Out of many data integration applications, the most important one in today’s IT world is making the stored data accessible on various systems. It can be well understood via an example – Hadoop, a modern big data analytics environment isn’t compatible with mainframe data. To bridge the gap, data integration can prove to be a good solution. This makes the legacy data of an organization available which can be used with business intelligence applications.
How to accomplish data integration?
Manual, as well as automated approaches, are primarily used to accomplish data integration. Today, many data integration solutions utilize a few forms of the ETL (extract, transform, load) methodology. As the name suggests, this methodology helps by getting the data from the host environment and then converting it into a standardized format. Thereafter, it loads the same into a destination system to be utilized by applications that are running on that system.
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