Azure Databricks

Fast, easy and collaborative Apache SparkTM based analytics service

Big data analytics and AI with optimised Apache Spark

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch and scikit-learn.

Apache Spark™ is a trademark of the Apache Software Foundation.

Reliable data engineering

Large-scale data processing for batch and streaming workloads

Analytics for all your data

Enable analytics for the most complete and recent data

Collaborative data science

Simplify and accelerate data science on large datasets

Rooted in open source

Fast, optimised Apache Spark environment

Start quickly with an optimised Apache Spark environment

Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Read Azure Databricks documentation

Boost productivity with a shared workspace and common languages

Collaborate effectively on shared projects using the interactive workspace and notebook experience, whether you are a data engineer, data scientist or business analyst. Build with your choice of language, including Python, Scala, R and SQL. Get easy version control of notebooks with GitHub and Azure DevOps.

Learn how to create an Azure Databricks workspace

Turbocharge machine learning on big data

Access advanced automated machine learning capabilities using the integrated Azure Machine Learning to quickly identify suitable algorithms and hyperparameters. Simplify management, monitoring and updating of machine learning models deployed from the cloud to the edge. Azure Machine Learning also provides a central registry for your experiments, machine learning pipelines and models.

Watch a webinar on Azure Databricks and Azure Machine Learning

Get high-performance modern data warehousing

Combine data at any scale and get insights through analytical dashboards and operational reports. Automate data movement using Azure Data Factory, then load data into Azure Data Lake Storage, transform and clean it using Azure Databricks and make it available for analytics using Azure Synapse Analytics. Modernise your data warehouse in the cloud for unmatched levels of performance and scalability.

Learn about cloud scale analytics on Azure

Key service capabilities

Optimised spark engine

Simple data processing on autoscaling infrastructure, powered by highly optimised Apache Spark™ for up to 50 x performance gains.

Machine learning run time

One-click access to preconfigured machine learning environments for augmented machine learning with state-of-the-art and popular frameworks such as PyTorch, TensorFlow and scikit-learn.

MLflow

Track and share experiments, reproduce runs and manage models collaboratively from a central repository.

Choice of language

Use your preferred language, including Python, Scala, R, Spark SQL and .Net—whether you use serverless or provisioned compute resources.

Collaborative notebooks

Quickly access and explore data, find and share new insights and build models collaboratively with the languages and tools of your choice.

Delta lake

Bring data reliability and scalability to your existing data lake with an open source transactional storage layer designed for the full data lifecycle.

Native integrations with Azure services

Complete your end-to-end analytics and machine learning solution with deep integration with Azure services such as Azure Data Factory, Azure Data Lake Storage, Azure Machine Learning and Power BI.

Interactive workspaces

Enable seamless collaboration between data scientists, data engineers and business analysts.

Enterprise-grade security

Effortless native security protects your data where it lives and creates compliant, private and isolated analytics workspaces across thousands of users and datasets.

Production-ready

Run and scale your most mission-critical data workloads with confidence on a trusted data platform, with ecosystem integrations for CI/CD and monitoring.

Learn more from solution architecture examples

Real-time analytics on big data architecture

Get insights from live-streaming data with ease. Capture data continuously from any IoT device or logs from website clickstreams and process it in near-real time.

Advanced analytics architecture

Transform your data into actionable insights using best-in-class machine learning tools. This architecture allows you to combine any data at any scale and to build and deploy custom machine learning models at scale.

Machine learning lifecycle management

Accelerate and manage your end-to-end machine learning lifecycle with Azure Databricks, MLflow and Azure Machine Learning to build, share, deploy and manage machine learning applications.

Data security and privacy are non-negotiable

  • Secure, monitor and manage your data and analytics solutions with a wide range of industry-leading security and compliance features.

  • Use single sign-on and Azure Active Directory integration to enable data professionals to spend more time discovering insights.

  • Azure has more certifications than any other cloud provider. View a comprehensive list.

Learn more about Azure Databricks products and services

Azure Databricks pricing

Trusted by companies across industries

Identifying safety hazards using cloud-based deep learning

Shell uses Azure, AI and machine vision to better protect customers and employees.

Read the story

Shell

Accelerating performance and increasing cost savings

Data service renewablesAI uses Azure and Apache Spark to help build a stable and profitable solar energy market.

Read the story

Renewables AI

Enabling an end-to-end analytics solution in Azure

Logistics provider LINX Cargo Care Group drives companywide innovation using Azure Databricks.

Read the story

LINX Cargo Care Group

Get started with Azure Databricks

Sign up for an Azure free account to get instant access.
Read the documentation to learn how to use Azure Databricks.
Explore the quickstart to create a cluster, notebook, table and more.

Community and Azure support

Ask questions and get support from Microsoft engineers and Azure community experts on MSDN Forum and Stack Overflow or contact Azure support.

Popular labs and templates

Discover self-paced labs and popular quickstart templates for common configurations made by Microsoft and the community.

Get the latest Azure Databricks news and resources

Frequently asked questions about Azure Databricks

  • The Azure Databricks SLA guarantees 99.95 percent availability.
  • A Databricks unit (“DBU”) is a unit of processing capability per hour, billed on per-second usage.
  • A data engineering workload is a job that automatically starts and terminates the cluster on which it runs. For example, a workload may be triggered by the Azure Databricks job scheduler, which launches an Apache Spark cluster solely for the job and automatically terminates the cluster after the job is complete.
    The data analytics workload is not automated. For example, commands within Azure Databricks notebooks run on Apache Spark clusters until they are manually terminated. Multiple users can share a cluster to analyse it collaboratively.

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