Databricks data engineering

Databricks data engineering features are a robust environment for collaboration among data scientists, data engineers, and data analysts. Data engineering tasks are also the backbone of Databricks machine learning solutions.

Note

If you are a data analyst who works primarily with SQL queries and BI tools, you might prefer Databricks SQL.

The data engineering documentation 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: Account and workspace setup and What is Databricks?.

• Delta Live Tables

  Learn how to build data pipelines for ingestion and transformation with Databricks Delta Live Tables.

• Structured Streaming

  Learn about streaming, incremental, and real-time workloads powered by Structured Streaming on Databricks.

• Apache Spark

  Learn how Apache Spark works on Databricks and the Databricks Lakehouse Platform.

• Runtimes

  Learn about the types of Databricks runtimes and runtime contents.

• Clusters

  Learn about Databricks clusters and how to create and manage them.

• Notebooks

  Learn what a Databricks notebook is, and how to use and manage notebooks to process, analyze, and visualize your data.

• Workflows

  Learn how to orchestrate data processing, machine learning, and data analysis workflows on the Databricks Lakehouse platform.

• Storage

  Learn how Databricks uses cloud object storage and block storage volumes for persistent and ephemeral data storage.

• Libraries

  Learn how to make third-party or custom code available in Databricks using libraries. Learn about the different modes for installing libraries on Databricks.

• Init scripts

  Learn how to use initialization (init) scripts to install packages and libraries, set system properties and environment variables, modify Apache Spark config parameters, and set other configurations on Databricks clusters.

• Repos

  Learn how to use Git to version control your notebooks and other files for development in Databricks.

• DBFS

  Learn about Databricks File System (DBFS), a distributed file system mounted into a Databricks workspace and available on Databricks clusters

• Files

  Learn about options for working with files on Databricks.

• Migration

  Learn how to migrate data applications such as ETL jobs, enterprise data warehouses, ML, data science, and analytics to Databricks.

• Optimization & performance

  Learn about optimizations and performance recommendations on Databricks.