What is a data lake house architecture?
What are the layers of a data lakehouse?
A data lakehouse is a relatively new data management architecture that combines the benefits of data lakes and data warehouses. The layers of a data lakehouse are designed to provide a robust, scalable, and flexible framework for storing, processing, and analyzing large volumes of data.
Key Components of a Data Lakehouse
The key components or layers of a data lakehouse typically include:
- a storage layer that provides a centralized repository for storing raw, unprocessed data in its native format,
- a metadata layer that manages metadata associated with the data, such as data definitions, schema, and lineage,
- a processing layer that enables data processing, transformation, and analysis through various engines and frameworks.
These layers work together to enable organizations to store, process, and analyze their data in a flexible, scalable, and cost-effective manner. The storage layer is often built on top of a cloud-based object store, while the metadata layer provides a unified view of the data, making it easier to discover, understand, and trust the data.
What are the five key lakehouse elements?
What is the difference between data lakehouse and data lake?
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