ABOUT COOKIES ON THIS SITE

We use cookies to personalize content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners.

Databricks: analytics platform, from the original creators of Apache Spark™

Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the analytics lifecycle, from preparation, setting up big data and streaming data flows to experimentation and deployment of ML applications.
Handle modern data use cases from data warehousing of large data volumes or IoT data to advanced analytics cases such as machine and deep learning, together with delaware and Databricks on Microsoft’s Azure cloud platform. Databricks, founded by the team that created Apache Spark, is a unified analytics platform that accelerates innovation by unifying data science, engineering and business.
On Microsoft Azure, Databricks is offered by Microsoft as a first-party service, which means you get all the things you love about Azure, such as enterprise-grade security and SLAs as well as seamless integration with existing Azure services (including AAD authentication), with Databricks’ fully managed, interactive workspace for modern data use cases.

Databricks drives innovation by giving you a unified platform for your modern data needs.

Functional highlights

Databricks focuses on modern data needs, enabling you to get started quickly without the hassle of managing a complex analytics landscape yourself.

It provides the following functionalities:

  • One-click set-up unlocks a fast track towards experimentation and innovation. Databricks offers fully managed Spark clusters in the cloud, provisioned directly from the Azure Portal like any other Azure service, and integrates seamlessly with other Azure services. This integration goes from data services such as Azure SQL DB, Azure CosmosDB and Azure Data Lake to enabling a true CI/CD pipeline with Azure DevOps or creating ready-to-deploy container images for edge devices via Azure Machine Learning Workspace.
  • Streamlined workflows offer a production pipeline scheduler to easily schedule a Databricks notebook as a job. This offers a seamless go-live of your data science and engineering efforts: what you write is what you run. Jobs can start and stop a cluster when needed to offer a true pay-as-you-use setup.
  • Via the interactive, notebook-style workspace, you enable collaboration between data scientists, data engineers, and business analysts. The workspace allows a one-click interface to assign notebooks to different clusters running different versions of the underlying Spark and Scale/Python runtimes to GPU-enabled clusters.
  • Next to the core runtime, Databricks includes MLflow to add DevOps to your machine-learning efforts and Delta to add a governance layer on top of an (Azure) data lake.
  • As first-party service on Microsoft Azure, Databricks on Azure offers enterprise-grade Azure security (including out-of-the-box Active Directory integration), compliance and enterprise grade SLAs.