Data Integration tools are perhaps the most vital components to take advantage of Big Data. Enterprise organizations increasingly view Data Integration solutions as must-haves for assistance with data delivery, data quality, Master Data Management, data governance, and Business Intelligence and Data Analytics. With data volumes on the rise and with no real end in sight, businesses are leaning on integration tools more and more to meet all of the data consumption requirements for vital business applications. The migration, organization, and delivery of key organizational data assets is done in such a way that allow business teams to easily pull what they need for use within other business systems.
If you’re just beginning your search for a new Data Integration solution, knowing the different feature offerings each tool offers is important. Between Data Virtualization, ETL, Integration Platform as a Service, migration, and many others, it can be difficult to distinguish between what a potential integration platform does as its main focus. That’s where we come in, and in this post, we’ll pit Data Integration and Data Migration against one another. When asked, most industry experts will group the two terms together, but for those that are serious about turning data into actionable insights, it is important to differentiate the two. In an attempt to gain a clearer focus, let’s dig in.
Data Integration is a combination of technical and business processes used to combine different data from disparate sources in order to turn it into valuable business insight. This process generally supports the analytical processing of data by aligning, combining, and presenting each data store to an end-user, and is usually executed in a data warehouse via specialized integration software. ETL (extract, transform, load) is the most common form of Data Integration in practice, but other techniques including replication and virtualization can also help to move the needle in some scenarios.
Data Migration is a process where data is transferred between storage types, formats, data architectures and enterprise systems. Whereas Data Integration involves collecting data from sources outside of an organization for analysis, migration refers to the movement of data already stored internally to different systems. Companies will typically migrate data when implementing a new system or merging to a new environment. Migration techniques are often performed by a set of programs or automated scripts that automatically transfer data.
Data Integration and Data Migration differ in a number of ways. First, integrating data from many outside sources is a prerequisite for Data Analytics, as organizations look to provide their users with a single unified view of data. Migration on the other hand, is a process that is undertaken when new systems or storage mediums come into play and enterprise companies need to take all of their existing resources and move them into a different environment.
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