A core component of Databricks is the Data Science Workspace, which enables collaboration among everyone in the data team. The collaborative notebook environment is used by everyone on the data team: data scientists, data analysts, data engineers and others. Databricks is used by a wide variety of industries for an equally expansive set of use cases. This gallery showcases some of the possibilities through Notebooks which can easily be imported into your own Databricks environment or the free community edition (CE).
Delta Lake
Build your data lakehouse and get ACID transactions, time travel, contraints and more on open file formats
Databricks: 7.6.x – not CE
Deep Dive into Delta Lake
This is a deep dive into Delta Lake, which is an open-source storage format that brings ACID transactions to Apache Spark™.
Databricks: 8.0.x
Using Delta Lake from R
This is a quick 101 introduction to using Delta Lake, which is an open-source storage format, using SparkR.
Machine Learning
Support for popular machine learning frameworks like TensorFlow, Spark MLlib, Horovod
Databricks: 7.6.x w/GPU – not CE
Distributed deep learning with PyTorch and Horovod
Learn how to perform distributed training of models in PyTorch using Horovod.
Databricks: 8.1.x
Build a streaming ML application with Spark
Build a streaming ML application that monitors credit card fraud using Spark.
Databricks: 7.6.x
Getting started with Spark MLlib
An introduction to using the Spark MLlib library for ML applications.
Databricks: 7.6.x w/GPU – not CE
From Spark to TensorFlow: Simplify your data conversion
Simplify the conversion of data from Spark DataFrames for use with TensorFlow.
MLflow
End-to-end support for machine learning: from training your models to moving them into production
Databricks: 7.6.x
Get started with logging for ML projects with MLflow
An introduction to the MLflow logging API for ML workflow management.
Databricks: 7.6.x
Quick Start : How to use MLflow fluent tracking APIs
Learn how to use the high-level fluent tracking APIs in MLflow.
Databricks: 7.6.x – not CE
An end-to-end example of machine learning for tabular data
This is a notebook showcasing an example of an end-to-end ML lifecycle for tabular data.
Apache Spark™
The distributed computing engine that powers data engineering and data science for the data lakehouse
Databricks: 8.1.x
Streaming applications for sensor data
Learn how to use Structured Streaming in Spark for sensor data applications.
Databricks: 8.1.x
Analysis of the San Franciso fire calls with Spark
Use Spark ETL to analyze the calls to the San Francisco Fire Department.
Databricks: 8.1.x
Interacting with External data sources from Spark
A brief introduction on how to access and interact with external data sources from Spark.
Databricks: 8.1.x
Structured Streaming for real-time applications
An introduction to the semantics of Structured Streaming in Spark for real-time data.
Databricks: 8.0.x – not CE
Extend SparkR with user-defined functions (UDFs)
Learn how to extend the capabilities of SparkR through custom functions written using UDFs in R.
Use Cases
Databricks is used across many industries, including finance, retail, technology, manufacturing and more
Databricks: 7.6.x
Market basket analysis for retail
This is a notebook showcasing how to perform market basket analysis for retail.
Solution
Accelerators
Complete templates for using Databricks in five different industries
