Fast, easy, and collaborative Apache SparkTM–based analytics service
The best destination for big data analytics and AI with 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.
Fast, optimized Apache Spark environment
Interactive workspace with built-in support for popular tools, languages, and frameworks
Supercharged machine learning on big data with native Azure Machine Learning integration
High-performance modern data warehousing in conjunction with Azure Synapse Analytics
Start quickly with an optimized 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’re 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
Modernize your data warehouse in the cloud for unmatched levels of performance and scalability. Combine data at any scale, and get insights through analytical dashboards and operational reports. Automate data movement using Azure Data Factory, load data into Azure Data Lake Storage, transform and clean it using Azure Databricks, and then make it available for visualization using Azure Synapse Analytics.Learn about modern data warehousing on Azure
Industry-leading security and compliance
- Take advantage of native integration with Azure Active Directory for role-based access control.
- Create secure architectures without compromising on compliance using configurable virtual networks.
- Get peace of mind with fine-grained user permissions for Azure Databricks notebooks, clusters, jobs, and data.
Azure Databricks pricing
- Spin up clusters quickly and autoscale up or down based on your usage needs. Explore all Azure Databricks pricing options.
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.
Accelerating performance and increasing cost savings
Data service renewablesAI uses Azure and Apache Spark to help build a stable and profitable solar energy market.
Enabling an end-to-end analytics solution in Azure
Logistics provider LINX Cargo Care Group drives companywide innovation using Azure Databricks.
Get started with Azure Databricks
Community and Azure support
Webinars, e-books, and reports
Explore Azure Databricks resources
Frequently asked questions about Azure Databricks
The Azure Databricks SLA guarantees 99.95 percent availability.
A Databricks unit, or 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 isn’t automated. For example, commands within Azure Databricks notebooks run on Apache Spark clusters until they’re manually terminated. Multiple users can share a cluster to analyze it collaboratively.
Ready when you are—let’s set up your Azure free accountStart free
Try Azure Databricks for 14 days
Take advantage of full Azure product integration, enterprise-grade performance, and SLA support with your trial. With free Databricks units, only pay for virtual machines you use.
Try with an Azure pay-as-you-go account
Create an Azure pay-as-you-go account and get free Databricks units. Pay only for the virtual machines you use, with no upfront commitments. Cancel anytime.
Try with an existing Azure account
Sign in to the Azure portal with your existing Azure account to get started. Get free Databricks units and pay only for virtual machines you use.