Databricks Data Science & Engineering is the classic Databricks environment for collaboration among data scientists, data engineers, and data analysts. It also forms the backbone of the Databricks Machine Learning environment.
If you are a data analyst who works primarily with SQL queries and BI tools, you may prefer the Databricks SQL persona-based environment.
The Databricks Data Science & Engineering guide provides how-to guidance to help you get the most out of the Databricks collaborative analytics platform. For getting started tutorials and introductory information, see Get started with Databricks and Introduction to Databricks.
- Navigate the workspace
Learn how to navigate a Databricks workspace and access the assets available in the workspace.
- DataFrames and Datasets
Learn how to use Apache Spark DataFrames and Datasets in Databricks.
- Structured Streaming
Learn how to use Apache Spark Structured Streaming to express computation on streaming data in Databricks.
Learn about the types of Databricks runtimes and runtime contents.
Learn about Databricks clusters and how to create and manage them.
Learn how to manage and use notebooks in Databricks.
Learn how to work with data processing tools and frameworks in Databricks.
Learn how to use and manage libraries in Databricks.
- Git integration with Databricks Repos
Learn how to use Git to manage Databricks notebooks and workspace folders as co-versioned Databricks Repos.
- Databricks File System (DBFS)
Learn about Databricks File System (DBFS), a distributed file system mounted into a Databricks workspace and available on Databricks clusters
Learn how to migrate workloads to Databricks.
- Applications: Genomics
Learn how to work with genomic data using Databricks and Glow.