![]() ![]() There should be a foundational compute layer that supports all of the core lakehouse use cases including curating the data lake (ETL and stream processing), data science and machine learning, and SQL analytics on the data lake. A foundational compute layer built on open standards.The format of the curated data in the lake should be open, integrated with cloud native security services, and it should support ACID transactions. The data lake should be able to accommodate data of any type, size, and speed. A data lake to store all your data, with a curated layer in an open-source format.When building a lakehouse architecture, keep these 3 key principles and their associated components in mind: SQL APIs for BI and reporting along with declarative DataFrame APIs for data science and machine learning. ![]() Metadata, versioning, caching, and indexing to ensure manageability and performance when querying.ETL and stream processing with ACID transactions.Reliable, scalable, and low-cost storage in an open format.The lakehouse architecture provides several key features including: Lakehouses combine the low cost and flexibility of data lakes with the reliability and performance of data warehouses. One architecture pattern that addresses many of the challenges of traditional data architectures is the lakehouse architecture. These companies are looking beyond the limitations of traditional data architectures to enable cloud scale analytics, data science, and machine learning on all of this data. Today's companies are dealing with data of many different types, in many different sizes, and coming in at varying frequencies. ![]()
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