White Papers

A dental chain with dozens of dental practices was using the Google Sheets spreadsheet program for output, which lacked features such as visual reporting that could have enhanced their analytics capabilities.


The customer found that their on-premises infrastructure management and operational costs were too expensive. They wanted to move to AWS to achieve several goals.


SQL Server 2012 reached End of Support (EOS) on July 12, 2022, after being a mainstay for many organizations over the past decade. Continuing to run this SQL Server version, especially on missioncritical production systems, can put your company at risk of unexpected downtime, database security problems, and ongoing performance issues, as it will no longer receive security updates or bug fixes.


Novus Partners, the makers of a portfolio analytics and intelligence platform for institutional investors, operated its own datacenter for many years to deliver its Novus solution to capital allocators and managers throughout the world. But with steep licensing costs—at $400,000 per year—combined with an aging, 20-blade, on-premises database server cluster and a push to modernize the company’s technology stack, the Novus IT team sought a cloud-based alternative that delivered cost savings, scalability, performance, and manageability, and laid the groundwork for agile development. Novus worked with Datavail to migrate its commercial on-premises database to two instances of Amazon Relational Database Service (Amazon RDS) MySQL on Amazon Web Services (AWS), cutting costs by 50 percent and relieving developers from operations work so they could ship more product features than ever before.


Commercial RDBMS databases such as SQL Server and Oracle are not always the right fit for developing modern applications, as these legacy systems were originally designed around relational data models and monolithic architecture. While performing a lift and shift cloud migration of SQL Server or Oracle databases offers some benefits, it’s only the start of your cloud journey – modernization is next.


Premier Farnell maintains global order information centrally and makes it available on in-market, regional data stores. Customers worldwide want to see their up-to-the minute order history, so synchronization of central and regional data stores is essential.


This white paper discusses data modeling and its business value across the enterprise. It also discusses the most common use cases as well as real-world stories of organizations that have successfully implemented data modeling.


Data modeling is an essential aspect of application modernization. As organizations migrate data and applications to the cloud and other modern forms of infrastructure, they need data modeling solutions that can facilitate the overall modernization lifecycle. This paper reviews the top 10 considerations for choosing a data modeling solution, based on real user experiences with erwin® Data Modeler by Quest®. Taken from reviews on IT Central Station, it explores the importance of support for NoSQL, collaboration, visualization and version management, among many factors.


The digital world is moving faster than ever before, so the enterprise needs to keep pace by becoming data-driven.


Not long ago, most data was generated by systems and stored in databases as structured data. That data had steady, predictable growth and the only people interested in that data were IT and financial staff, who had the skills to work directly with the databases. Today, however, most content is created outside of databases and in a wide variety of formats. Some are structured but many are unstructured — documents, photos, videos. Add social and sensor data, and the growth rate becomes exponential.


Ever wish you could automate data preparation, so you could focus more on data analysis? In this complimentary e-book, you’ll learn how to make those frustrating, manual tasks a distant memory.


IDC explores the value of data intelligence to help organizations synthesize information, improve their capacity to learn, and automate insights at scale.


Organizations have a legal, ethical and financial responsibility when it comes to safeguarding sensitive data. Enterprises that make a strategic commitment to data governance can mitigate the risks associated with sensitive data while still realizing the benefit of data democratization.


Data Quality Is Data Governance's Most Confounding Challenge in 2022. According to an ESG research survey of 220 IT and business leaders on the state of data governance and DataOps.


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.


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.


Hybrid cloud infrastructure presents a complex management challenge. Without a straightforward way of tracking performance and cloud resource usage, organizations risk accruing unnecessary expenses.


As IT and business leaders strive to transform their organizations into data-driven enterprises, market forces and technology trends are reshaping the role of the DBA from fighting fires to navigating a new world of cloud and automation. In a recent DBTA survey, we asked more than 200 data management professionals what changes they are seeing in their roles, and what is needed to move their businesses forward in this new era.


Database developers and database administrators (DBAs) have been using open-source databases, such as MySQL and PostgreSQL, for years. The platforms are mature, offer flexibility with low license costs and have a huge community following. Plus, they help reduce your dependence on commercial databases, such as Oracle Database. What’s getting in the way of your using them more? This technical brief explores ways to use SharePlex® by Quest® to replicate your Oracle data to open-source databases. Data analysts and DBAs will see how SharePlex replicates data from Oracle in nearly real time to platforms like MySQL and PostgreSQL. The replication technology in SharePlex opens up your options for enjoying the maturity, flexibility and low cost of open source in your IT landscape.


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. Download your copy to learn: The three phases of database migrations How to navigate each phase How to use Quest products to reduce the risks to system performance and service levels


Databases (Oracle or otherwise) have long resided on-premises. For many reasons, organizations have largely relied on local infrastructure to host their Oracle databases. But in recent years, the opportunity to take advantage of the cloud for Oracle workloads has presented itself, allowing organizations to take advantage of the scalability, availability, processing power, and adjacent services the cloud has to offer. This book focuses mainly on Azure – Microsoft’s cloud platform – and discusses the why and how of moving your Oracle database there.


Emergency calls. Late nights. Struggling to get applications back up and running. It’s enough to make any DBA sweat. This infographic will help you relax. You’ll see just how far you are from being alone. And best of all, how you can easily ensure uptime.


Are you ready to take full advantage of modern data architectures? Getting started with modern data architecture often starts with moving some or all your data from on-premises to the cloud.


Are you incorporating data mesh and data fabric into your IT strategy and enterprise architecture? If not, you’re already behind two popular data engineering trends.


How confident are you in your data strategy? Currently, 94% of CEOs are pursuing a digital-first strategy — and cloud is an essential part of that strategy.


Your IT strategy and data architecture can be your competitive edge in today’s high-pressure, digital economy. Now, more than ever, it’s critical to upgrade your data architectures and exploit emerging cloud technologies.


Let’s face it. CIOs are legends. Magicians even. They're pivotal to accelerating data-driven business transformation, enabling teams to improve CX and edging out the competition.


As data pipelines grow in size and complexity, DataOps continues to gain a bigger foothold in enterprises hungry for fast, actionable insights. DataOps brings people, processes, and technology together to orchestrate the effective, efficient, and secure flow of data. To do that, organizations must have in place key components in all three areas. Download this special report for a better understanding of the key technologies, strategies, and use cases that are unlocking the power of DataOps at enterprises today.


As data becomes crucial for business success and regulatory compliance, companies strive to boost their data quality. This whitepaper explores machine learning as a tool to improve data quality. Read to find out more about its potential uses.


The Internet of Things can be thought of as a network of interconnected sensors and processes that integrate with a system of models derived through AI/ML. This system of models allows for decision and action at machine speeds in controlling devices, and systems of devices. Business value is realized at the moment that an event (or meaningful combination of events) is recognized and acted upon. So what is required to handle the data capture and decision-making in real-time when it makes the most difference? Download this special white paper to learn about key requirements and considerations for building streaming data architectures.


Software license compliance mistakes can quickly cost millions of dollars. Therefore, SAM software is a crucial component of proactively managing your software deployment. This guide will provide insight and practical tips for purchasing a software asset management solution and the criteria to consider when hiring a software license consultant. We will also discuss a new, more efficient, hybrid approach to dealing with the challenges of software license compliance called the SAM Managed Service.


How can you build a data culture and strategy that will unlock your data’s full potential? Financial experts from Immuta and Databricks lead a discussion on: Why data sharing in financial services is becoming more complicated as consumer expectations and regulatory requirements grow Having strong data culture, and why it’s a key driver of a data strategy How to execute on purposeful data strategies faster and more securely with Immuta and Databricks


Quest Toad Data Point is recognized as a leader by analyst Quadrant Knowledge Solutions in its 2022 Data Preparation Tools Spark Matrix assessment. More than 15 software offerings were evaluated for companies searching for leading data preparation tools.


This e-book discusses harmonizing IT-oriented data management with business-led data governance to fuel an automated, high-quality data pipeline. That yields faster time to data preparation, data visibility and data-driven insights.


With a view into the data governance, data management and DataOps efforts within large enterprises across North America, the 2022 State of Data Governance and Empowerment report offers benchmark data you can use to compare to your organization.


This e-book explores the evolution of data governance, marked by the idea that everyone within the organization collaborates in the process to inform and enable the effort's return on investment.


Data visualization, dashboards and predictive data science are only as good as the data you start with.


Data-driven enterprises do not exist on data on their own; they require an advanced, responsive data architecture to gain ground within their markets. Download this special report for best practices of leaders in data-driven architecture who have established high-producing data architectures.


TODAY’S ENTERPRISES are more distributed than ever before—both in terms of employees working across different geographical locations and the dispersal of data across different departments, applications, databases, on-prem data centers, and clouds. This expansion of enterprise data landscapes offers opportunities and challenges for IT leaders and data management professionals. Succeeding in this increasingly distributed, complex world requires rethinking traditional approaches to data architecture and key data management processes around integration, governance, and security. Download this special report for key considerations to achieving real data democratization.


10 Things to Consider Before Choosing a Data Observability Platform You’re ready to experience the power of data observability. Awesome! So, what’s next? In this guide, we’ll share the 10 most important things to consider when choosing a data observability platform. We’ll talk about: The fundamentals of data observability How to choose a data observability platform that will scale with your business And what features will speed up time to value for data quality at your company Stop fighting bad data. Download our platform guide, and take the next step in your journey to data trust.


Treat data like a product, not an afterthought. A new paradigm has emerged among forward-thinking data teams: treat your data like a product. This may sound easy in theory, but it’s far from it! Product development processes have become incredibly sophisticated, with resources dedicated to reliability, adoption, feature roadmaps, and more. While challenging, data products also present a massive opportunity for data teams to increase trust, measure value, and improve capacity. We’ll cover: Creating SLAs Assigning ownership Documentation, discovery, and self-service Governance and compliance Developing data contracts Certifying data sets Assessing and demonstrating value with KPIs DataOps and agile methodologies External Data Products And more! Our guide will take you beyond the buzz and into the best practices deployed by some of today’s leading data professionals. Get your free copy today.


For decades, the lack of visibility into the health of our data has led to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail. This is the only guide of its kind to help data engineers and analysts understand the key factors that contribute to poor data quality and how to detect, resolve, and prevent these issues at scale. Access your copy to learn: Why data quality deserves attention and what exactly is the concept of data downtime. How data engineers and analysts can architect more reliable data ecosystems, from ingestion in the warehouse or lake to the analytics layer downstream. What it takes to identify, alert for, resolve, and prevent data quality issues in a holistic and end-to-end way across your stack.


When users need access to customer data, you need a secure and automated way of managing this access. Satori helps you apply the least privilege principle on customer data with data security automation. That way you can reduce risks and reach your security & compliance goals effortlessly.


Data pipelines are crucial in optimizing processes for organizations, but most have yet to fully maximize the first step in the pipeline chain; the data.


This Checklist Report examines the six most popular and frequently used solution categories that are enabled by a logical data fabric architecture.


Real-world data (RWD) is no longer a buzzword or something from the future. With the 21st Century Cures act putting RWD in focus, and FDA guidance issued in January 2022 on its use in regulatory decision making, all functions within healthcare and life sciences need to understand the what, how, and when to use RWD for drug discovery, clinical development, and commercial use cases.


The database world has been changing dramatically over the past decade, and the pace of change has been accelerating in recent years. Ensuring a smooth-running, high-performing database environment today means rethinking how data flows, where it flows, and who oversees the flow. Download this special report to learn about the new technologies and strategies for managing database performance in the modern enterprise.


Data is one of the most valuable resources for an organization. Yet, it’s difficult to extract intelligence from it because data management and app development are traditionally centered around applications. The result is data silos, lack of control, and numerous integrations.


Currently, the world of work is going through one of the biggest changes we have seen on a global scale with the shift to remote and hybrid working. As we try to work our way through this ‘new normal’, businesses have a tough task in trying to optimize teams - however, there is a source of information that currently might not be utilized to its full potential, third-party data.


Aligning customer experience efforts with the associated business value can be a significant challenge. In IDC’s recent Future of Customer Experience Survey, they highlight some of the key areas of ROI that organizations can expect from customer experience improvements and how to showcase them to your stakeholders.


Keeping pace with today’s dynamic business environment requires advanced analytics and a way to deliver consistent, multi-channel customer experiences.


There’s never been a greater need for speed and agility. Are legacy applications and processes holding you back? See why new cloud means new speeds.


Choosing the right integration platform is key to embracing hyperautomation and digital transformation. It’s critical to consider your organization’s goals, pain points and resource constraints when evaluating integration platform-as-a-service (iPaaS) solutions.


Hyperautomation earned a top mention in the Gartner “Top Strategic Technology Trends for 2022” report. But, what is hyperautomation — and how can it empower you to tackle complex business processes and thrive in today’s digital-first economy?


The need to leverage data—at unprecedented scale and complexity, with unprecedented speed and accuracy—has led businesses to adopt cloud technologies, solutions, and applications, usually alongside existing on-premises infrastructures. The resulting complexity challenges IT’s ability to achieve the core goal of delivering trusted, actionable data when and how the business needs it.


Managing data in the cloud, but seeing the opposite of what you expected? Slow performance – and cost overruns? Cloud data management provides flexibility both in terms of resources and budget. But it can be hard to get right.


If you’re like nearly 80% of organizations, you rely on a variety of cloud platforms to store your data and provide critical business functions.


As organizations consolidate and modernize their on-premises data warehouses and data lakes in the cloud – or stand up new ones in the cloud, it’s more important than ever to avoid the pitfalls of hand coding.


Organizations of all types are turning to intelligent, automated cloud-native data management to deliver cloud analytics that accelerate insights and drive innovation.


Data is commonly called “the food for artificial intelligence (AI)”. But in order to extract meaningful insights for faster and better decision-making, both structured and unstructured data must be used to achieve success.


Today’s successful data-driven digital transformations require a modern approach to integrating complex data ecosystems. The right approach to data integration allows you to manage your data as a valuable, strategic asset. A modern solution for data integration enables you to orchestrate, unify, govern, and share your data.


In the move to become a digital-first organization, putting data first is non-negotiable. In order to maximize the value of your data, you must focus on how data is captured, curated and presented – and that it’s available for fueling actionable business insights.


Data sharing unlocks tremendous potential for an enhanced customer experience. Organizations are now realizing how they can achieve this, but it must be done with the right governance foundations.


It’s no secret that to fuel data-driven decision-making your business needs exceptional insights. To create them, the right people need fast, easy access to trusted, quality data.


Aligning data & analytics governance to business outcomes has become increasingly difficult with the volume of data that businesses now manage. It is now essential to have an evolving strategy around your organization’s data and analytics governance as well as its role as a business capability.


In today’s digital economy, your company’s success (or failure!) could come down to data. More specifically, the ability for you to easily and proactively share data with every relevant person.


Data is a foundational asset for your organization. A recent IDC survey found that 63% of organizations who invested in data intelligence technology reported improved business outcomes, with customer satisfaction being the most improved, followed closely by industry innovation and time to market.


“Master data management (MDM) is a technology-enabled business discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, governance, semantic consistency, and accountability of an enterprise’s official shared master data assets.” The Gartner “December 2021 Critical Capabilities for Master Data Management Solutions” evaluates MDM vendors technical capabilities and their ability to support five business use cases.


We are thrilled to be named a 2022 Gartner Peer Insights Customers’ Choice for Data Integration Tools. Per Gartner, “Vendors placed in the upper-right quadrant of the ‘Voice of the Customer’ quadrants are recognized with the Gartner Peer Insights Customers’ Choice distinction, denoted with a Customers’ Choice badge.” The recognized vendors meet or exceed both the market average Overall Rating and the market average User Interest and Adoption”. We are the ONLY vendor to receive it four times in a row!


In today’s digital economy, you need the speed and flexibility of a SaaS solution to deliver trusted, connected, and contextual master data.


To tie all of your systems and information together into a single source of trusted data, you need a master data management (MDM) strategy that combines scale and robust functionality with speed and flexibility. A cloud-native MDM solution enables you to obtain MDM as a service (SaaS), which allows greater focus on driving business value from master data.


Whether you want to sharpen the focus of marketing campaigns or identify opportunities to streamline business processes, Master Data Management (MDM) delivers the 360-degree view of data that makes it possible. But what’s the best way to build the right MDM solution for your organization?


Scalability, performance, flexibility, and cost-effectiveness are just a few benefits organizations experience when modernizing to cloud data platforms. However, moving on-premises workloads to the cloud requires meticulous planning and execution to accelerate time to value and reduce cost, time and risk.


Cloud computing now fuels the way your organization manages data lakes and warehouses with ETL tools, allowing for enhanced business intelligence and advanced analytics. As you modernize data management tools by migrating from PowerCenter to the cloud, there are existing on-prem ETL mappings and data pipeline investments that can’t be ignored.


A global automaker had various legacy data challenges impeding its digital transformation. They sought a new approach to managing and analyzing data for better customer insights, SCM and forecasting.


As you modernize your data warehouse, your data integration and management strategy should align with your cloud-first strategy.


Download Nucleus Research’s report, Reducing Costs with Cloud Modernization, to prepare your company for cloud modernization success.


Amazon RDS simplifies setting up, operating, and scaling databases in the cloud. IDC conducted in-depth research on Amazon RDS’s value, finding it delivered 86% faster new database deployments, 97% less unplanned downtime, and a five-month average investment payback period. Explore how Amazon RDS improves businesses’ agility, scalability, and performance while lowering ownership costs.


With data-driven initiatives central to modern success, it’s incredibly important that this data is kept from falling into the wrong hands. Access control models achieve this goal by limiting who is allowed to see what data and for which reasons. In today’s era of cloud computing and storage, access control must be able to evolve while still maintaining full security. In this white paper, we’ll explore: The evolution of access control models over time The successes and struggles of role-based access control (RBAC) The future-proof success of attribute-based access control (ABAC) A head-to-head comparison of RBAC and ABAC


Regardless of size, geography, or industry, organizations are all facing a similar challenge: how to use data effectively without compromising security or customers’ trust. Our conversations and experiences with data leaders, practitioners, and users, as well as governance, risk, and compliance stakeholders, have identified six core use cases with a single common denominator – the need for data security and access control. In this eBook, you’ll learn: The top six use cases for modern data teams How real customers achieved their data use goals with automated, dynamic access control 4 best practices for building an agile data stack that will unlock more data for more use cases


As privacy regulations increase, and Google Analytics is increasingly blocked, companies are turning to alternatives for web and mobile analytics. In this guide, we detail 8 different alternatives to Google Analytics including strengths, weaknesses, and use cases.


The cloud is outpacing on-premises for databases and Amazon Aurora is a leading cloud-native, MySQL and PostgreSQL compatible relational database service. This IDC report explores the value and benefits for organizations using Amazon Aurora to support their business goals and database transformation efforts.


The future of databases is in the cloud, but achieving higher levels of growth and agility can be hampered by persistent myths. Oracle Corporation, which offers its own proprietary cloud platform, has promoted fear, uncertainty, and doubt about the viability of running Oracle databases on robust, competitive cloud platforms such as Amazon Web Services (AWS). As a result, it is understandable when some IT leaders and database teams hesitate to migrate their Oracle databases. This paper explores and debunks the leading myths that inhibit organizations from migrating, and the realities of how they benefit from moving databases and applications to more flexible and scalable cloud environments.


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


This paper examines eight main questions at the intersection of database administration and data privacy. Data controllers — and database administrators (DBAs) working with data controllers — can use the following guidelines to evaluate the tools they’ll use to comply with data protection regulations such as GDPR.


Whether you are new to Toad for Oracle or have been using it for several years, there are several features that you should be familiar with for achieving maximum productivity. This white paper will cover key Toad fundamentals, and then break down the key features you should know. Download it today.


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.


To examine how database environments and roles are changing within enterprises – as well as how deeply new modes of collaboration and technology are being adopted – Unisphere Research recently conducted a survey of DBTA subscribers in partnership with Quest. From cloud and automation to the rise of "Ops" culture, the world of database management is evolving with new challenges and opportunities emerging for IT leaders and database professionals. Download this special study for a first-hand look at the results and learn where database management is heading.


The challenge for multi-cloud and hybrid environments is to live up to their promise of enabling organizations to be more flexible and agile without the overhead incurred from system complexity. Data management needs to achieve this as well. Developing a well-functioning, hybrid, multi-cloud management strategy requires a number of considerations. Download this report today to dive into key strategies.


Five customers fuel their growth with an open data stack for real-time apps Most business leaders recognize that data is a key asset with enormous potential for accelerating growth. Yet many still struggle with data silos that hamper their ability to develop high-growth applications to drive their business forward. It doesn't have to be that way. Learn how companies in five different industries are breaking down their silos to unify and activate their data in real time with Astra DB, a multi-cloud DBaaS built on Apache Cassandra™: Technology Transportation and Logistics Media and Entertainment/Gaming Financial Services/Insurance Retail and eCommerce Astra DB is helping organizations in almost every industry harness the power of their data to support business growth. Read this ebook to learn more.


As enterprises look to win new customers and accelerate growth, they need a technology stack that scales with no limits. The risk behind digital transformation is that if it’s not done right, it can lead to more data complexity and silos — rather than the open flow of data and the fast time-to-value laid out in your data strategies. Read this eBook, “The CIO’s Guide to Shattering Data Silos,” to learn how standardizing on an open data ecosystem can help: Deliver breakthrough digital experiences that bring more value to your data Mobilize real-time data so you can act quickly, provide faster insights, improve customers’ experiences, and find new revenue Build high-scale, high-impact applications to become a true data-driven business


New survey: Real-time data = revenue growth Great news! According to a newly published survey of 500+ CIOs and technology leaders, leveraging real-time data pays off in two important ways: higher revenue growth and increased developer productivity. Read The State of the Data Race 2022 research report to learn: Real-time data = revenue growth. 71% of respondents agree that they can tie their revenue growth directly to real-time data Real-time data is a game changer for data leaders. 42% of organizations that make real-time data a strategic focus say that building capabilities in this area has had a transformative impact on revenue growth Real-time data helps developers build efficiently. 66% of organizations that make real-time data a strategic imperative say that developer productivity has improved because of this focus


Data rule #1: Don’t get locked in with one cloud provider Based on a global survey conducted by Forrester, 86% of enterprises have adopted a multi-cloud strategy due to shifting business priorities. Organizations seek to optimize the costs of running and managing data in the cloud while simultaneously enabling developer velocity to quickly build the modern, intelligent applications of tomorrow. Read this white paper, “Why Multi-Cloud is Imperative to Modern Data Strategy” to learn about the benefits of a multi-cloud strategy and why it’s a top priority for today’s IT decision makers, including: The freedom of choice to host applications and data anywhere — on any cloud, in any global region The most robust approach for fast-growing, cloud-native applications — with real-time capabilities, infinite scalability, and no downtime No single cloud provider lock-in — 73% of the enterprises consider multi-cloud to avoid it Competitive pricing — 65% of businesses consider reduced pr


New from 451 Research: Find out why enterprises want multi-cloud and serverless for data Where will your enterprise be running your database systems in two years? According to new findings from 451 Research, organizations are overwhelmingly choosing cloud – but they don’t want to get locked into one vendor. View this infographic “Top considerations when selecting a database management system" to learn: The top five reasons companies plan to move to a cloud-based data platform Why almost 80% say those platforms need to support multiple clouds How serverless data processing provides what enterprises need to fuel growth


How Endowus delivers real-time data at scale Digital disruptors are proving that a cloud-native data strategy delivers faster business results and competitive advantage. Their cloud platforms can scale without compromise to easily support growing volumes of data, users, applications, and transactions. Read this IDC publication ‘In Conversation’ by Clay Miller, Senior Executive Advisor at IDC, titled: “Leveraging Data and Cloud for Accelerated Business Results," to learn how a DataStax wealthtech customer, Endowus: Creates an ecosystem for growth by embracing multi-cloud, microservices, and containers to maximize developer productivity Builds an open data platform with a cloud-first approach to deliver breakthrough experiences, innovation, scale, and cost-effectiveness Leverages a modern database technology to manage the volume and velocity of data at speed, and to enable faster time to insight


As data environments continue to grow in size and complexity, spanning on-premises and cloud sites, effective data integration and governance processes are paramount. Download this special report for the latest trends and strategies for success.


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 cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users. But which solution is the right one for you and your business? Download the eBook to see a side-by-side comparison.


Do you need an easy solution to help you get more out of your SAP data investments? With Qlik, you can leverage your SAP data to modernize your data warehouse or lake, integrate with your cloud, manage test data, and boost your analytics capabilities.


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.


For years, there was a standard order of operations for data delivery: ETL, or extract, transform, and load. But things have changed.


The explosion in data, the vast array of new capabilities, and the dramatic increase in demands have changed how data needs to be moved, stored, processed and analyzed. But new architectures like data warehouses and lakes are creating additional bottlenecks within IT, because many existing processes are labor-intensive and insufficient.


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™.


As you make the decision to move your data warehouse from on-premise to the cloud or cloud to cloud, there are many things to take into consideration. Find out how a cloud data warehouse in Azure has advantages in cost, time to value, and the ability to work with real-time data across the organization for analytics.


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.


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.


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.


In today’s fast-moving marketplace, real-time decisions are essential – and they have to be informed by data. That requires Active Intelligence, or the ability to take immediate action based on up-to-the-minute data.


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.


In this whitepaper, Eckerson Group discusses how to get maximum value from data lakes and how Qlik’s Data Integration Platform helps businesses get the most value out of their data lakes quickly, accurately, and with the agility to respond to shifting business needs.


Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end data integration solution that delivers rapid time-to-insight.


To adapt – and succeed – in today’s rapidly evolving digital landscape, enterprises have adopted new data architectures, including data lakes. But despite the investment, the insights still aren’t coming quickly enough – because traditional integration processes just can’t meet the demand.


For database managers and users, moving to the cloud means breaking through the confines imposed by capacity ceilings, speed limits, performance issues, and security concerns. At the same time, moving to the cloud does not mean outsourcing control or responsibility for data to an outside provider, and care must be taken to ensure migrations take place with as little disruption to the business as possible. In addition, organizations need to be prepared with the specific skills required to migrate to and manage databases in the cloud. Download this white paper for the questions you need to ask before undertaking a cloud migration.


This Analyst Brief lists the top 16 Key Value NoSQL Databases Ranked by Customer Satisfaction Score. Find out why Aerospike is voted number one.


Dream11 is India’s largest fantasy sports platform that allows users to play fantasy cricket, hockey, football, kabaddi and basketball. Aerospike runs as a cache in AWS in conjunction with RDS for real-time leaderboards and contest management handling 2.7 million concurrent users. Dream11 moved from Elasticache Redis to Aerospike for improved availability, cost, latency and elasticity. Aerospike is deployed with Spark and Kafka to manage traffic spikes across geos using microservices across seven clusters – all on AWS.


Consulting on behalf of Aerospike, customer analytics is the engine of a customer-centric, insights-driven business. And as the pace of business accelerates and real-time insights become a critical component to growth, new tech organizations must turn to platforms that can deliver analytics in real-time to support modern customer experience initiatives.


During this 45 minute BrightTALK Summit presentation Stuart Tarmy, Global Director of Financial Services Industry, Aerospike will be discussing how our customers are addressing the key challenges present in leveraging AI effectively in Financial Services.


Barclays processes circa 30 million-plus payment transactions a day for its 20 million-plus customers and had many fraud detection solutions in place across its various business units. Barclays knew transaction fraud detection required ultra low latency, but not having the ability to seamlessly re-use its large-scale user profile datasets across use cases across its business units resulted in multiple complex, bespoke engineering solutions. These solutions became increasingly difficult to both maintain and evolve, and thus posed a significant limitation to achieving the company’s strategy.


To thrive in today’s highly competitive digital environment, financial services companies will need to modernize their data infrastructure, connecting and streamlining information flow across exploding arrays of data sources and datasets. This will power their customer-facing front office applications, risk mitigation and trading analysis, conduct faster and more cost-effective settlements, payments and adhere to compliance regulations.


Join us for an interactive live conversation with Ram Kumar Rengaswamy, VP Engineering, FreeWheel, a Comcast Company, Gerry Louw, Head of Worldwide Solutions Architectire, AWS and Srini Srinivasan, Founder and Chief Product Officer, Aerospike. Learn how Freewheel used a real-time data platform to predictably scale their business running Aerospike on AWS.


It's time to make sense of the modern data stack. Read this guide to learn what a practical implementation of modern data tooling looks like along four phases: Starter, Growth, Machine Learning, & Real-Time. The 80-page resource delivers data stack architectures and tactical advice to help you progress towards data maturity. It will help you define where you are today and build on your existing toolset to take the next step on your journey.


Read this eBook to learn the four steps of building a modern data architecture that’s cost-effective, secure, and future proof. In addition, this eBook address the most common challenges of integrating mainframe data: Data structure Metadata Data mapping Different storage formats


Data masking is essential when working with sensitive data. It is a crucial part of maintaining a secure data environment and avoiding data breaches. While, data masking projects are seemingly simple, they are actually quite tricky. These projects are full of pitfalls and “yes, but” conditions, which aren’t flexible and dynamic enough to keep up with the changing data environment but are unfortunately common experiences of data team members. This document will cover some of the reasons why data teams struggle with data masking projects.


Deploying today’s flexible technology services and components— containers, microservices, and cloud-based infrastructure—may bring measures of improved agility to IT and data management operations, but translating this into business agility is what makes these technologies impactful. Here’s where an agile technology architecture demonstrates its true value. Download this special report for key capabilities and best practices for success.


To keep your MySQL environment running at peak performance, you need granular real-time information about database performance and availability. Automated alerts, change tracking, compliance reporting and centralized management are also critical, especially in highly distributed environments.


The datapocalypse is upon us. At the same time data volumes are growing at an exponential rate, business stakeholders are demanding expanded access to data for daily decision-making. Data is taking a central role as enterprises seek to harvest its maximum value, protect it from threats and remain compliant with data privacy regulations.


Explore iPaaS Customers’ Payback Period, 3-Yr ROI, TCO & More.


Even the most complex integrations should be quick and easy. It’s time for next-gen iPaaS.


Qlik Data Integration enables seamless Mainframe data movement to Microsoft Azure Synapse, Azure HDInsight, and/or Azure Event Hubs in real-time. Our Replicate solution extracts data from 40+ sources, including Db2 for z/OS, IMS, and VSAM as well as mid-range IBM Db2 for I (DB2/400; iSeries; AS/400) and IBM DB2 for LUW in real-time through our CDC technology.


If you’re like nearly 80% of organizations, you rely on a variety of cloud platforms to store your data and provide critical business functions.


Is your organization hindered by data silos and a misaligned data strategy? If so, you’re not alone. According to a recent survey of 300 data leaders like you, companies are juggling data across multiple solutions while their analytics are dispersed across various groups. Further, many report that their data strategy isn’t aligned with the business strategy.


According to the "Gartner Sixth Annual Chief Data Officer Survey," "top data and analytics leaders are either leading (24%) or are heavily involved (48%) in digital transformation."


Looking for an enterprise data catalog to automate data discovery, curation and lineage? Forrester’s 2022 evaluation of enterprise data catalog providers can help.


When your success depends on high-quality, trusted data to deliver enterprise value, you can rely on our 14-time position as a Leader in 2021 Gartner® Magic Quadrant™ for Data Quality Solutions.


Achieving enterprise-wide data governance is a huge opportunity, so it makes sense to break that journey down into more manageable steps.


Just as pilots run through a pre-flight checklist before they ever leave the ground, taking steps to increase your understanding of and confidence in your data in the cloud is key to better data-driven decisions that send your business initiatives soaring.


How Data Integration Boosts a Competitive Edge in a Multi-Cloud Ecosystem If you’re like nearly 80% of organizations,1 you rely on a variety of cloud platforms to store your data and provide critical business functions.


You need trusted, real-time data and analytics to fuel your business growth — and you need it fast.


O’Reilly’s Cloud Native Go provides developers with a comprehensive overview of the Go programming language and its unique properties that make it optimal for building, deploying, and managing services distributed across ten, one hundred, or even a thousand servers. Download the free 3-chapter excerpt, courtesy of Cockroach Labs, for hands-on guidance to start building resilient, cost-effective, cloud-native applications today.


Database management today is flush with new challenges and opportunities. More than ever before, businesses today desire speed, scalability, and flexibility from their data infrastructure. At the same time, the complexity of data environments continues to grow – spread across different database types and brands, applications, and on-premise and cloud sites. This makes new technologies and strategies critical. Download this special report today for key practices to managing databases in this highly diverse, emerging landscape.


Each year, Cockroach Labs publishes the industry’s only independent performance analysis of a variety of instance types across AWS, GCP, and Azure — to help you find the best options for your workloads.Beyond benchmarks alone, the report goes inside the numbers to offer practical advice you can apply to the applications you’re building. Download your free copy of Cockroach Labs’ 2022 Cloud Report now.


Analytics on the warehouse unlock insights and analysis that aren't possible in third party tools. Your organization can make better decisions, faster when you use your data warehouse as a central source of truth to fuel the downstream tools. In this guide, you’ll learn how to put together the infrastructure required to enable richer, more comprehensive analytics on top of your warehouse.


Organizations demand real-time analytics in order to make the best decisions for their business. They want data streamed from data sources such as IoT devices, online transactions and log files, which can only be gathered using modern data architecture that supports real-time ingestion, allows faster work speeds and enables the freshest data to be shared among multiple teams. That’s where Quest SharePlex comes in. In this tech brief, we discuss how organizations can use Quest SharePlex to efficiently replicate data from Oracle databases into Kafka for real-time streaming.


Worried about your next big database migration or upgrade? You’re not alone. Whether it’s planned or unplanned, downtime is out of the question. You need to ensure high availability to keep your business up and running. Without that, you lose productivity and profits. So it may feel safer to put off your next big database project. But holding off on switching to faster, more affordable databases puts you at a competitive disadvantage. To meet your business goals, you need modern databases that maximize data value and cut costs. What if you could run your mission-critical apps during migrations? This means you could meet SLAs while future-proofing your database environment. In this three-part e-book, you’ll learn how to: Move data safely and easily – without interrupting business. Avoid risk, downtime and long hours. Select the best tools for your next database migration or upgrade.


Sharing workloads between your data center and the cloud to form hybrid cloud and multi-cloud environments firms your IT resilience and business continuity. However, it also comes with a thorny technical question: How do you keep them in sync? You’re only as IT-resilient as the lag between production data and its replica, so true IT resilience depends on high speed and low fault tolerance as you replicate between databases. This technical brief describes ways to use SharePlex® by Quest® to support your hybrid and multi-cloud strategies. Using examples from some SharePlex customers, it illustrates replication techniques that keep Oracle data synchronized in nearly real time so that you can apply those techniques in your own hybrid and multi-cloud scenarios.


Has Oracle 19c Standard Edition 2 (SE2) taken a step backward in high availability (HA) and disaster recovery (DR)? It appears so. If you plan to stick with Standard Edition 2, take a second look at how you will handle server failures and avoid downtime in your environment. Oracle 19c will make you re-think your options for achieving the high availability and disaster recovery you’ve become accustomed to. 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.


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. Business requirements around uptime and continuity are ultimately what create pressure and stress around upgrades and migrations. And, while it’s reasonably easy to predict downtime associated with the known steps in upgrading, there’s always the threat of unplanned downtime associated with unexpected problems. This paper explores the various drivers and challenges associated with upgrades and migrations and presents proven approaches used by SharePlex customers to mitigate the risks and flawlessly upgrade without impact to the business.


Meeting the demands of the rapid evolution to real-time business requires new perspectives and approaches. Download this report for seven recommendations to make the most of real-time capabilities today.


Integration is essential to cloud modernization. Your enterprise integration platform as a service (iPaaS) needs to be up to the challenge. Informatica is a Leader again in the 2021 Gartner® Magic Quadrant™ for Enterprise iPaaS. Download the report to see why.


When you choose an integration Platform as a Service (iPaaS), it’s important to consider your overall benefits and ROI. How long will it take you to achieve payback on your investment? What will your three-year ROI be? Where will you reap the most time and cost savings?


Hyperautomation and iPaaS: Automate key business processes Hyperautomation earned a top mention in the Gartner “Top Strategic Technology Trends for 2022” report.1 But, what is hyperautomation — and how can it empower you to tackle complex business processes and thrive in today’s digital-first economy?


The need to leverage data—at unprecedented scale and complexity, with unprecedented speed and accuracy—has led businesses to adopt cloud technologies, solutions, and applications, usually alongside existing on-premises infrastructures. The resulting complexity challenges IT’s ability to achieve the core goal of delivering trusted, actionable data when and how the business needs it.


There’s never been a greater need for speed and agility. Are legacy applications and processes holding you back? See why new cloud means new speeds.


he “Voice of the Customer” synthesizes Gartner Peer Insights’ reviews into insights for IT decision makers. This aggregated peer perspective, along with the individual detailed reviews, is complementary to Gartner expert research and can play a key role in your buying process, as it focuses on direct peer experiences of implementing and operating a solution.


Looking for faster ROI from cloud data integration initiatives? Download TDWI’s Checklist Report, “Six Ways to Accelerate ROI from Data Warehouse and Data Lake Modernization,” now!


By 2025, more than 75% of the midsize, large, and global organizations will establish integration strategy empowerment teams to support collaborative integration, up from 40% in 2021.


This ESG Technical Review documents the detailed evaluation of Quest QoreStor with Veeam. ESG evaluated how the Quest QoreStor solution provides easy installation and management, cyber resiliency, and enhanced performance.


This paper explains how you can significantly lower cloud object storage requirements and cost for data protection. With the right practices and technology in place, you can take advantage of object storage in the cloud to lower the overall cost of data protection.


Multiple backup solutions, clouds, and cloud storage tiers can all lead to escalating cloud storage costs and complexity. These, in turn, hamper an organization’s ability to perform a DR in the cloud. Three new best practices exist that account for these new variables. By adopting them, organizations better position themselves to more quickly perform DR in the cloud while incurring lower costs.


Cloud storage provides organizations with a simple and effective means to store their backup data off-premises. However, as they do so, they should strive to keep their cloud storage management practices as simple as possible. To achieve these ends, use backup solutions that interface with and manage multiple cloud storage offerings, such as Quest QoreStor. These solutions equip organization to use a single cloud storage provider or multiple. They may then freely move between cloud providers at any time so they can adapt should their business or technical needs ever change.


Download a free copy of O’Reilly’s CockroachDB: The Definitive Guide. Whether building from scratch or rearchitecting an existing app, modern distributed applications need a distributed database. This essential reference guide to CockroachDB — the world’s most evolved distributed SQL database — shows how to architect apps for effortless scale, bulletproof resilience, and low-latency performance for users anywhere.


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.


So what makes a good API? The same thing that makes products outstanding – design.


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.


We’ve compiled a range of expert outlooks on the state of data in an effort to understand what the near future may hold. From new technologies to emerging skill sets and beyond, the ideas collected in this Trendbook point towards a world centered around the importance of data. In this Trendbook, you’ll explore: How evolving technologies and tools are critical for properly leveraging data Why democratizing access is more important than ever How data teams are being diversified and upskilled to keep pace Actions being taken to derive efficient, critical insights from data


Traditionally, organizations depended on monolithic architectures that initially served them well, but today’s on-demand business environment calls for a model that can support a more flexible, microservices-driven approach, and facilitate the pace of innovation. This eBook covers the evolution of data management and application environments, contributing factors to modern application requirements, common customer challenges with traditional database technologies, and presents the value of document databases and Amazon DocumentDB. We will also highlight Amazon DocumentDB customer use cases, target workloads, new features, migration programs, and key resources to get started.


Many organizations’ data assets are hidden away in silos or proprietary applications, which can take great amounts of time and resources to locate. This is made more complicated as the amount of data flowing through, into, and out of enterprises keeps growing exponentially. Data catalogs can enable self-service analytics and data lake modernization, as well as support data governance, privacy, and security initiatives. Download this special report, sponsored by Sandhill Consultants, to dive into best practices for harnessing the power of data catalogs in the enterprise today.


Despite greater awareness of threats, protecting data has not become easier in recent years. Data is being created at a rapid clip and there are more ways than ever before to store it. Understanding today’s data security and governance problems is the first step to solving them. Download this special report for 10 key tenets for improving data security and governance in today’s complex data environments.


More data is being collected, stored, transformed, analyzed, and acted upon than ever before. The explosion in data generation has led to growth in the number of database platforms and instances deployed across organizations of all sizes. Many IT organizations and database professionals struggle to manage this, resulting in database operations not optimized for performance, agility, security, or cost. Download this report to learn how organizations are turning to solutions like Nutanix Era on HPE GreenLake to solve these challenges by creating cloud-like agility and enabling business process automation to achieve faster time to value.


There is one clear direction data management has been going in as of late – to the cloud. The availability of cloud resources provides new options for building and leveraging enterprise-scale data environments. Support for hybrid and multi-cloud data warehousing is becoming mainstream, edge analytics adoption is rising, and the demand for real-time analytics capabilities is soaring. As more organizations invest in data warehouse and data lake modernization, these areas are also converging with concepts such as the “data lakehouse” and the “data mesh.” Download this special report to navigate the growing constellation of technologies and strategies emerging in modern data management and analytics today.


Data governance often conjures up the idea of some central authority instituting a “culture of no” but in reality it can be a powerful engine to scale the use and distribution of trusted data pipelines. Your organization’s data governance should be able to meet complex requirements, as well as enable your team to develop and deliver trusted data to the right users in the right format at the right time. With data governance in place, you can confidently ensure data privacy, proactively comply with regulations, and allow easy collaboration with data professionals in every function. Read this whitepaper to get your Data Governance Strategy started.


Based on a global survey conducted by Forrester, 86% of enterprises have adopted a multi-cloud strategy due to shifting business priorities. Organizations need to optimize the costs of running and managing data in the cloud while simultaneously enabling developer velocity to efficiently build the modern, intelligent applications of tomorrow. This white paper details the benefits of a multi-cloud strategy and explains how to simplify deployment of your cloud database. Learn the top four reasons to consider multi-cloud: Freedom of choice — host applications and data anywhere, on any device, any cloud, and at global scale No single cloud provider lock-in — 73% of the enterprises consider multi-cloud for that reason Higher (ROI) — 65% of the enterprises consider competitive pricing as a compelling reason to make a strategic shift to multi-cloud Reduced downtime risk


Monolithic databases like SQL can’t handle the demands of today’s data-intensive workloads. They are too slow, too unscalable, and too prone to failure. But there is a path forward. Modern, open NoSQL databases like Apache® Cassandra™ are designed for the data demands of customer-engagement apps and streaming IoT. Read this white paper on the best approaches for data modernization, and learn: The opportunities coming from turning business logic trapped in a monolithic database into lightweight, independent microservices. Critical components for a successful transformation, including a scalable and open data API layer SQL-to-Cassandra migration methodology and the value of testing and validating query patterns


The data management landscape can be very confusing, with many database vendors offering quite distinct technologies. This IDC brief provides you with a template to help define your workloads and identify combinations of database technologies that support extreme scalability, high performance, and flexible operations.  Read this report by IDC’s Analyst Carl Olofson, “Combining Scalable Database Technologies to Achieve Operational Flexibility and Analytic Power,” and learn: How to achieve synergy among several distinct data management technologies Differences between database workload types: operational, streaming, and analytical Trends impacting the data management landscape, such as streaming data and AI The growth of cloud-native DBMSs and benefits Read the IDC brief now.


At a time when enterprises are seeking to leverage greater automation and intelligence, many are becoming acquainted with the advantages of using knowledge graphs to power real-time insights and machine learning. In fact, Gartner predicts that, by 2023, graph technology will play a role in the decision-making process for 30% of organizations worldwide. Download this special report to understand how knowledge graphs are accelerating analytics and AI in the enterprise today.


Watch this webinar with Incorta, Microsoft, & DBTA to learn how you can build a modern data architecture with a complete, end-to-end unified data analytics platform.


Why are organizations adopting SQL data lakehouses? The SQL data lake house is a type of cloud data architecture that queries object stores at high speed and provides access to data across multiple sources to support both business intelligence (BI) and data science workloads. Enterprises adopt the SQL data lakehouse to streamline their architectures, reduce cost, and simplify data governance. Common use cases include reporting and dashboards, ad-hoc queries, 360-degree customer views, and artificial intelligence/machine learning (AI/ML).


For most enterprises, information is a competitive weapon. Organizations increasingly compete on the effectiveness of their information systems to make better business decisions. As a result, data analytics has become a strategic imperative. Data engineers play a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics. What are their most important challenges and how can they solve them? Read this white paper to discover strategic solutions to data engineering challenges for increased effectiveness of analytic systems.


Download a free copy of the latest O'Reilly Ebook on Distributed SQL Databases, to learn what Distributed SQL means and how it helps support massive, global applications. In this report you will also learn what kinds of companies benefit from using Distributed SQL and what the future holds for Distributed SQL databases.


There’s no turning back from cloud as an enterprise data platform, and adoption continues to expand rapidly. The question is not whether to pursue a cloud strategy, but how to do so to meet your organization’s business requirements most efficiently and cost-effectively. Download this special report to gain a deeper understanding of emerging best practices, key enabling technologies, challenges, and solutions in the evolving world of cloud and data management.


Customers are increasingly migrating from legacy on-premises databases to purpose-built cloud databases because of the performance, scalability, availability, and cost benefits that cloud databases provide. In Gartner vendor evaluation covering cloud database management systems (CDBMS), AWS is named a Leader positioned highest in Ability to Execute among the 16 vendors evaluated. Read the report to discover why Gartner positioned AWS as a Leader and take a deep dive into the benefits of AWS purpose-built databases for modern applications.


Enterprise IT is undergoing massive transformation led by technologies that enable the virtualization, dynamic deployment, and elastic scalability of resources. Coupled with professional management services in the cloud, these technologies offer more control and cost-effective management of IT systems than has ever been seen before. This is particularly important in the database sphere. Without cloud managed database services, databases are managed manually through fixed compute and storage resources acquired for fixed periods and maintained by the datacenter staff.


According to Gartner, by 2025, 75% of enterprise-generated data will be created and processed outside of a traditional centralized data center or cloud. This means data will come from everywhere, with applications—and users—geographically distributed. Is your enterprise prepared for this new data environment? Edge computing distributes computation and data storage closer to where the data is produced and consumed. New edge applications exploit devices and network gateways to perform tasks and provide services locally on behalf of the cloud—with the most powerful edge applications being stateful. However, stateful edge applications demand a new data architecture that considers the scale, latency, availability, and security needs of applications. In this white paper, we explore the issues, challenges, and opportunities created when you build stateful applications at the edge. YOU WILL DISCOVER: The key considerations when designing a data architecture for edge applications H


Achieving data pipeline observability in complex data environments is becoming more and more challenging and is forcing businesses to allocate extra resources and spend hundreds of person-hours carrying out manual impact analyses before making any changes. Challenge this status quo and learn how to enable DataOps in your environment, ensure automated monitoring and testing, and make sure that your teams are not wasting their precious time on tedious manual tasks. This session will help you: Uncover your data blind spots Define validation and reconciliation rules across your on-premises and cloud platforms Deliver pipeline visibility to all data users so they know exactly which areas demand immediate attention Monitor data conditioning over time to ensure data accuracy and trustworthiness Carry out automated impact analyses to prevent data incidents and accelerate application migration


One of MANTA’s customers, a global Fortune 500 financial services company, was falling behind on BASEL II/III compliance. The manual reporting that was being done in Excel spreadsheets couldn’t keep up with the mounting compliance requirements of the regulation that requires financial institutions to prove they meet the minimum capital and reporting requirements with the goal of minimizing credit risks. The customer initially had around 250 such metrics to meet, and the number eventually rose to 500. Manually collecting information about their data turned out to be inefficient and error-prone, and it led to numerous decisions that did not reflect the actual state of the data. In order to address these issues, the customer tried to implement several data governance and data catalog solutions. However, due to their limited lineage capabilities, they were not able to answer the most crucial questions. What are the sources of the data that is being used to generate riskbased metrics? Ho


Cloud transformation radically changes how an organization operates, how it budgets, and how it competes. And while being very disruptive,migration needs to be carefully controlled and closely observed in order to succeed.


Today’s enterprises are equipped with hundreds of specialized tools and technologies to enable data experts and business intelligence operators to collect, integrate, and analyze record volumes of data. So why does the divide between data creators and data consumers continue to widen? Why does it feel like the more we can do with our data, the less we understand what should be done with it? The answer is that we are being challenged by the complexity that all these integrations, tools, and processes have introduced to the data environment, wreaking havoc on our ability to effectively manage data pipelines and trust the data in our reports.


Data-driven organizations are adopting augmented data management to deal with complexity, keep the ability to innovate, and iterate quickly. CTOs and CIOs of successful companies are facing one major challenge: the skyrocketing complexity of their data stack (data pipelines). Combined with a shortage of engineering talent, it limits their ability to cope with the fast pace of changes, negatively impacts innovation initiatives, increases the risk of data incidents, causes reputation issues, and leads to non-compliance with regulatory requirements. “Active metadata” and “data lineage” are key cornerstones of any augmented data management initiative. While data lineage started as a simple way to describe the “journey of data”, it has now evolved and become the main tool for organizations to map, understand, and gain insights into their data pipelines. The Ultimate Guide to Data Lineage in 2022 presents a vast array of data lineage perspectives and outlines several approaches to its


It’s not news that effective metadata management is essential for organizations that want to be nimble and agile in today’s fast-moving business climate. However, what may not be as well known is the role that automated active metadata plays in effective metadata management.


DataOps helps to improve processes throughout the data lifecycle – from initial collection and creation to delivery to the end user, but implementing the methodology requires effort. Download this special report to learn the ten key tenets for a successful DataOps implementation.


Cloud native application architectures let you make highly available, massively scalable, globally distributed applications. This O’Reilly technical guide for architects and developers details the most commonly used cloud native design patterns. You will learn how to build cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.


Kroger, America’s largest supermarket chain with over 2800 retail stores, has experienced tremendous growth in digital sales and e-commerce. In this video, Sriram Samu, VP of Engineering for Customer Technology at Kroger, describes the company’s journey and lessons learned from using YugabyteDB—the open source, high-performance distributed SQL database for transactional applications—to power business-critical microservices used by millions of customers across the United States.


Data-driven decision-making and the ability to drive meaningful insights from increasing volumes of data is no longer just a competitive advantage: it’s a requirement for business leaders. However, as the volume and complexity of data grows, data teams still struggle to manage data migration and maintenance, causing new and growing information gaps as well as burnout across the teams. In our ebook, find out what we heard from 450 data professionals just like you. In this ebook, you’ll learn: Key trends and pain points facing enterprise data teams How to spot blind spots in your data as volumes continue to increase and how to turn those gaps into knowledge The top three challenges of working with different data sources Why introducing the right technology that can integrate and manage data at scale is the key to winning market, mindshare, and talent


Now, more than ever, businesses want scalable, agile data management processes that can enable faster time to market, greater self-service capabilities, and more streamlined internal processes. Download this report for seven key steps to designing and promoting a modern data architecture that meet today’s business requirements.


With all that’s happened in the past 2 years, it is often observed that there may be more risk in staying with the status quo than moving forward and trying something new. Today, agility, enabled by modern methodologies, such as DataOps and DevOps, and the use of new technologies, like AI and machine learning, is critical for addressing new challenges and flexibly pivoting to embrace new opportunities. As we get further into 2022, the annual Data Sourcebook issue puts the current data scene in perspective and looks under the covers of the key trends in data management and analytics. Download your copy today.


In keeping up with the demands of a digital economy, organizations struggle with availability, scalability, and security. For users of the world’s most popular enterprise database, Microsoft SQL Server, this means evolving to deliver information in a hybrid, multi-platform world. While the data platform has long been associated with the Windows Server operating system, many instances can now be found running within Linux environments. The increasing movement toward cloud, which supports greater containerization and virtualization, is opening platform independence in ways not previously seen, and enterprises are benefitting with greater flexibility and lower costs. Download this special white paper today to learn about the era of the amplified SQL Server environment supported by the capabilities of Linux and the cloud.


From the rise of hybrid and multi-cloud architectures to the impact of machine learning, automation, and containerization, database management today is rife with new opportunities and challenges. Download this special report today for the top 9 strategies to overcome performance issues.


In a hybrid, multi-cloud world, data management must evolve from traditional, singular approaches to more adaptable approaches. This applies to the tools and platforms that are being employed to support data management initiatives – particularly the infrastructure-as-a-service, platform-as-a-service, and SaaS offerings that now dominate the data management landscape. Download this special report today for new solutions and strategies to survive and thrive in the evolving hybrid, multi-cloud world.


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.


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.


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.


Leading Ad Tech companies use the Aerospike non-relational NoSQL database to improve customer engagement, campaign effectiveness and top-line results.


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.


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.


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.


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.


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.


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.


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.


Los data warehouses en la nube son un elemento fundamental de la transformación digital porque no requieren ningún tipo de hardware, son infinitamente escalables y solo se pagan los recursos de datos que se consumen. Sin embargo, eso no es todo, hay más, y la cosa se complica. Azure Synapse, Amazon Redshift, Google Big Query y Snowflake, todos ellos, requieren integración de datos en tiempo real y automatización del ciclo de vida para alcanzar su máximo potencial. El problema es que estas dos capacidades no están incluidas, por lo que es necesario codificar a mano las secuencias de comandos de ETL para compensarlo. En consecuencia, los desarrolladores se ven limitados y las transferencias de datos, restringidas; lo que afecta negativamente a su retorno de inversión inicial.


Les data warehouses dans le cloud sont au cœur de la transformation digitale, car ils ne nécessitent aucun hardware, sont infiniment redimensionnables, et parce que vous payez uniquement pour les ressources de données que vous consommez. Mais ce n'est pas tout. Les systèmes tels qu'Azure Synapse, Amazon Redshift, Google BigQuery et Snowflake exigent tous une intégration des données en temps réel et une automatisation du cycle de vie pour atteindre leur plein potentiel. Pour autant, ces deux fonctionnalités ne sont pas incluses dans ces systèmes, ce qui oblige à coder manuellement des scripts ETL pour y pallier. Par conséquent, vos développeurs sont soumis à des contraintes et vos transferts de données sont restreints, ce qui s'avère compromettant pour votre ROI initial.


Cloud Data Warehouses sind das Rückgrat der Digitalisierung, denn sie benötigen keine Hardware und sind unendlich skalierbar. Außerdem brauchen Sie nur für die Ressourcen zu zahlen, die Sie tatsächlich nutzen. Doch das ist nur die halbe Wahrheit. Datenintegration und eine Automatisierung des gesamten Daten-Lebenszyklus, damit Sie ihre Stärken voll ausspielen können. Allerdings gehören diese beiden Möglichen bei keinem dieser Tool zum Funktionsumfang. Es bleibt Ihnen daher nichts Anderes übrig, als die Lücken mit selbstgeschriebenen ETL-Skripts zu schließen, was Ihre Entwickler und Datentransfers stark einschränkt und wodurch der anfänglich avisierte ROI in weite Ferne rückt.


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.


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.


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.


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.


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.


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.


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.


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.


As organizations are more likely than ever to be audited by their software vendor, one of the top questions we are asked is, “How at risk is my organization in the event of an Oracle audit?” In this eBook, you will be able to quantify your organization’s Oracle audit risk through traditional risk calculating practices in a risk matrix.


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.


While cloud is seen as the go-to environment for modernizing IT strategies and managing ever-increasing volumes of data, it also presents a bewildering array of options. Download this special report for the nine points to consider in preparing for the hybrid and multi-cloud world.


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.


This book will discuss the ins and outs of Oracle’s licensing web, clarifying the murky points. We’ll also go in-depth on the petrifying and dreaded “Oracle Audit,” providing clear advice on how to prepare for it; advice that includes calling in the cavalry when needed, to protect you from Oracle’s clutches.


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