Databricks is a cloud-based collaborative data science, data engineering, and data analytics platform that combines the best of data warehouses and data lakes into a lakehouse architecture.
These articles can help you understand the key concepts and features of the Databricks platform
Use the Databricks Lakehouse for ACID transactions, data governance, ETL, BI, and machine learning.
Learn fundamental Databricks concepts such as workspaces, data objects, clusters, machine learning models, and access.
Get a high-level overview of Databricks and its enterprise architecture.
- Language-specific overviews
Learn how to use Python, SQL, R, and Scala to perform collaborative data science, data engineering, and data analysis in Databricks.
- Supported clouds and regions
Learn about the cloud platforms and regions supported by Databricks.
- Databricks datasets
Databricks includes a variety of datasets mounted to the Databricks File System (DBFS) that you can use to test your queries and models. These datasets are used in examples throughout the documentation.
- Apache Spark
New to Apache Spark? Write your first Apache Spark application, create a DataFrame and Dataset, do some basic machine learning, and learn how to handle streaming data.
- Delta Lake
Learn about the Delta Lake storage layer and optimizations available with Delta Lake on Databricks.
Learn how to connect Databricks to data sources, BI tools, and developer tools.
Learn about Databricks support options.
- Free training
Try out our self-paced training, free to Databricks customers.