The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all data architecture, and not all platforms are created equal. Ongoing debates about the benefits and pitfalls of data lakes, warehouses, and other architecture have led many to shift toward hybrid models for managing their data.
Data lakes, once touted as the future of data architecture, have many limitations. For those who implemented data lakes, only to find that they do not optimize data management, the decision to redesign their organization’s entire data infrastructure can be daunting.
