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Databricks Lakehouse

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description Databricks Lakehouse Overview

Databricks Lakehouse is a unified data management platform created by the original developers of Apache Spark. It merges the capabilities of data lakes and data warehouses, enabling organizations to store, process, and analyze structured and unstructured data in one environment. The platform is built heavily on open-source technologies like Delta Lake and MLflow, supporting data engineering, data warehousing, and machine learning workloads. It is designed for data scientists, engineers, and analysts collaborating on big data projects.

help Databricks Lakehouse FAQ

What makes the Databricks Lakehouse different from a traditional data warehouse?

The lakehouse keeps data in cloud object storage while adding warehouse-like reliability, governance, and SQL performance. Databricks commonly combines Apache Spark processing, Delta Lake tables, Photon execution, and Unity Catalog governance.

What role does Delta Lake play in Databricks?

Delta Lake adds a transaction log and ACID behavior to tables stored in a data lake. It supports operations such as schema enforcement, time travel, updates, and merges that are difficult to manage safely with loose Parquet files.

Can Databricks Lakehouse handle both batch and streaming data?

Yes. Apache Spark and Databricks pipelines can process historical batches and continuous streams using related APIs and Delta tables, which reduces the need for entirely separate processing stacks.

What does Unity Catalog govern in a Databricks workspace?

Unity Catalog provides centralized permissions, discovery, auditing, and lineage for data and AI assets. It can govern tables, files, functions, and machine-learning resources across multiple Databricks workspaces.

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