps challenges in heavy-asset industries with technology.
Utilities are gathering more data than ever—from equipment, sensors, business processes, customer interactions and third-party sources. This data can drive down risk from operational change by helping utilities understand patterns and trends, and also by increasing flexibility and nimbleness. For utilities to become driven by data, rather than drowning in it, they must effectively and efficiently deliver the right, meaningful data to the personnel and applications that need it.
Learn from BCG and Cognite how two symbiotic perspectives on data and organizational change together enable effective enterprise transformation.
We believe data, algorithms, and software should power industry, freeing human creativity to shape a profitable, safe, and sustainable future. Today, heavy-asset industries like oil and gas, manufacturing, shipping, and power have reached a digitalization tipping point. Increasing access to data has made data handling a key differentiator.
Databases are vital to any organization. So with an increase in data growth, and a complex mix of both relational and non-relational database engines, protecting and managing applications has become a challenge for DBAs. Many organizations are now running Amazon RDS engines on AWS, along with Oracle, or SQL either on-premises or in the cloud. So how can you effectively protect these critical DB resources?
In this session, learn how DBAs can get complete data protection for databases across hybrid and cloud environments while providing visibility, control, and automation to meet SLAs, lower costs, and increase agility, all from a single platform.
Database architecture is an important consideration in any MarTech and AdTech strategy. If you’re an application developer or technical executive, learn why Aerospike’s innovative Hybrid Memory Architecture is the defacto standard among the world’s leading advertising and marketing technology organizations.
Adform replaces Cassandra with the Aerospike database to achieve predictable low latency at scale for its multi-screen marketing platform.
The Advertising industry historically has been built upon cookie technology whereby advertisers can glean very detailed user profile information in order to segmentation and target advertisements, profitably. However, with Google’s proposal to eliminate 3rd party cookies and compliance mandates notably in the US and EMEA, the need to create a new, better alternative to cookie technology is upon us.
The Trade Desk, the world's largest independent programmatic advertising DSP, needed to migrate from cloud back to on-prem for one of its largest Aerospike clusters. There were multiple catalysts for the change including a new business requirement, a new, tailor-built site, as well as risk and capacity challenges. This session will uncover the findings and methods used to gain confidence in the move.
This whitepaper provides a simplified reference architecture for Demand Side Platform datastores for real-time bidding and campaign reporting using Aerospike as the datastore technology.
Leading Ad Tech companies use the Aerospike non-relational NoSQL database to improve customer engagement, campaign effectiveness and top-line results.
Enterprise data fabrics offer the new way forward. The data fabric weaves together data from internal silos and external sources and creates a network of information to power your business’ applications, AI, and analytics. Quite simply, they support the full breadth of today’s complex, connected enterprise.
This is the first demonstration of a massive Knowledge Graph that consists of materialized and virtual graphs that span multiple cloud platforms. We show that it is possible to have a one trillion-edge Knowledge Graph with sub-second query times without storing all the data in a central location. This capability has the ability to usher in a new era where the Knowledge Graph is a powerful component of company profitability and competitive advantage.
For years, TDWI research has tracked the evolution of data warehouse architectures as well as the emergence of the data lake. The two have recently converged to form a new and richer data architecture.
Within this environment, data warehouses and data lakes can incorporate distinct but integrated, overlapping, and interoperable architectures that incorporate data storage, mixed workload management, data virtualization, content ETL, and data governance and protection.
This TDWI Best Practices Report examines the convergence of the data warehouse and data lake, including drivers, challenges, and opportunities for the unified DW/DL and best practices for moving forward.
While cloud data warehouses like the Snowflake Data Cloud make it easier and less expensive to manage large, rich data, most companies still face an analytics bottleneck. To effectively turn all that data into better decisions, we need better analytics tools purpose-built for Snowflake-scale data. Download this ebook to discover three quick wins to unlock the value of your Snowflake data for a faster, more comprehensive analysis.
This paper will guide DBAs, infrastructure admins, IT managers, IT security admins and IT directors along the path of migrating databases to Microsoft Azure SQL Database. It breaks the process into three phases—planning, migration and maintenance—and explores the logical steps in each phase. IT professionals will see how to smoothly navigate each phase and use Quest products to reduce the risks to system performance
and service levels, from start to finish.
Building a business case for consolidating taxonomies to create a unified marketing data governance practice is essential for marketing to deliver personalization at scale and accurately measure marketing campaign ROI.
Data teams struggle to architect, build, and operate data systems to meet rapidly expanding business requirements. Data observability seeks to address this new world of unprecedented data complexity with a systematic approach that builds on its predecessor technology, application performance monitoring (APM). It monitors and correlates data events across the data pipeline, data, and infrastructure layers, enabling business owners, DevOps engineers, data architects, data engineers and site reliability engineers to detect, predict, prevent, and resolve issues—in an automated fashion—that would otherwise break production analytics and AI. To succeed with data observability, data analytics leaders must assemble and prioritize requirements, then select a comprehensive data observability product that minimizes custom integration work. They should tackle small, achievable observability projects first, enlisting a cross-functional team of contributors to focus on key pain points, such as perfo
The database is no longer just a database. It has evolved into the vital core of all business activity; the key to market advancement; the essence of superior customer experience. In short, the database has become the business. What role does the new data environment—let’s call it “the era of data races”—play in moving enterprises forward? Download this special report to learn about the ways emerging technologies and best practices can support enterprise initiatives today.
Companies embarking on significant Hadoop migrations have the opportunity to advance their data management capabilities while modernizing their data architectures with cloud platforms and services. Consequently, having a comprehensive approach reduces the risk of business disruption and/or the potential for data loss and corruption. Download this special eGuide today to learn the do’s and don’ts for migrating Hadoop data to the cloud safely, securely, and without surprises, and key architecture strategies to follow.
As data lakes increasingly make their move to the cloud, it’s easier than ever to set up, maintain, and scale storage to meet your all your analytic needs. But, with all the platforms out there, it can be hard to know exactly which is right for you.
This study guide goes in-depth on the topics you need to pass the CKAD exam from the Cloud Native Computing Foundation. Learn core principles of services and networking, and gain a thorough understanding of state persistence and volumes. Practice with real sample exercises.
Source-to-target time reduced from days to seconds. 60+ million rows replicated hourly. And improved data delivery by 400%. These are just a few of the outcomes that leading organizations have seen after solving their data integration challenges with Qlik. Around the world, Qlik is helping enterprises in every industry streamline, accelerate, and automate their data pipelines to deliver in-the-moment data for immediate action.
It’s not every day a company launches a billion-dollar product. Samsung’s Mobile team does so at least twice a year. And with mounting pressure from lower-quality competitors and a rapidly changing global marketplace, it’s critical to understand the complex galaxy of variables that can impact success.
The marketing and analytics teams at Samsung had access to a wealth of dashboards and market reports, but digging even one level deeper into the data could take weeks to answer a single question. When the team needed to understand upgrade preference across demographics, device profiles, carrier loyalty, and more, they needed answers fast.
Read the case study to learn how Samsung is able to successfully address critical business decisions with an augmented intelligence solution.
The future of analytics in all its fast, proactive, comprehensive glory is in the cloud. But, to successfully unlock the speed and agility of your team, there are key data analytics platforms, data structures, and processes you’ll need to invest in first to get truly proactive in your use of data. Learn how to build the analytics stack of your dream with this blueprint for a better, faster, and more efficient cloud-native data architecture.
Sisu is a 2021 Gartner Cool Vendor for Analytics and Data Science. Gartner recognized Sisu based on evaluation in the areas of Augmentation, Contextualization, Composability and Automation.
Gartner Cool Vendors in Analytics and Data Science, Julian Sun, David Pidsley, Shubhangi Vashisth, James Richardson, May 10, 2021
The GARTNER COOL VENDOR badge is a trademark and service mark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular
Businesses need better quality data and analytics to drive decisions and respond to change. Read Gartner’s Top Trends in Data and Analytics 2021 to learn the critical investments companies can’t afford to ignore to build a disruption-ready and resilient organization.
Gartner, Inc. [Top 10 Trends in Data and Analytics, 2021], [Rita Sallam, Donald Feinberg, Pieter den Hamer, Shubhangi Vashisth, Farhan Choudhary, Jim Hare, Lydia Clougherty Jones, Julian Sun, Yefim Natis, Carlie Idoine, Joseph Antelmi, Mark Beyer, Ehtisham Zaidi, Henry Cook, Jacob Orup Lund, Erick Brethenoux, Svetlana Sicular, Sumit Agarwal, Melissa Davis, Alan D. Duncan, Afraz Jaffri, Ankush Jain, Soyeb Barot, Saul Judah, Anthony Mullen, James Richardson, Kurt Schlegel, Austin Kronz, Ted Friedman, W. Roy Schulte, Paul DeBeasi, Robert Thanaraj], [February 2021]
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission.
Download KgBase’s Vision Paper to learn how to build the ultimate enterprise knowledge system: a federated mesh of independently maintained no-code knowledge graphs.
To successfully make the journey to a data-driven enterprise, businesses are under pressure to extract more value from their data in order to be more competitive, own more market share and drive growth. This means they have to make their data work harder by getting insights faster while improving data integrity and resiliency, leverage automaton to short cycle times and reduce human error, and adhere to data privacy regulations. DataOps opens the path to delivering data through the enterprise as its needed, while maintaining its quality and viability. In this thought leadership paper, we will provide perspectives on the advantages DataOps gives to stakeholders across the enterprise, including database administrators, data analysts, data scientists, and c-level executives.
If you run Oracle, database upgrades and migrations are inevitable. While there are real benefits to performing these upgrades and migrations, changes of this scale introduce equally real risks of unforeseen issues and downtime. Native Oracle solutions provide some protection, but all have trade-offs or leave remaining risks.
Now that Oracle has deprecated Streams, Oracle Database Advanced Replication and Change Data Capture in Oracle Database 12c, they want you to buy Oracle GoldenGate. But this replacement is extremely expensive and leaves you vulnerable to downtime.
What if you could replace Streams with an affordable alternative that doesn’t expose you to risk? With SharePlex® data replication, you get even more functionality to avoid downtime and data loss than GoldenGate provides – all for a fraction of the price. See how you can achieve high availability, improve database performance and more with a more powerful and cost-effective replacement for Streams.
This technical brief examines those options in detail. You’ll see the differences — especially in recovery time — and discover how you can use SharePlex® by Quest® to stick with Oracle SE2 without putting your high availability and disaster recovery strategies at risk.
Even with the emergence of new technologies such as Apache Kafka and Spark, the ability to effectively support the speed and scalability requirements of real-time data can be difficult for many enterprises. In a recent survey of DBTA subscribers, nearly half indicated that streaming data is a top priority. However, less than half were confident that their infrastructure is capable of handling the demands. Download this special report for best practices to ensure confidence and performance in developing real-time analytical capabilities.
So what makes a good API? The same thing that makes products outstanding – design.
This ebook takes an in-depth look at the entire ML lifecycle and reveals how your organization can power more trusted and explainable AI use cases. Download it now to learn how to take control of the ML lifecycle—so you can build and scale practical AI use cases to solve your actual business problems.
Learn how each embedded use case increases the value of analytics for that company, and find out how you can do the same for your organization. In this paper, you'll learn how 5 companies overcame the challenge and offered their customers an analytic solution that added value to the original solution.
The challenge that each company faced
How each embedded solution was implemented
How long it took to implement the solution and achieve ROI
The main benefit of the embedded solution for the company and their customers
With more data than ever flowing into organizations and stored in multiple cloud and hybrid scenarios, there is greater awareness of the need to take a proactive approach to data security. Download this special report for the top considerations for improving data security and governance from IT and security leaders today.
There are many types of disruption affecting the data management space, but nowhere will the impact be more substantial than at the edge. Leading operations moving to the edge include smart sensors, document and data management, cloud data processing, system backup and recovery, and data warehouses. Download this special report for the key transformational efforts IT leaders need to focus on to unlock the power of IoT and the edge.
Today’s companies struggle with the challenges of protecting data cost-effectively. Cloud-based backup and recovery can help your company – no matter how large or small – achieve reliable, scalable backup at a much lower cost compared to expensive on-premises protection.
Find out how your company can benefit from cloud-based backup and recovery. Read this white paper to learn:
• The difference between “backup and recovery” and “disaster recovery”
• Why cloud-based backup and recovery costs much less than on-premises backup
• How easy it is for your end-users to access cloud-based data storage
• Why cloud-based backup is ideal for remote-office/branch-office storage
How to integrate cloud backup with Oracle RMAN to reduce costs and complexity
Oracle’s RDBMS has been the gold standard for managing structured data for decades, and today most major businesses rely on it for their mission-critical applications. Yet maintaining relational database integrity during a backup can be complex. It takes keeping physical parameters secure and database processes consistent as well as auditing data trails and performing risk-based validation. To reduce this complexity, Oracle introduced Recovery Manager (RMAN) as its standard tool to handle basic backup and restore functionality.
Read this white paper to learn about the fundamental backup concepts applicable to RMAN and how Druva works with an Oracle image copy and incremental merge features to securely protect an Oracle database in the cloud. As an Oracle Backup Solutions Program (BSP) partner, Druva also gives additional control of data protection to your backup admins and teams while it provides:
SSIS is a popular and mature tool for performing data movement and cleanup operations. In the Microsoft ecosystem, SSIS is one of the most common extract, load, and transform (ETL) tools in use today. SSIS is powerful and configurable, yet surprisingly easy to use.
Execution plans provide a rich source of information that can help us identify ways to improve the performance of important queries. Sometimes, performance will still not be good enough, even after multiple performance tuning techniques are applied.
We've curated several articles to help you understand the plans themselves and the optimizer's strategy behind them. Learn how to effectively optimize your queries from industry experts.
Is your application easy to monitor in production? Many are, but sadly, some applications are designed with observability as an afterthought.
Quarkus can save as much as 64% of cloud resources as compared to Framework A when running in native mode and 37% when running on a JVM. Read how.
Learn four reasons developers should try Quarkus, a modern, Kubernetes-native Java framework.
Cloud-native application development is a key part of open transformation. By focusing on technology, processes, and people, you can deploy innovative, open approaches that support business agility, transformation, and success. Align your cloud technology with your business needs.
Learn how to build application environments for reliability, productivity, and change.
O’Reilly provides reusable Kubernetes patterns so containers can improve rapid app development. Learn how to use Kubernetes to support cloud-native app development.
This O’Reilly e-book explains how to build Kubernetes Operators using SDK and the Operator Framework. Learn how Operators are used to automate the app life-cycle.
In addition to data being more diverse, distributed, and dynamic, a growing number of organizational roles work with data daily to complete tasks, make decisions and affect business outcomes. This tide of users is increasing the demand for data consumption throughout organizations. The data must be accessible from anywhere people are working but controlled to ensure the data is being used by the right resource and for the right reason.
Enter the cloud-native data warehouse to meet these demands, take advantage of cloud scale and elasticity, and reclaim control of data in the cloud. Cloud-native data, data lakes, and data warehouses require cloud-native data integration solutions that can also take advantage of cloud scalability and elasticity to help calm the storm.
Download this Technology Spotlight by Stewart Bond, Research Director of Data Integration and Data Intelligence Software at IDC, to learn about the benefits of cloud-native data integration, the trends surrounding it
The only constant is change, especially when it comes to technology. And 2020 was a year of rapid and sometimes tumultuous change. Some changes will be permanent and will affect the trends we’ll see in the coming year. Our need to work 100 percent remotely, and our need to have data accessible to us no matter where we worked, accelerated the move to the cloud for many organizations. IDC predicts that 80 percent of enterprises will speed up their shift to the cloud.
As we leave behind a year that generated— and required—so many changes in the way we work with data and each other, let’s take a look at the data integration trends you can expect to see in 2021.
This eBook highlights how four leading financial services organizations are accelerating their speed-to-insight with fast analytics to drive some of the most compelling and mission-critical use cases today.
-Real-Time Fraud Detection
Enabling real-time fraud detection in under 50 milliseconds with a modern real-time data infrastructure.
-Modernizing the Wealth Management Experience
Delivering premium data experiences for 40,000 users requires reliable ingest and query performance under extreme market conditions.
-Smart Portfolio Management for Reduced Risk
Dramatically improve the performance of analytical engines to continuously assess risk and optimize portfolio performance, to recommended actions in real-time.
-Operational Analytics for Digital Transformation
Deliver near real-time visibility into business performance and enterprise operations across finance, support, sales, marketing, and other business functions.
When it comes to thwarting cyberattacks, every millisecond matters.
Nucleus Security needed an underlying database that was truly fast and scalable to power their vulnerability management platform.
With SingleStore, they were able to dramatically improve performance by 50x, at ? the costs of the alternatives.
Join us for a 45-minute interactive session, with Nucleus Security & SingleStore, to learn more about:
• How SingleStore reduced Nucleus Security’s vulnerability scan from hours to minutes
• Why Nucleus Security chose SingleStore over MariaDB and other alternatives
• How they were able to achieve this without any architectural changes
Scott Kuffer, Co-Founder & COO, Nucleus Security
Domenic Ravita, Field CTO, SingleStore
Speed & Scale
As an application developer speed and performance at scale are key to delivering an optimal customer experience. It's even more critical in Cybersecurity where your application or platform needs to ingest and process millions of events every second.
At Nucleus Security, Scott Kuffer, the COO and his development team were constrained by performance and scalability bottlenecks with MariaDB to power their vulnerability management platform. They needed a relational database that was truly fast and scalable that enabled them to do ultra-fast data ingestion at scale while running thousands of low-latency queries in parallel.
Learn more about how Nucleus Security, with SingleStore, was able to increase in the number of scans processed in one hour by 60x, with 20x improvement in performance of the slowest queries.
For organizations with growing data warehouses and lakes, the cloud offers almost unlimited capacity and processing power. However, transitioning existing data environments from on-premises systems to cloud platforms can be challenging. Download this special report for key considerations, evolving success factors and new solutions for enabling a modern analytics ecosystem.
Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns. Download this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.
For a variety of reasons, organizations are moving their workloads to the cloud. Our research shows that one-third of organizations have their primary data lake platforms in the cloud and most organizations (86%) expect the majority of their data to be in the cloud at some point in the future. Those organizations that already have the majority of their data in the cloud report they gained a competitive advantage, decreased time to value, and improved communication and knowledge sharing in their organizations.
Paul Scott-Murphy, VP of Product Management at WANdisco, demonstrates using LiveData Migrator to migrate actively changing data from an on-premises Hadoop environment to AWS S3, and leveraging the WANdisco UI to manage and monitor the migration.
AWS and WANdisco show how GoDaddy easily automated their big data migration with zero business disruption—and how you can too.
In this LiveData Unplugged session Tony Velcich, Sr. Director of Product Marketing at WANdisco, speaks with Steve Kilgore, Vice President, Field Technical Operations at WANdisco. Steve discusses the top 10 list of cloud data migration mistakes he and his team of global solution architects have witnessed while working with customers on their cloud data migration initiatives.
This is the inaugural session of the LiveData Unplugged series. In this first episode Tony Velcich, Sr. Director of Product Marketing at WANdisco, talks to Daud Khan, VP of Corporate Development at WANdisco, about WANdisco’s LiveData strategy. In this session they cover what is a LiveData strategy, and the benefits it provides to organizations moving their on-premises data lakes to the cloud and want to ensure data consistency across multiple distributed environments.
The adoption of hybrid and multicloud environments is accelerating; boosted by a mounting urgency for enterprises to digitally transform into more efficient and agile operators. At the same time, the challenges of managing, governing, security and integrating data are growing in step. Download this special DBTA report to navigate the key data management solutions and strategies for surviving and thriving in the growing hybrid, multi-cloud world.
Microsoft SQL Server sites are challenged with delivering greater capabilities at the same level of budget and staff. Backup and recovery processes have been in place for decades and so have many of the solutions and approaches offered. These solutions are no longer a match for the pace and scope of today’s digital enterprises. To create an environment that can effectively support digital transformation, enterprises need to move toward providing backup and recovery of databases that spans across on-premises and the cloud.
Today’s Oracle DBAs have a lot on their plate: a growing number of databases, expanding data volumes, not enough time and resources, pressure to do more with less, and the need to manage database protection across cloud and on-premises environments. These are all reasons why you need modern backup and recovery strategies that provide automation and access to data to drive other business needs.
Organizations are beginning to invest heavily in artificial intelligence (AI) initiatives. Some are spending millions of dollars on AI strategies, and with good reason. Companies that have already adopted AI report that it has allowed them to edge ahead of competitors.
This report illustrates the current state of AI and machine learning, detailing how organizations are implementing AI within their business. From the types of data that companies leverage to the tools they use and budgets they have, this report shows the differences and commonalities between line-of-business owners and technical practitioners. For readers who might be in the midst of their own AI projects, understanding the dial turns for AI success will be invaluable.
Download this eBook to gain a thorough understanding of what data lineage is and learn the benefits for a variety of use cases affecting the entire data pipeline. You will also get familiar with automated data lineage provided by Octopai's BI Intelligence Platform, which is key to ensuring your data lineage won’t be left behind in 2021.
The popularity of the Oracle Unlimited Licensing Agreement (ULA) has grown over the last decade and doesn’t seem likely to decrease. Whether your organization simply needs more licenses (more seats) or you’ve been audited and a ULA is being recommended to avoid this happening again in the future, it’s absolutely critical to understand when a ULA is a smart investment, when it’s a terrible idea, and how to best navigate the pitfalls of the complex world of licensing.
Read this Cloudera Special Edition of Production Machine Learning for Dummies to learn what’s needed to succeed with production ML and how to successfully apply a production ML approach at scale in your enterprise.
Read this whitepaper from Blue Badge Insights to better understand how Cloudera Machine Learning MLOps capabilities and features were built to address industry and customer needs.
This guide covers how to deploy a stateful application with CockroachDB using Kubernetes StatefulSets. Topics include:
• An overview of options to deploy stateful applications
• Details about how StatefulSets and DaemonSets work in Kubernetes
• Steps to get your stateful application into production using CockroachDB
The truth is, there are a lot more things to consider when pricing out the total cost of a database. The hardware and software costs are still there, but you also need to think about the price of scaling the database, of integrating with your existing and future systems, and of planned--or unplanned--downtime.
In this comprehensive guide, we'll lay out a framework for you to think about the true costs of a cloud database.
In this practical guide written by a distributed systems expert, learn how to build apps that scale to handle unpredictable traffic with zero downtime for customers.
The 2021 Cloud Report is the only cloud performance report to compare AWS, Azure, and GCP on benchmarks that reflect critical applications and workloads.
Read the report to learn:
Which cloud is the most cost efficient
How to evaluate performance tradeoffs
How to assess the cost/benefit of disks and CPU processors
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges, and opportunities. The demand for fast, wide-ranging access to information is growing. At the same time, the need to effectively integrate, govern, protect, and analyze data is also intensifying. Download this special report for the top trends in data management to keep on your radar for 2021.
Datavail recently conducted a cloud adoption industry benchmark survey where hundreds of companies responded to questions and offered a wealth of insight into the cloud landscape. Download the white paper to learn more about the results and the 2021 cloud trends in order to refine your cloud strategy with lessons learned and best practices.
A series of global pandemics, man made disasters, and natural catastrophes have characterized the first two decades of the current millennium.
The 9/11 terrorist attacks, Hurricane Katrina, the Indian Ocean Tsunami, and the ongoing COVID-19 pandemic are the most vivid examples. Each of these has posed a unique challenge to the business continuity culture of both small businesses and large corporations. Over 40% of small businesses don’t open after a disaster and 25% more fail in under a year from the event according to FEMA.
DataOps is now considered to be one of the best ways to work toward a data-driven culture and is gaining ground at enterprises hungry for fast, dependable insights. Download this special report to learn about the key technologies and practices of a successful DataOps strategy.
Do you really know your data? Profiling is the first step in uncovering weaknesses in your database so that data can be captured accurately for use in analytics, business intelligence and master data management. How can you use data profiling to understand every nuance of your metadata and keep it in top shape? Find out now!
Data quality is important to your business for things like operational efficiency, analytics that mean something, and nurturing good customer relationships. But the question arises, should you buy an out-of-the-box solution, or build it yourself? There might be another, better way – a hybrid way. Curious? Download now!
The only constant is change – this goes for your data too. There is a critical need to cleanse and validate data when it’s received and on a regular basis. But how can you leverage the SQL Server tool set to achieve validation, cleansing and deduping? Find out how today!
What is the golden record? Data has a nasty way of becoming duplicated in your database and wreaking havoc on your business. What’s the most effective way to determine survivorship when merging and purging duplicate data? Learn why Melissa bases survivorship off of quality instead of other factors. Read it now!
Cloud data warehouses are at the heart of digital transformation because they require no hardware, are infinitely scalable, and you only pay data resources you consume. However, that’s not the whole story.
Azure Synapse, Amazon Redshift, Google Big Query and Snowflake all require real-time data integration and lifecycle automation to realize their full potential. Yet these two capabilities are not included, forcing you to hand-code ETL scripts to close the gaps. As a result, your developers are constrained and your data transfers are constricted, compromising your initial ROI.
Take a deep dive into data warehouse automation (DWA) to know its’ history, drivers and evolving capabilities. Learn how you can reduce the dependency on ETL scripting, advance your user experience, implementation, maintenance and updates of your data warehouse and data mart environments with Qlik Compose™.
If you are reading this eBook, you are probably considering or have already selected Google BigQuery as your modern data warehouse in the cloud — way to go. You are ahead of the curve (and your competitors with their outdated on-premise databases)! Now what?
Whether you’re a data warehouse developer, data architect or manager, business intelligence specialist, analytics professional, or tech-savvy marketer, you now need to make the most of that platform to get the most out of your data! With the growing masses of data being produced by sources as diverse as they are plentiful, a data-driven smart solution is needed. That’s where BigQuery and this eBook come in handy!
This whitepaper will outline the key tenets of a Data Analytics Platform (DAP) and illustrate how your business can adopt cloud technologies to de¬sign a fit-for-purpose solution that is cost efficient and scalable. A DAP can help your business ingest raw data, transform it, and use it for reporting, analytics, and visualizations - all at scale - giving your users the ability to draw out the relevant insights that inform better decisions.
A proof of concept (PoC) is a framework of tests to determine if a product will function as you envision, and if it will truly provide long-term value that merits an investment in technology and resources. These tests should not attempt to build an entire solution; they’re what confirm or deny that the entire solution should be implemented. A PoC is an implementation on a micro scale, that can demonstrate that the larger project can be done. The proof of concept should take minimal time and if it works, it creates a starting point for the development project as a whole.
You've selected Amazon Redshift as your cloud data warehouse - now what? Read "Optimizing Amazon Redshift" to understand how to connect the dots in your data and start focusing on what’s really important - finding answers to your business' most challenging questions. Includes tried and tested best practices from Matillion's data transformation experts, who help Amazon Redshift users on a daily basis.
You've selected Snowflake as your cloud data warehouse - now what? Migrating all of your business’ data from different locations and into Snowflake (and in the format you need) is difficult. Read "Optimizing Snowflake" to understand how to get the most out of your data and the insights you seek. Includes tried and tested best practices from Matillion's data transformation experts, who help Snowflake users migrate and transform their data on a daily basis.
Changes in data warehousing result in changes and developments in the supporting processes, applications and technologies. As such the origin, growth and decline of ETL can be mapped directly against data warehousing innovations. In this eBook we review pivotal moments in data warehousing history to understand the changes in ETL, ultimately resulting in the shift from E-T-L to E-L-T for modern cloud-based data warehouses.
How do you modernize your data warehouse? Start with implementing a cloud data warehouse that can keep pace with your growing data needs, and then explore solutions that are purpose-built to work with them.
With Matillion ETL, our customers and partners are able to make analytics-ready data available across their organizations in a fraction of the time it took before, allowing them to spend more time using data for business innovation. Read on to find out how cloud-native data transformation can help you reduce costs, centralize data sources for easier reporting, scale ETL workflows and processes, and prepare data for machine learning models.
How can businesses consolidate all their data? Centralizing all data within a data warehouse can prove effective for a number of use cases. While this approach may work for some businesses, others may require advanced scalability, accessibility, and a need to better control costs. A data lake, which allows all data types in any volumes to be stored and made available without the need to transform it before being ready for analysis, can address these unique requirements by providing a cost-effective resource for scaling, storing and accessing large volumes of diverse data types.
In “A Radical Guide to Data Analytics Mastery,” you’ll discover how easy it is to switch to a modern analytics approach. Use the spreadsheet skills you’ve already got — but take advantage of repeatability, transparency, and all the other great features of workflows.
In “How to Make Workflows and Influence People: A Trusted Guide to Empowering Your Organization to Solve with Alteryx,” we’ll show you how to be your company’s touchstone for data truth as a data champion. You’ll learn how to:
• Win over your boss. We’ll walk you through the components of a great Alteryx pitch and give you supporting stats and success stories.
• Energize your fellow analysts. We’ve included a handy package of resources to light their data fire.
• Convince your major stakeholders. We’ll tell you what they care about most and how to connect that to an Alteryx solution.
The move to modern data architecture is fueled by a number of converging trends – the rise of advanced data analytics and AI, the Internet of Things, edge computing, and cloud. Both IT and business managers need to constantly ask whether their data environments are robust enough to support the increasing digitization of their organizations. Over the past year, requirements for data environments have been driven largely by cost considerations, efficiency requirements, and movement to the cloud. Download this special report for emerging best practices and key considerations today.
This report highlights the current and future state of data engineering and DataOps, including analysis on:
The adoption rate of cloud data platforms and what it means for the future of data use
The most challenging aspects of the cloud data management process
Handling sensitive data in an increasingly regulated environment
What organizations and data teams need to remain competitive in our evolving data ecosystem
To read the full findings and what the implications for organizations adopting a multi-cloud strategy, download the Immuta Data Engineering Survey: 2021 Impact Report.
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
By observing how users and applications regularly consume data and limiting or stopping any abnormal consumption of data in real time, we get very close to a true Zero Trust posture. Going further and creating a governance model so that data requests must flow through it ensures an even tighter mechanism of control.
Whether through intentional malicious acts or simple negligence, people are the biggest threat to your enterprise data. Download this white paper to reveal the top 5 threats to your data, along with strategic measures you can take to eliminate & prevent exposure.
This exhaustive review of Vertica 10 from Constellation Research goes beyond a general product overview. If you are a technology buyer considering modernizing your data warehouse, then this report is a must read as part of your evaluation process.
Data and analytics can supercharge a company’s success and establish competitive advantages in any industry. Accordingly, companies are thinking more holistically about their analytics strategies. Inevitably, two questions surface: What is the best approach to analytics? And what technologies and capabilities are vital for an enterprise-level environment? Download this white paper today to understand the three specific necessities that consistently factor into the success of modern enterprise analytics.
Conquering data governance may seem like a superhuman task. But when you activate these five super powers, it gets a whole lot easier.
With the inherent risks that database upgrades often bring, business needs assurances that such upgrades combine minimum risk to data integrity with minimum downtime, low cost and flexibility. SharePlex meets those expectations by enabling a safe way to upgrade your databases to Oracle 19c, whether using Enterprise Edition or Standard Edition 2, regular instance, RAC or Exadata, on-premises or to the cloud. The combination of SharePlex reliability for moving data safely, support for multiple use cases and low cost ensures SharePlex is the tool of choice for your Oracle upgrades.
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.
Accurate data is imperative for an organization to conduct cost effective decision making, marketing promotions, mailings, database bloat impacting performance, storage and more. Like everything else, change is constant for your data. There is a need to cleanse and validate data when received and on a regular basis to not waste resources. Unfortunately, cleansing and validating data is difficult with the native SQL Server toolset. T-SQL, Integration Services, PowerShell and .NET all include a framework to validate strings, but significant programming logic is necessary. These technologies will not completely validate, cleanse, match and de-duplicate data. How do we leverage the SQL Server tool set to achieve these goals?
Melissa has a variety of tools available to clean, validate and enhance the Contact dimension in your SQL Server data warehouse. Specifically, Melissa’s suite of SSIS Data Quality Components can be leveraged for this task. The Melissa SSIS components are plug and play; you simply drag and drop the components onto the Data Flow, configure the component properties, and you are ready to go. There is no coding required.
The data landscape is evolving and growing exponentially, requiring organizations to find new ways to streamline and simplify their database estate management. The traditional methods of database management are manual, slow, complex, and expensive. Managing IT complexity demands significant process overhead for executing even basic tasks, such as adding additional capacity or upgrading software—adding unwanted friction to IT operations and slowing the business. Research shows that half of data management implementations use both on-premises and cloud environments, furthering database sprawl and management complexity.
Delivering high levels of performance is a requirement for IT environments that rely heavily on mission- and business-critical databases. This is especially important in dynamic environments where data growth is constant and continuous accessibility is a requirement.
Managing traditional legacy databases often involves multiple solutions that are expensive, time consuming, and slow, with significant storage and compute requirements. These roadblocks often lead to IT inefficiencies, inconsistent performance, and a cumbersome and expensive database estate.
The critical role of data as fuel for the growing digital economy is elevating data managers, DBAs, and data analysts into key roles within their organizations. In addition, this rapid change calls for a three-pronged approach that consists of expanding the use of more flexible cloud computing strategies, growing the automation of data environments, and increasing the flow of data and collaboration through strategies such as DevOps and DataOps. Download this special report today to better understand the emerging best practices and technologies driving speed and scalability in modern database management.
With financial services organizations under pressure to act quickly, responsibly and accurately to change, data analytics and Business Intelligence (BI) professionals have been instrumental in helping businesses remain resilient and accelerate decision-making.
To understand more about how they’re adapting to the new world of financial services and enabling innovation, Exasol has interviewed and surveyed professionals from financial services organizations in FTSE 100 and Fortune 500 lists to reveal which strategies they are implementing today.
To enable high performance, database teams should implement a core set of industry best practices. This ‘kernel’ of Database DevOps leads to better alignment between database and application teams and increases the throughput of higher quality releases. As a result, software teams can meet customer demands quicker and gain a competitive advantage over organizations where the database is a bottleneck.
This whitepaper describes four key practices for Database teams to implement when planning to follow a DevOps approach, and includes examples of successful customers.
See how Oracle Database 19c and Db2 11.5 compare to one another on cost and benefits for transactional deployments.
Discover how data management vendors stack up in Forrester’s recent wave. Then, dive into strengths like AI, real-time capabilities, and hybrid support.
Oracle, EDB, Move, Migration, Decision, Tips, Workload, Database, PostgreSQL, Postgres, Cloud, IBM, AWS, Microsoft, tools, schema, data, application logic, test, partners
PostgreSQL is an incredibly popular relational database management system. This white paper discusses in detail: skill set needs, setup and configuration, multi-model architecture, DevOps and production.
Learn more about the partnership between IBM and Cloudera and how their solutions can help prepare you for the Journey to AI. Their suite of offerings are described in detail, including: Cloudera Data Platform, Cloudera Data Flow, IBM DataOps, IBM Big Replicate, IBM Db2 Big SQL and many others.
Db2 is powered by AI and built for AI. This eBook explores what that means with an in depth look at the technologies supporting it.
Kafka performance relies on implementing continuous intelligence and real-time analytics. It is important to be able to ingest, check the data, and make timely business decisions.
Autoscaling is the process of automatically increasing or decreasing the computational resources delivered to a cloud workload based on need. This typically means adding or reducing active servers (instances) that are leveraged against your workload within an infrastructure. The promise of autoscaling is that workloads should get exactly the cloud computational resources they require at any given time, and you only pay for the server resources you need, when you need them. Autoscaling provides the elasticity that customers require for their big data workloads, but it can also lead to exorbitant runaway waste and cost.
Whether today’s organizations sink or swim in data depends on their ability to transform boundless data streams into timely, contextual insights relevant to their business operations. Continuous intelligence is the next big evolution in software architecture that helps organizations accelerate their digital transformation while contending with the inundation of data.
If a SaaS company cannot deliver fast analytics to thousands of users simultaneously through cloud applications, the business is in trouble. Companies must be able to deliver high-quality, fast insights to customers. That requires an underlying data warehouse and data analytics platform that can keep up—which can be hard with existing solutions. Too often, companies must limit the amount of data analyzed to deliver acceptable response times, such as limiting analytics to a few months of data instead of a year or more.
Existing data warehouse solutions were not designed for today’s SaaS companies. But a hybrid cloud data warehouse is, by providing sub second queries across petabytes of data. Learn more about how Yellowbrick is designed for SaaS companies in this eBook.
There’s a wide range of reasons why many organizations are deciding to modernize their data architectures. But they all agree on one thing: by using data more effectively, more widely, and more deeply, they can improve and optimize business and decision-making processes that will help them stay competitive in the emerging digital economy.
There is no doubt that cloud computing has become mainstream and organizations are strategically utilizing it to modernize their applications for a data driven architecture. With this maturity, the digital transformation and cloud adoption has become a lot more manageable than ever before. New trends are emerging to support a variety of use cases in hybrid and multi-cloud environments.
Organizations continue to struggle with integrating data quickly enough to support the needs of business stakeholders, who need integrated data faster and faster with each passing day. Traditional data integration technologies have not been able to solve the fundamental problem, as they deliver data in scheduled batches, and cannot support many of today’s rich and complex data types.
Within recent years, the ability and need to develop flexible and scalable high-performance environments has radically changed the methods and processes for reporting, business intelligence, and analytics. As organizations opt for cloud computing platforms and migrate their data and applications to a hybrid cloud environment, they utilize multiple platforms to support new and increasing data types for analytics.
Mainframe computers deliver mission-critical applications with strong performance, reliability and security. But unlocking insights comes at a cost. Are you looking to extract more value from your mainframe data – while offloading
processing to less expensive platforms, especially to the cloud?
Modern data analytics have the potential to reinvent your business. But to take advantage, IT has to reinvent how they move, store and process data. And integration is a big challenge.
There are constant challenges in the payments industry ranging from preventing fraud, verifying identities, and decreasing risk. Other items to grapple with include scaling a solution to deal with rapid growth and meeting strict SLAs while, of course, containing costs. Plus, to remain competitive, new solutions will require development, such as those that deliver value from customer payment data.
The global payments industry is being disrupted by digitalization. A confluence of trends in technology, business, global regulations, and consumer behavior is redefining how payment transactions are executed. The industry is witnessing rapid innovation growth across the value chain, not to mention disintermediation and fragmentation.
A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
Avez-vous besoin d'une solution simple pour mieux exploiter vos investissements dans les données SAP ? Avec Qlik, vous pouvez exploiter vos données SAP pour moderniser votre Data Warehouse ou votre Data Lake, réaliser l'intégration à votre Cloud, gérer les données de test et augmenter vos capacités d'analyse.
Sind Sie auf der Suche nach einer einfachen Lösung, mit der Sie Ihre SAP-Daten optimal nutzen können? Mit Qlik können Sie SAP-Daten zur Modernisierung Ihres Data Warehouses oder Data Lakes nutzen und in Ihre Cloud integrieren. SAP-Testdaten lassen sich einfacher managen und Ihre Analysemöglichkeiten werden erweitert.
Today’s enterprises rely on an assortment of platforms and environments, from on-premise systems to clouds, hybrid clouds and multi-clouds. This calls for modern data management practices that leverage emerging technologies, providing enterprise decision managers with the tools and insights they need to improve and transform their businesses. Download this special report for best practices in moving to modern data management standards to ensure the integration and governance of valuable data sources within today’s diverse environments.
Emerging agile technologies and techniques are leading to new ways of accessing and employing data. At the same time, the increasing complexity of these environments is creating additional challenges around security and governance, and orchestration and monitoring, which is particularly evident with the rise of hybrid, multi-cloud enterprise environments. Welcome to the era of the digitally enriched platform. Download this special report today to dive into emerging technologies and best practices.
AIOps market is set to be worth $11B by 2023 according to MarketsandMarkets. Originally started as automating the IT operations tasks, now AIOps has moved beyond the rudimentary RPA, event consolidation, noise reduction use cases into mainstream use cases such as root causes analysis, service ticket analytics, anomaly detection, demand forecasting, and capacity planning. Join this session with Andy Thurai, Chief Strategist at the Field CTO ( thefieldcto.com) to learn more about how AIOps solutions can help the digital business to run smoothly.
A challenge of ML is operationalizing the data volume, performance, and maintenance. In this session, Rashmi Gupta explains how to use tools for orchestration and version control to streamline datasets. She also discusses how to secure data to ensure that production control access is streamlined for testing.
As market conditions rapidly evolve, DataOps can help companies produce robust and accurate analytics to power the strategic decision-making needed to sustain a competitive advantage. Chris Bergh shares why, now more than ever, data teams need to focus on operations, not the next feature. He also provides practical tips on how to get your DataOps program up and running quickly today.
Traditional methodologies for handling data projects are too slow to handle the teams working with the technology. The DataOps Manifesto was created as a response, borrowing from the Agile Manifesto. This talk covers the principles of the DataOps Manifesto, the challenges that led to it, and how and where it's already being applied.
The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. However, it was not until recently that new technologies emerged to address the speed and scalability requirements of real-time analytics, both technically and cost-effectively. Attend this session to learn about the latest technologies and real-world strategies for success.
Each week, 275 million people shop at Walmart, generating interaction and transaction data. Learn how the company's customer backbone team enables extraction, transformation, and storage of customer data to be served to other teams. At 5 billion events per day, the Kafka Streams cluster processes events from various channels and maintains a uniform identity of each customer.
To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.
We are at the juncture of a major shift in how we represent and manage data in the enterprise. Conventional data management capabilities are ill equipped to handle the increasingly challenging data demands of the future. This is especially true when data elements are dispersed across multiple lines of business organizations or sourced from external sites containing unstructured content. Knowledge Graph Technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge Graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.
Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade. However, the scale, performance, and analytics capabilities of AnzoGraph DB, a graph database, is a key catalyst in Knowledge Graph adoption.
Though MongoDB is capable of incredible performance, it requires mastery of design to achieve such optimization. This presentation covers the practical approaches to optimization and configuration for the best performance. Padmesh Kankipati presents a brief overview of the new features in MongoDB, such as ACID transaction compliance, and then move on to application design best practices for indexing, aggregation, schema design, data distribution, data balancing, and query and RAID optimization. Other areas of focus include tips to implement fault-tolerant applications while managing data growth, practical recommendations for architectural considerations to achieve high performance on large volumes of data, and the best deployment configurations for MongoDB clusters on cloud platforms.
Just as in real estate, hybrid cloud performance is all about location. Data needs to be accessible from both on-premise and cloud-based applications. Since cloud vendors charge for data movement, customers need to understand and control that movement. Also, there may be performance or security implications around moving data to or from the cloud. This presentation covers these and other reasons that make it critical to consider the location of your data when using a hybrid cloud approach.
What if your business could take advantage of the most advanced AI platform without the huge upfront time and investment inherent in building an internal data scientist team? Google’s Ning looks at end-to-end solutions from ingest, process, store, analytics, and prediction with innovative cloud services. Knowing the options and criteria can really accelerate the organization's AI journey in a quicker time frame and without significant investment.
After 140+ years of acquiring, processing and managing data across multiple business units and multiple technology platforms, Prudential wanted to establish an enterprise wide data fabric architecture to allow data to be available where and when its needed. Prudential chose data virtualization technology to create the logical data fabric that spans their entire enterprise.
The pace of technology change is continuing to accelerate and organizations have no shortage of tool and application options. But while many are modernizing tool infrastructure and ripping out legacy systems, the data that powers new tools still presents difficult and seemingly intractable problems. Seth Earley discusses approaches for bridging the gap between a modernized application infrastructure and ensuring that quality data is available for that infrastructure.
As business models become more software driven, the challenge of maintaining reliable digital services and delightful customer experiences, as well as keeping those services and customer data safe is a "continuous" practice. It’s particularly important now, when the COVID-19 global pandemic has created a discontinuity in digital transformation and many industries have been forced entirely into a digital business model due to social distancing requirements. Bruno Kurtic discusses the impact of the pandemic on industries and digital enterprises leverage continuous intelligence to transform how they build, run, and secure their digital services and use continuous intelligence to outmaneuver their competition.
In this session, Lee Rainie discusses public attitudes about data, machine learning, privacy, and the role of technology companies in society—including in the midst of COVID-19 outbreak. He covers how these issues will be factors shaping the next stages of the analytics revolution as politicians, regulators, and civic actors start to focus their sights on data and its use.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, database professionals today are flush with new challenges and opportunities. Now, more than ever, enterprises need speed, scalability and flexibility to compete in today’s business landscape. At the same time, database environments continue to increase in size and complexity; crossing over relational and non-relational, transactional and analytical, and on-premises and cloud sites. Download this report to dive into key enabling technologies and evolving best practices today.
Hai bisogno di una soluzione semplice che ti aiuti a ottenere di più dai tuoi investimenti nei dati SAP? Con Qlik, puoi sfruttare i dati SAP per modernizzare il tuo data warehouse o data lake, eseguire l'integrazione con il cloud, gestire i dati dei test e migliorare le tue funzionalità di analytics.
With constantly evolving threats and an ever-increasing array of data privacy laws, understanding where your data is across the enterprise and properly safeguarding it is more important today than ever before. Download this year’s Cybersecurity Sourcebook to learn about the pitfalls to avoid and the key approaches and best practices to embrace when addressing data security, governance, and regulatory compliance.
Para adaptarse y tener éxito en el cambiante contexto digital actual, las empresas han adoptado nuevas arquitecturas de datos, incluidos los data lakes. Sin embargo, a pesar de la inversión, los conocimientos siguen sin llegar con la celeridad necesaria, ya que los procesos de integración tradicionales no pueden satisfacer la demanda.
Per adattarsi e avere successo nell'odierno panorama digitale in rapida evoluzione, le aziende hanno adottato nuove architetture di dati, compresi i data lake. Tuttavia, nonostante l'investimento, ancora le intuizioni non vengono rivelate abbastanza velocemente, perché i processi di integrazione tradizionali non riescono a soddisfare la domanda.
L'esplosione dei dati, la vasta gamma di nuove funzionalità e il drastico aumento delle richieste hanno cambiato il modo in cui i dati devono essere spostati, memorizzati, elaborati e analizzati. Tuttavia, nuove architetture come i data warehouse e i data lake stanno creando ulteriori colli di bottiglia all'interno dell'IT, poiché molti processi esistenti sono molto laboriosi e insufficienti.
Le moderne architetture cloud combinano tre elementi essenziali: la potenza del data warehousing, la flessibilità delle piattaforme di Big Data e l'elasticità del cloud a una frazione del costo per gli utenti delle soluzioni tradizionali.
La explosión de datos, el amplio abanico de nuevas capacidades y el espectacular aumento de la demanda han cambiado la forma en que los datos deben moverse, almacenarse, procesarse y analizarse. Sin embargo, nuevas arquitecturas como los data warehouses y los data lakes están creando nuevos cuellos de botella en los departamentos de TI, porque muchos procesos requieren mucho trabajo y son insuficientes.
Las arquitecturas modernas en la nube combinan tres elementos básicos: el poder del data warehousing, la flexibilidad de las plataformas de Big Data y la elasticidad de la nube por una fracción del precio de las soluciones tradicionales.
Immer mehr Daten werden in die Cloud ausgelagert, gleichzeitig wurden die Konzepte des Data Warehousing in den letzten Jahren modernisiert und praktisch neu erfunden. In der Folge haben sich Data Warehouses explosionsartig ausgebreitet. Snowflake-Anwendern bietet Qlik eine End-to-End-Plattform für die Datenintegration, die es ermöglicht, schneller Erkenntnisse zu gewinnen.
Um auf die rasanten Veränderungen der digitalen Welt zu reagieren und erfolgreich in ihr zu arbeiten, setzen Unternehmen auf neue Datenarchitekturen wie Data Lakes. Allerdings führen die Investitionen oft nicht zu den erwünschten schnelleren Erkenntnissen. Herkömmliche Integrationsprozesse sind den damit verbundenen Anforderungen einfach nicht gewachsen.
Moderne Cloud-Architekturen zeichnen sich durch drei Merkmale aus: die Leistungsfähigkeit eines Data Warehouse, die Flexibilität von Big-Data-Plattformen und die Elastizität der Cloud, zu einem Bruchteil der Kosten herkömmlicher Lösungen.
Immer mehr Daten, immer mehr Möglichkeiten und immer mehr Bedarf haben die Art und Weise, wie Daten bewegt, gespeichert, verarbeitet und analysiert werden von Grund auf verändert. Es gibt zwar viele neue Architekturen, doch Data Warehouses und Data Lakes führen oft zu zusätzlichen Engpässen in der IT, weil viele der bestehenden Prozesse arbeitsintensiv und unzureichend sind.
Plus que jamais, les données sont migrées vers le cloud, où le Data Warehouse s'est modernisé et réinventé. C'est pourquoi son adoption est en forte hausse. Pour les utilisateurs de Snowflake, Qlik propose une intégration de données de bout en bout qui accélère le temps d'analyse.
Pour s’adapter - et réussir - à l’évolution rapide du paysage numérique actuel, les entreprises ont adopté de nouvelles architectures de données, y compris les data lakes. Mais malgré l’investissement, les perspectives ne sont toujours pas assez rapides - parce que les processus d’intégration traditionnels ne peuvent tout simplement pas répondre à la demande.
Les architectures modernes dans le cloud allient trois éléments essentiels : la puissance du data warehouse, la flexibilité des plateformes de Big Data et l'élasticité du cloud, le tout à une fraction du prix des solutions traditionnelles.
L'explosion des données, l'immense choix de nouvelles fonctionnalités et l'augmentation spectaculaire des demandes ont modifié la manière dont nous devons déplacer, stocker, traiter et analyser ces données. Cependant, de nouvelles architectures, telles que les Data Warehouses et Data Lakes, donnent naissance à de nouveaux goulets d'étranglement au sein des services informatiques, car de nombreux processus existants s'avèrent insuffisants et nécessitent une main-d'œuvre importante.
¿Necesita una solución sencilla que le ayude a sacar más provecho de su inversión en datos de SAP? Con Qlik, puede utilizar los datos de SAP para modernizar su data warehouse o data lake, integrarlo todo en la nube, gestionar datos de pruebas y mejorar sus capacidades de analítica.
Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
Tungsten Clustering by Continuent is the only complete, fully-integrated, fully-tested MySQL High Availability, Disaster Recovery and Geo-clustering solution running on-premises and in the cloud combined with industry-best and fastest, 24/7 support for business-critical MySQL, MariaDB, & Percona Server applications. Learn more about it in this product guide.
Rapid data collection is creating a tsunami of information inside organizations, leaving data managers searching for the right tools to uncover insights. Knowledge graphs have emerged as a solution that can connect relevant data for specific business purposes. Download this special report to learn how knowledge graphs can act as the foundation of machine learning and AI analytics.
It’s no surprise then that adoption of data lakes continues to rise as data managers seek to develop ways to rapidly capture and store data from a multitude of sources in various formats. However, as the interest in data lakes continues to grow, so will the management challenges. Download this special report for guidelines to building data lakes that deliver the most value to enterprises.
Even though it’s still common practice today, the harsh reality is that using an external cache layer as the fundamental component of a System of Engagement (SoE), where huge scale, ultrafast response, and rock-solid reliability are critical success factors, is a last decade approach for solving next decade problems. Learn the five signs to watch for that show your cache-first data architecture needs an update.
DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.
Today’s enterprises are looking to data managers to be able to respond to business challenges with scalable and responsive systems that deliver both structured and unstructured data – and accompanying insights – at a moment’s notice, with the ability to respond to any and all queries. What’s needed is a modern data architecture that is built on flexible, modular technology, either from open source frameworks and software or through cloud services. Download this special report for the eight key ways to prepare for and manage a modern data architecture.
From modern data architecture and hybrid clouds, to data science and machine learning, the Data Sourcebook is your guide to the latest technologies and strategies in managing, governing, securing, integrating, governing and analyzing data today. Download your copy today to learn about the latest trends, innovative solutions and real-world insights from industry experts on pressing challenges and opportunities for IT leaders and practitioners.
As enterprise data warehouses evolve to become modern data warehouses in the cloud, they still hold a significant role for enterprise analytics as a vital component of an enterprise data analytics platform. The reality is that this evolution will be a hybrid-cloud architecture that requires shared and unified capabilities to represent both cloud and on-premises environments as a single data analytics platform for the business. A multi-cloud architecture will be likely for many companies as data gravity from more data sources, users, and applications shifts data processing among clouds, requiring open data architecture principles and furthering the need for enterprise data unification and governance.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, most database teams are struggling just to keep the lights on. While database environments continue to grow in size and complexity in conjunction with new business demands, the challenge of maintaining the performance and availability of business-critical systems and applications is growing in step. Download this report for key strategies and technologies to survive and thrive in today’s world of speed and scalability today.
Data science and machine learning are on the rise at insights-driven enterprises. However, surviving and thriving means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from infrastructure management, to data preparation and exploration, model training and deployment. In response, new solutions have emerged, along with the rise of DataOps, to address key needs in areas including self-service, real-time and visualization.
Until recently, clunkiness ruled the data systems and integration game. Expensive and complicated middleware was required to bring applications and information together, consisting of connectors, adapters, brokers, and other solutions to put all the pieces together. Now, cloud and containers – and Kubernetes orchestration technology – have made everyone’s jobs easier, and raised the possibility that both applications and data can be smoothly transferred to whatever location, platform, or environment best suits the needs of the enterprise. Download this special reports to learn the ins and outs of Containers, emerging best practices, and key solutions to common challenges.
From the rise of cloud computing, machine learning and automation, to the impact of growing real-time and self-service demands, the world of database management continues to evolve. Download this special report to stay on top of new technologies and best practices.
Data analytics is no longer the luxury of organizations with large budgets that can accommodate roving teams of analysts and data scientists. Every organization, no matter the size or industry, deserves a data analytics capability. Thanks to a convergence of technology and market forces, that’s exactly what’s happening. Download this special report to dive into the top technology trends in analytics today and why 2019 is becoming a year of transformation.
The pressure on companies to protect data continues to rise. In this year’s Cyber Security Sourcebook, industry experts shed light on the ways the data risk landscape is being reshaped by new threats and identify the proactive measures that organizations should take to safeguard their data. Download your copy today.
In a world where customers "crave self-service," having the technology in place to allow them to do this—and do it swiftly, efficiently, and correctly—is critical to satisfying customers.
Data warehouses are poised to play a leading role in next-generation initiatives, from AI and machine learning, to the Internet of Things. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to JSON data and automation. Download this special report for the top trends, emerging best practices and real-world success factors.
"Digital transformation can only bring value if it supports what the business is trying to achieve. Viewing information as a single entity, connected through technology, is crucial to positioning modern organizations to cope with the challenges they face is a rapidly changing business environment."
The ability for knowledge graphs to gather information, relationships, and insights – and connect those facts – allows organizations to discern context in data, which is important for extracting value as well as complying with increasingly stringent data privacy regulations. Download this special report to understand how knowledge graphs work and are becoming a key technology for enterprise AI initiatives.
Data lakes help address the greatest challenge for many enterprises today, which is overcoming disparate and siloed data sources, along with the bottlenecks and inertia they create within enterprises. This not only requires a change in architectural approach, but a change in thinking. Download this special best practices report for the top five steps to creating an effective data lake foundation.
With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. Data virtualization transcends the limitations of traditional data integration techniques such as ETL by delivering a simplified, unified, and integrated view of trusted business data.
Managing data environments that cross over from on-premises to public cloud sites requires different approaches and technologies than either traditional on-premises data environments or fully cloud-based services. Following the eight rules outlined in this special report will help data managers stay on track. Download today.
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.
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.
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.
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.
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.
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.
The world of data management in 2018 is diverse, complex and challenging. The industry is changing, the way that we work is changing, and the underlying technologies that we rely upon are changing. From systems of record, to systems of engagement, the desire to compete on analytics is leading more and more enterprises to invest in expanding their capabilities to collect, store and act upon data. At the same time, the challenge of maintaining the performance and availability of these systems is also growing. Download this special report to understand the impact of cloud and big data trends, emerging best practices, and the latest technologies paving the road ahead in the world of databases.
From automated fraud detection to intelligent chatbots, the use of knowledge graphs is on the rise as enterprises hunt for more effective ways to connect the dots between the data world and the business world. Download this special report to learn why knowledge graphs are becoming a foundational technology for empowering real-time insights, machine learning and the new generation of AI solutions.
Fast Data Solutions are essential to today’s businesses. From the ongoing need to respond to events in real time, to managing data from the Internet of Things and deploying machine learning and artificial intelligence capabilities, speed is the common factor that determines success or failure in meeting the opportunities and challenges of digital transformation. Download this special report to learn about the new generation of fast data technologies, emerging best practices, key use cases and real-world success stories.
Cognitive computing is such a tantalizing technology. It holds the promise of revolutionizing many aspects of both our professional and personal lives. From predicting movies we'd like to watch to delivering excellent customer service, cognitive computing combines artificial intelligence, machine learning, text analytics, and natural language processing to boost relevance and productivity.
GDPR is coming, and with it, a host of requirements that place additional demands on companies that collect customer data. Right now, organizations across the globe are scrambling to examine polices and processes, identify issues, and make the necessary adjustments to ensure compliance by May 25th. However, this looming deadline is just the beginning. GDPR will require an ongoing effort to change how data is collected, stored, and governed to ensure companies stay in compliance. Get your copy of the GDPR Playbook to learn about winning strategies and enabling technologies.
Today, more than ever, data analysis is viewed as the next frontier for innovation, competition and productivity. From data discovery and visualization, to data science and machine learning, the world of analytics has changed drastically from even a few years ago. The demand for real-time and self-service capabilities has skyrocketed, especially alongside the adoption of cloud and IoT applications that require serious speed, scalability and flexibility. At the same time, to deliver business value, analytics must deliver information that people can trust to act on, so balancing governance and security with agility has become a critical task at enterprises. Download this report to learn about the latest technology developments and best practices for succeeding with analytics today.
Data lake adoption is on the rise at enterprises supporting data discovery, data science and real-time operational analytics initiatives. Download this special report to learn about the current challenges and opportunities, latest technology developments, and emerging best practices. You’ll get the full scoop, from data integration, governance and security approaches, to the importance of native BI, data architecture and semantics. Get your copy today!
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.
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.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility, and the ability to innovate through better collaboration, visibility, and performance. However, as data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes.
Download this special report to gain a deeper understanding of the key technologies and strategies.
The Internet of Things represents not only tremendous volumes of data, but new data sources and types, as well as new applications and use cases. To harness its value, businesses need efficient ways to store, process, and ana¬lyze that data, delivering it where and when it is needed to inform decision-making and business automation. Download this special report to understand the current state of the marketplace and the key data management technologies and practices paving the way.
Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level where data is captured, stored, and processed. This transformation is being driven by the need for more agile data management practices in the face of increasing volumes and varieties of data and the growing challenge of delivering that data where and when it is needed. Download this special report to get a deeper understanding of the key technologies and best practices shaping the modern data architecture.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, the reality is most IT departments are struggling just to keep the lights on. A recent Unisphere Research study found that the amount of resources spent on ongoing database management activities is impacting productivity at two-thirds of organizations across North America. The number one culprit is database performance.
Since its early beginnings as a project aimed at building a better web search engine for Yahoo — inspired by Google’s now-well-known MapReduce paper — Hadoop has grown to occupy the center of the big data marketplace. Right now, 20% of Database Trends and Applications subscribers are currently using or deploying Hadoop, and another 22% plan to do so within the next 2 years. Alongside this momentum is a growing ecosystem of Hadoop-related solutions, from open source projects such as Spark, Hive, and Drill, to commercial products offered on-premises and in the cloud. These next-generation technologies are solving real-world big data challenges today, including real-time data processing, interactive analysis, information integration, data governance and data security. Download this special report to learn more about the current technologies, use cases and best practices that are ushering in the next era of data management and analysis.
The value of big data comes from its variety, but so, too, does its complexity. The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information. While companies are investing in initiatives to increase the amount of data at their disposal, most are spending more time finding the data they need than putting it to work. Download this special report to learn about the key developments and emerging strategies in data integration today.
When asked recently about their top reasons for adopting new technologies, the readers of Database Trends and Applications all agreed: supporting new analytical use cases, improving flexibility, and improving performance are on the short list. To compete in our global economy, businesses need to empower their users with faster access to actionable information and a better overall picture of their operations and opportunities. At the forefront of this journey to create value from data is in-memory processing. Download this special report to learn about the latest developments surrounding in-memory data management and analysis.
Download this special report to guide you through the current landscape of databases to understand the right solution for your needs.
From fraud detection to ad targeting, supply-chain optimization to campaign forecasting, the key use cases for big data require a successful analytics program. Businesses are investing heavily in initiatives that will increase the amount of data at their fingertips. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months according to a recent study from Unisphere Research. However, many businesses are spending more time finding needed data rather than analyzing it. To compete on analytics, the right mix of people, processes and technology needs to be in place to generate value. Download this special report to learn about the key technology solutions and strategies for succeeding with big data analytics today.
Today, the world of decision-making, along with the data sources and technologies that support it, is evolving rapidly. The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. A new data warehousing architecture is emerging, along with a new generation of technologies and best practices, to support the requirements of big data and the need for faster decision-making. To learn about the new technologies and strategies paving the way, download this special report today.
The “pie-in-the-sky” days of big data may be over, but the urgency for businesses to compete on analytics is stronger than ever. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months based on a recent study from Unisphere Research. The conversation around big data is shifting, from why to how. How can businesses harness the bits and bytes of data being captured inside and outside their enterprise to improve, empower and innovate? To learn about the key big data success stories today, download this special report.
The hottest term today—the “Data Lake”—is currently coming off the hype cycle and into the scrutiny of pragmatic IT and business stakeholders. As with all big concepts that have transformed the industry, from the early days of data warehousing and business intelligence, to the growth of cloud computing and big data, best practices are ultimately proven to deliver the benefits promised. To clarify the ambiguities surrounding the concept of the Data Lake, Unisphere Research and Database Trends and Applications combined forces with Radiant Advisors to publish a comprehensive report, “The Definitive Guide to the Data Lake.” By combining an analysis of fundamental information management principles with existing customer implementations of big data and analytics, this report explains how current data architectures will transform into modern data platforms. Download your copy today.
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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".