Bringing together the best of both worlds with the lake house architecture

In today's highly digitized world, data about customers, products, operations, and the supply chain can come from many sources and can have a diverse set of structures. To gain deeper and more complete data-driven insights about a business topic (such as the customer journey, customer retention, product performance, and more), organizations need to analyze all the relevant topic data of all the structures from all the sources, together.

Organizations collect and analyze structured data in data warehouses, and they build data lakes to manage and analyze unstructured data. Historically, organizations have built data warehouse and data lake solutions in isolation from each other, with each having its own separate data ingestion, storage, processing, and governance layers. Often, these disjointed efforts to build separate data warehouse and data lake ecosystems have ended up creating data and processing...