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.
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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. |
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. |
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