The tutorials in this section introduce core features and guide you through the basics of working with the Databricks platform. For information about online training resources, see Get free …

This tutorial walks you through using a Databricks notebook to query sample data stored in Unity Catalog using SQL, Python, Scala, and R and then visualize the query results in the notebook.

This tutorial shows you how to load and transform data using the Apache Spark Python (PySpark) DataFrame API, the Apache Spark Scala DataFrame API, and the SparkR SparkDataFrame …

In this step, you will use the Lakeflow Pipelines Editor to develop and validate source code for Lakeflow Declarative Pipelines interactively. The code uses Auto Loader for incremental data …

Some reference pages also provide examples for calling a Databricks REST API operation by using the Databricks CLI, the Databricks Terraform provider, or one or more of the Databricks …

Learn how to configure a connection to Databricks using the Databricks JDBC Driver.

How to use large language models (LLMs) on Databricks, including tools and libraries such as Hugging Face, LangChain, and OpenAI.

This section describes how to create a secret using the Databricks CLI (version 0.205 and above) or in a notebook using the Databricks SDK for Python. You can also use the Secrets API.

How-to guides and reference documentation for data teams using the Databricks Data Intelligence Platform to solve analytics and AI challenges in the Lakehouse.

This article helps you get started with Databricks Apps using a step-by-step example to create a simple app in your Databricks workspace using a template that follows Databricks best practices.