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Azure Databricks is a cloud service that provides an optimized Apache Spark environment for data engineering, data science, and machine learning. Learn how to use Azure Databricks with Azure services, open source libraries, and popular frameworks to build and deploy AI solutions.
- Overview
- How does a data intelligence platform work?
- What is Azure Databricks used for?
- Managed integration with open source
- Tools and programmatic access
- How does Azure Databricks work with Azure?
- What are common use cases for Azure Databricks?
- Build an enterprise data lakehouse
- ETL and data engineering
- Machine learning, AI, and data science
- GeneratedCaptionsTabForHeroSec
Azure Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. The Databricks Data Intelligence Platform integrates with cloud storage and security in your cloud account, and manages and deploys cloud infrastructure on your behalf.
Azure Databricks uses generative AI with the data lakehouse to understand the unique semantics of your data. Then, it automatically optimizes performance and manages infrastructure to match your business needs.
Natural language processing learns your business’s language, so you can search and discover data by asking a question in your own words. Natural language assistance helps you write code, troubleshoot errors, and find answers in documentation.
Azure Databricks provides tools that help you connect your sources of data to one platform to process, store, share, analyze, model, and monetize datasets with solutions from BI to generative AI.
The Azure Databricks workspace provides a unified interface and tools for most data tasks, including:
•Data processing scheduling and management, in particular ETL
•Generating dashboards and visualizations
•Managing security, governance, high availability, and disaster recovery
•Data discovery, annotation, and exploration
Databricks has a strong commitment to the open source community. Databricks manages updates of open source integrations in the Databricks Runtime releases. The following technologies are open source projects originally created by Databricks employees:
•Delta Lake and Delta Sharing
•MLflow
•Apache Spark and Structured Streaming
Azure Databricks maintains a number of proprietary tools that integrate and expand these technologies to add optimized performance and ease of use, such as the following:
•Workflows
•Unity Catalog
•Delta Live Tables
•Databricks SQL
•Photon compute clusters
The Azure Databricks platform architecture comprises two primary parts:
•The infrastructure used by Azure Databricks to deploy, configure, and manage the platform and services.
•The customer-owned infrastructure managed in collaboration by Azure Databricks and your company.
Unlike many enterprise data companies, Azure Databricks does not force you to migrate your data into proprietary storage systems to use the platform. Instead, you configure an Azure Databricks workspace by configuring secure integrations between the Azure Databricks platform and your cloud account, and then Azure Databricks deploys compute clusters using cloud resources in your account to process and store data in object storage and other integrated services you control.
Unity Catalog further extends this relationship, allowing you to manage permissions for accessing data using familiar SQL syntax from within Azure Databricks.
Azure Databricks workspaces meet the security and networking requirements of some of the world’s largest and most security-minded companies. Azure Databricks makes it easy for new users to get started on the platform. It removes many of the burdens and concerns of working with cloud infrastructure, without limiting the customizations and control experienced data, operations, and security teams require.
Use cases on Azure Databricks are as varied as the data processed on the platform and the many personas of employees that work with data as a core part of their job. The following use cases highlight how users throughout your organization can leverage Azure Databricks to accomplish tasks essential to processing, storing, and analyzing the data that...
The data lakehouse combines the strengths of enterprise data warehouses and data lakes to accelerate, simplify, and unify enterprise data solutions. Data engineers, data scientists, analysts, and production systems can all use the data lakehouse as their single source of truth, allowing timely access to consistent data and reducing the complexities...
Whether you’re generating dashboards or powering artificial intelligence applications, data engineering provides the backbone for data-centric companies by making sure data is available, clean, and stored in data models that allow for efficient discovery and use. Azure Databricks combines the power of Apache Spark with Delta Lake and custom tools to provide an unrivaled ETL (extract, transform, load) experience. You can use SQL, Python, and Scala to compose ETL logic and then orchestrate scheduled job deployment with just a few clicks.
Delta Live Tables simplifies ETL even further by intelligently managing dependencies between datasets and automatically deploying and scaling production infrastructure to ensure timely and accurate delivery of data per your specifications.
Large language models and generative AI
Databricks Runtime for Machine Learning includes libraries like Hugging Face Transformers that allow you to integrate existing pre-trained models or other open-source libraries into your workflow. The Databricks MLflow integration makes it easy to use the MLflow tracking service with transformer pipelines, models, and processing components. In addition, you can integrate OpenAI models or solutions from partners like John Snow Labs in your Databricks workflows. With Azure Databricks, you can customize a LLM on your data for your specific task. With the support of open source tooling, such as Hugging Face and DeepSpeed, you can efficiently take a foundation LLM and start training with your own data to have more accuracy for your domain and workload. In addition, Azure Databricks provides AI functions that SQL data analysts can use to access LLM models, including from OpenAI, directly within their data pipelines and workflows. See AI Functions on Azure Databricks.
Azure Databricks is a cloud service that lets you build, deploy, and maintain data, analytics, and AI solutions at scale. Learn how it works with Azure, integrates with open source, and supports data lakehouse, ETL, ML, AI, and BI use cases.
Azure Databricks is a data and AI service from Databricks that runs on Microsoft Azure. It offers a simple, open and collaborative platform to store all your data on a lakehouse and unify your analytics and AI workloads.
Azure Databricks is a fully managed first-party service that enables an open data lakehouse in Azure. With a lakehouse built on top of an open data lake, quickly light up a variety of analytical workloads while allowing for common governance across your entire data estate.
Learn Azure Databricks, a unified analytics platform for data analysts, data engineers, data scientists, and machine learning engineers. Find tutorials, how-to guides, concepts, reference, and troubleshooting for various features and integrations.
Apr 12, 2024 · Learn how Azure Databricks operates out of a control plane and a compute plane, with serverless and classic options. See the diagram and the details of each plane and their networking features.
Learn how to build a data lakehouse with Azure Databricks, the jointly-developed data and AI service from Databricks and Microsoft. Download the resources and follow the notebooks to ingest, query and train data with Azure Databricks.
