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Azure Databricks

高速で使いやすい、コラボレーション対応の Apache Spark ベースの分析サービス

概要

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.

Azure Databricks offers new capabilities at a lower cost –– Read the blog

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 SQL Data Warehouse

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 service 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 service 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 SQL Data Warehouse.

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.
More on Azure Databricks security

Azure Databricks pricing

  • Spin up clusters quickly and autoscale up or down based on your usage needs. Explore all Azure Databricks pricing options.
価格の詳細

Trusted by companies across industries

  1. Shell
  2. Renewables AI
  3. LINX Cargo Care Group

Identifying safety hazards using cloud-based deep learning

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

事例を見る

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.

事例を見る

Renewables AI

Enabling an end-to-end analytics solution in Azure

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

事例を見る

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.

Webinars, e-books, and reports

Explore Azure Databricks resources

Webinars

E-books

Report

Databricks updates, blogs, and announcements

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.

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