![]() ![]() Many data sources use real-time streaming directly from devices. The ability to separate compute from storage resources makes it easy to scale storage as necessary. Using open and standardized storage formats means that data from curated data sources have a significant head start in being able to work together and be ready for analytics or reporting. These are brought into a data lakehouse as a means of rapidly preparing data, allowing data from curated sources to naturally work together and be prepared for further analytics and business intelligence (BI) tools. 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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