In today's data-driven world, the confluence of big data technologies with traditional and emerging data integration paradigms is shaping how organizations perceive, handle, and gain insights from their data. The terms "big data" and "data integration" often coexist but seldom are they considered in a complementary context. In this piece, let's delve into the symbiotic relationship between these two significant aspects of modern data management, focusing on how each amplifies the capabilities of the other. For an exhaustive exploration, you can check out the post here.
The Limitations of Traditional Data Integration in the Era of Big Data
Historically, data integration has been tackled through Extract, Transform, Load (ETL) or its younger sibling, Extract, Load, Transform (ELT) methodologies. These processes were mainly designed for on-premises databases, be it SQL or the early forms of NoSQL databases. But the entry of big data has altered the landscape. The 3V's of big data: Volume, Velocity, and Variety, throw up challenges that traditional data integration methods are ill-equipped to handle.