![]() ![]() This simplified data infrastructure solves several challenges that are inherent to the two-tier architecture mentioned above: Why might a business use a data lakehouse?Ĭombining data lakes and data warehouses into data lakehouses allows data teams to operate swiftly because they no longer need to access multiple systems to use the data. Consumption layer: The business tools and applications that leverage the data stored within the data lake for analytics, BI, and AI purposes.API layer: Metadata APIs allow users to understand what data is required for a particular use case and how to retrieve it.The metadata layer is the defining element of the data lakehouse. ![]() This enables data indexing, quality enforcement, and ACID transactions, among other features. Metadata layer: A unified catalog that provides metadata about all objects in the data lake.Storage layer: Various types of data (structured, semi-structured, and unstructured) are kept in a cost-effective object store, such as Amazon S3.Ingestion layer: Data is pulled from different sources and delivered to the storage layer.To address the data storage aspect, a relatively new open source standard called Delta Lake brings the essential functionality of a data warehouse, such as structured tables, into a data lake.ĭata lakehouse architecture is made up of 5 layers: Pioneered by Databricks, the data lake house is different from other data cloud solutions because the data lake is at the center of everything, not the data warehouse. Benefitting from the cost-effective storage of the data lake, the organization will eventually ETL certain portions of the data into a data warehouse for analytics purposes.Ī data lakehouse, however, allows businesses to use the data management features of a warehouse within an open format data lake. As a result, these organizations typically leverage a two-tier architecture in which data is extracted, transformed, and loaded (ETL) from an operational database into a data lake. When businesses use both data warehouses and data lakes - without lakehouses - they must use different processes to capture data from operational systems and move this information into the desired storage tier. What are the components of data lakehouse architecture? A data lakehouse is an emerging system design that combines the data structures and management features from a data warehouse with the low-cost storage of a data lake. ![]()
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