Your organization likely collects large amounts of data in various systems such as databases, CRM systems, application servers, and so on. Accessing a

Data Integration Guide: Techniques, Technologies, and Tools

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2022-05-20 00:30:05

Your organization likely collects large amounts of data in various systems such as databases, CRM systems, application servers, and so on. Accessing and analyzing data that is spread across multiple systems can be a challenge. To address this challenge, data integration can be used to create a unified view of your organization's data.

If you agree that your organization could benefit from having a unified view of all of its data, here are some questions you will need to ask yourself to come up with a data integration strategy:

To help you answer these questions, in this article I cover the key data integration concepts that you will need to understand. I start out with a definition of data integration, including an overview of various approaches that can be used. This is followed by a discussion of the benefits of data integration into a unified destination versus alternative approaches. I then compare the two most popular data integration techniques: ETL (extract, transform, load) versus ELT (extract, load, transform). Next, I review various storage technologies that can be used for centralizing data. Finally, I assess the main tools that can be used for data integration.

At a high level, data integration is the process of combining data from disparate source systems into a single unified view. This can be accomplished via manual integration, data virtualization, application integration, or by moving data from multiple sources into a unified destination.