Data integration vs data migration; What is the difference?
Data integration tools are perhaps the most essential components for leveraging big data. Companies increasingly see data integration solutions as indispensable tools to support data delivery, data quality, master data management, data governance, Business Intelligence and data analysis. With increasing data volumes and no real end in sight, organizations increasingly rely on integration tools to meet all the data consumption requirements for mission-critical business applications. The migration, organization, and delivery of key organizational data assets are done in a way that makes it easy for business teams to extract what they need for use in other business systems.
If you’re just starting your search for a new data integration solution, it’s important to know the different feature offerings each tool offers. Between data virtualization, ETL, Integration Platform as a Service, migration and many more, it can be difficult to distinguish between what a potential integration platform does as its main goal. That’s where we come in, and in this article we’ll pit data integration and data migration against each other. When asked, most industry experts will lump the two terms together, but for those who really want to turn data into actionable information, it’s important to differentiate the two. In an attempt to get a clearer focus, let’s dig.
Data integration is a combination of technical and business processes used to combine different data from disparate sources to turn it into valuable business information. This process typically supports analytical processing of data by aligning, combining, and presenting each data store to an end user, and is typically performed in a data warehouse through 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 help shake things up in some scenarios as well.
Data migration is a process by which data is transferred between storage types, formats, data architectures, and business systems. While data integration involves the collection of data from sources outside an organization for analysis, migration refers to the movement of data already stored internally to different systems. Organizations typically migrate data when implementing a new system or when merging to a new environment. Migration techniques are often performed by a set of automated programs or scripts that automatically transfer data.
Data integration and data migration differ in several ways. First, integrating data from many external sources is a prerequisite for data analysis, as organizations seek to provide their users with a single unified view of data. Migration, on the other hand, is a process that is undertaken when new storage systems or media come into play and businesses need to use all of their existing resources and move them to a different environment.