Data Integration Solutions You Need to Keep an Eye on in 2019

Extract, transform, load, or simply ETL, refers primarily to three database functions that are consolidated into a unified tool that extracts data from one database and loads it into another. We will record the details of the ETL process for another day, but it is usually used for data integration and storage.

In addition, the emergence of new cloud-based native solutions is driving the rapid transformation of technology, raising doubts about ETL's ability to survive this change without being archaic. The questions are certainly valid and ETL, like any other technology, must keep pace with the times and evolve to remain relevant in today's bitter environment.

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In addition, the most effective way to do this is to keep abreast of new trends in data integration as they are critical to the process and therefore able to decide on its future. In addition, why should companies retain an interest in this regard? Well, because the way things happen with ETL has a direct impact on their business and their ability to grow. Here are the main trends in data integration that you should keep an eye on in 2019.

Hybrid Integration Platform (HIP):

With more and more businesses scanning, it's important to remember that this can not be done without linking existing systems on-premise. To this end, hybrid integration platforms will enable companies to extend the life of their traditional technology investments. Thus, instead of removing outdated systems and investing considerable sums in modern systems, companies can add an integration layer to their old systems and then link them to new solutions.

Unified Data Management Structure:

UDM solutions enable organizations to assemble the most appropriate solutions for data lakes, data flows and data warehouses. As a result, organizations can improve the performance and reliability of their data warehouses in real-time. In addition, UDM systems also allow low latency of the streaming system. The best part is that all of this is achievable with the profitability and scale of a data lake.