On average, a typical mid-size enterprise has hundreds of applications, databases, and unstructured file sources. A consumer-facing application may also have thousands to millions of end devices (IOTs). The data residing in such applications create ubiquitous data islands that do not capture a complete business view. To get a 360 idea of the business,
enterprises need to combine data from various sources into a harmonized structure. Often companies create data warehouses, data marts, etc., to integrate the applications/databases to get this complete picture. While these traditional approaches lead to incremental value for the business, they are too slow and lose relevance over time. While the need to harmonize data still exists, the techniques for traditional ETL are no longer adequate. Businesses need to look into modern methods of ingesting data in raw form and defining analysis techniques at run time. In other words, traditional techniques of ETL put too much burden on moving extensive data through the network while performing computations, thus slowing it down significantly. In the new world, data is ingested as is (ELT), and compute performed closer to data, thus resulting in faster analysis at scale.
Enterprises can break down these silos of data or applications by adopting new data integration techniques deployed on on-demand, unlimited compute on scalable cloud platforms.