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The Comprehensive Handbook of Data Integration

This free e-book will give you an exhaustive overview of all data integration approaches.

The Comprehensive Handbook of Data Integration

Download your free copy now

The evolution of data integration nicely mirrors the different kinds of challenges organizations face when turning data into insights. Starting from the question of how to move the data from a data source into a central repository, data integration is now trying to answer complex strategic topics that reflect many aspects of the entire data architecture such as data governance, the democratization of data, and the enablement of business users.

This e-Book examines the different data integration approaches that have evolved over the past 30+ years. You will learn about the various benefits and challenges that each of these approaches brings and how or if these approaches can help you overcome your data management challenges incl. Data Governance, breaking data silos, and real-time connection to meet the current business objectives.

This eBook is most helpful for Data Architects, Enterprise Architects, BI Managers, Data Warehouse Practitioners, Project Managers.

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Simplify Your Decision-Making Process

Step 1: Understanding the approaches

Learn about the different modern data integration methods and understand the pros and cons of them incl. the different data management capabilities like data governance.

Step 2: Setting objectives and goals

Understand the needs and requirements of the different stakeholders. Based on that, set your goals and priorities for your data integration project.

Step 3: Evaluating an approach

Make an informed decision on which approach to evaluate based on the findings from step 1 and step 2.

"Data Virtuality is so easy to use that even our business analysts can use it and get the data whenever they need it. Before Data Virtuality only our developers could use the tool and the business analysts had to wait. Now that the developers as well as business analysts can use the solution, we are not only more efficient but were able to cut cost by 80%!"
Fred Dunant
DMO, Crédit Agricole Consumer Finance

Highlights From Our Resources

Data Architectures for Data Science Using Data Virtualization

In this whitepaper, Rick van der Lans explains how a modern data architecture can help data scientists work more efficiently.

Best Practices for Hybrid- and Multi-Cloud Architectures

This whitepaper describes best practices for orchestrating Hybrid- and Multi-Cloud architectures and how Data Virtuality can enable these.

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