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True data-driven organizations seek to extract all the insight from all their data to optimize every aspect of their business and better serve their customers. They collect and analyze more and more data from traditional sources, such as ERP, CRM, and point of sale systems, as well as from newer data sources such as logs, web applications, Internet of things (IoT) devices and more.

However, that’s only possible with a single repository to easily and efficiently store all your data and make it useful. But it’s not possible or practical to load all data into a traditional data warehouse. Data from newer sources often arrives in semi-structured formats, which require further transformation and processing before loading. Further, the cost and complexity of storing large quantities of raw, unrefined data in a traditional data warehouse from an increasing number of sources would be prohibitive.

The data lake emerged more than a decade ago to solve this problem: How to create a scalable, low-cost data repository for storing raw data from a diverse set of sources to explore and refine that data. Then, move subsets of the refined data to other systems, including a data warehouse, to support high-performance analytics and reporting.