Databricks Notebooks Cheat Sheet

Updated 2026-04-20

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Databricks notebooks are interactive, multi-language development environments within the Databricks Data Intelligence Platform, designed for collaborative data engineering, analytics, and machine learning workflows. They combine executable code cells with visualizations, markdown documentation, and real-time collaboration features. Unlike traditional Jupyter notebooks, Databricks notebooks provide native integration with Apache Spark, automatic versioning, built-in Git support, serverless compute, and AI-powered coding assistance through Genie Code—making them production-ready tools for both exploratory analysis and automated data pipelines. Understanding magic commands, dbutils, widgets, and the notebook execution model is essential for maximizing productivity.

What This Cheat Sheet Covers

This topic spans 14 focused tables and 153 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Notebook Basics and Cell TypesTable 2: Magic CommandsTable 3: Code Development FeaturesTable 4: Databricks Utilities (dbutils)Table 5: Compute and ExecutionTable 6: Data VisualizationTable 7: Notebook Widgets and ParametersTable 8: Collaboration and SharingTable 9: Version Control and Git IntegrationTable 10: File Formats and Import/ExportTable 11: Notebook Workflows and OrchestrationTable 12: Debugging and MonitoringTable 13: Advanced FeaturesTable 14: Performance and Best Practices

Table 1: Notebook Basics and Cell Types

Start here to understand how a notebook actually runs: code versus markdown cells, the keyboard shortcuts that execute them, and the display() function that gives Databricks its richer table and chart rendering. A couple of less-obvious touches live here too — the notebook's default language, per-cell language overrides, and the _sqldf variable that quietly hands a SQL cell's results to the next Python cell.

Concept	Example	Description
Code cell	`df = spark.read.table("sales")` `display(df)`	• Default cell type that executes code in the notebook's default language (Python, Scala, SQL, or R) • results appear below the cell immediately after execution.
Run cell	Press `Shift+Enter`	• Executes current cell and automatically moves focus to the next cell • `Ctrl+Enter` runs cell without advancing • cells execute sequentially by default.
display() function	`display(spark.read.parquet("/data"))` `display(dbutils.fs.ls("/mnt"))`	• Databricks-specific function for enhanced table rendering with built-in chart builder and data profile • triggers visualization options for DataFrames • shows formatted tables for dbutils commands.
Run all	Click Run All button	• Executes all cells from top to bottom • on DBR 14.0+ runs as a batch workflow and halts on first error • cells with `%skip` are excluded.
Cell output	`display(df)` returns interactive table	• Rendered results below cell • supports tables, charts, HTML, images, and text • output persists in IPYNB format when output commits are enabled.
Markdown cell	`%md` `# Sales Report` `Data updated: 2026-04-20`	• Creates formatted documentation using Markdown syntax • supports headings, lists, tables, images, and inline code for narrative explanations.

Table 1: Notebook Basics and Cell Types

Concept	Example	Description
Code cell	`df = spark.read.table("sales")` `display(df)`	• Default cell type that executes code in the notebook's default language (Python, Scala, SQL, or R) • results appear below the cell immediately after execution.
Run cell	Press `Shift+Enter`	• Executes current cell and automatically moves focus to the next cell • `Ctrl+Enter` runs cell without advancing • cells execute sequentially by default.
display() function	`display(spark.read.parquet("/data"))` `display(dbutils.fs.ls("/mnt"))`	• Databricks-specific function for enhanced table rendering with built-in chart builder and data profile • triggers visualization options for DataFrames • shows formatted tables for dbutils commands.
Run all	Click Run All button	• Executes all cells from top to bottom • on DBR 14.0+ runs as a batch workflow and halts on first error • cells with `%skip` are excluded.
Cell output	`display(df)` returns interactive table	• Rendered results below cell • supports tables, charts, HTML, images, and text • output persists in IPYNB format when output commits are enabled.
Markdown cell	`%md` `# Sales Report` `Data updated: 2026-04-20`	• Creates formatted documentation using Markdown syntax • supports headings, lists, tables, images, and inline code for narrative explanations.