Data management featuresĪ data warehouse typically offers data management features such as data cleansing, ETL, and schema enforcement. A data lakehouse offers many pieces that are familiar from historical data lake and data warehouse concepts, but in a way that merges them into something new and more effective for today’s digital world. With an understanding of a data lakehouse’s general concept, let’s look a little deeper at the specific elements involved. In a way, data lakehouses are data warehouses-which conceptually originated in the early 1980s-rebooted for our modern data-driven world. By providing the space to collect from curated data sources while using tools and features that prepare the data for business use, a data lakehouse accelerates processes. The result creates a data repository that integrates the affordable, unstructured collection of data lakes and the robust preparedness of a data warehouse. This means data can be easily moved between the low-cost and flexible storage of a data lake over to a data warehouse and vice versa, providing easy access to a data warehouse’s management tools for implementing schema and governance, often powered by machine learning and artificial intelligence for data cleansing. So, how does a data lakehouse combine these two ideas? In general, a data lakehouse removes the silo walls between a data lake and a data warehouse. A data warehouse typically includes data management features such as data cleansing and extract/load/transform (ETL). This data is typically queried by business users, who use the prepared data in analytics tools for reporting and projections. Data warehouse (the “house” in lakehouse): A data warehouse is a different kind of storage repository from a data lake in that a data warehouse stores processed and structured data, curated for a specific purpose, and stored in a specified format.
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