Data solutions for Python apps on Azure

Azure offers a wide range of fully managed database and storage solutions, including relational, NoSQL, and in-memory databases, with support for both proprietary and open-source technologies. You can also choose from object, block, and file storage services. The following articles can help you get started using these options with Python on Azure.

Databases

• PostgreSQL: Build scalable, secure, and fully managed enterprise apps using open-source PostgreSQL. You can scale single-node PostgreSQL for high performance or migrate existing PostgreSQL and Oracle workloads to the cloud.

  • Quickstart: Use Python to connect and query data in Azure Database for PostgreSQL - Flexible Server
  • Quickstart: Use Python to connect and query data in Azure Database for PostgreSQL - Single Server
  • Deploy a Python (Django or Flask) web app with PostgreSQL in Azure App Service

• MySQL: Build scalable applications using a fully managed, intelligent MySQL database in the cloud.

  • Quickstart: Use Python to connect and query data in Azure Database for MySQL - Flexible Server
  • Quickstart: Use Python to connect and query data in Azure Database for MySQL

• Azure SQL: Build scalable applications with a fully managed and intelligent SQL database platform in the cloud.

  • Quickstart: Use Python to query a database in Azure SQL Database or Azure SQL Managed Instance

NoSQL, blobs, tables, files, graphs, and caches

• Cosmos DB: Build low-latency, high-availability apps at global scale, or migrate Cassandra, MongoDB, and other NoSQL workloads to the cloud.

  • Quickstart: Azure Cosmos DB for NoSQL client library for Python
  • Quickstart: Azure Cosmos DB for MongoDB for Python with MongoDB driver
  • Quickstart: Build a Cassandra app with Python SDK and Azure Cosmos DB
  • Quickstart: Build an API for Table app with Python SDK and Azure Cosmos DB
  • Quickstart: Azure Cosmos DB for Apache Gremlin library for Python

• Blob storage: Secure, massively scalable object storage for cloud-native apps, data lakes, archives, high-performance computing (HPC), and machine learning.

  • Quickstart: Azure Blob Storage client library for Python
  • Azure Storage samples using v12 Python client libraries

• Azure Data Lake Storage Gen2: Scalable, secure data lake optimized for high-performance analytics.

  • Use Python to manage directories and files in Azure Data Lake Storage Gen2
  • Use Python to manage ACLs in Azure Data Lake Storage Gen2

• File storage: Simple, secure, and serverless enterprise-grade cloud file shares.

  • Develop for Azure Files with Python

• Redis Cache: Accelerate application performance with a scalable, in-memory data store compatible with open source.

  • Quickstart: Use Azure Cache for Redis in Python

Big data and analytics

• Azure Data Lake analytics: Fully managed, pay-per-job analytics service that delivers powerful parallel data processing with built-in enterprise-grade security, auditing, and support.

  • Manage Azure Data Lake Analytics using Python
  • Develop U-SQL with Python for Azure Data Lake Analytics in Visual Studio Code

• Azure Data Factory: A fully managed data integration service that lets you visually build, orchestrate, and automate data movement and transformation across various data sources.

  • Quickstart: Create a data factory and pipeline using Python
  • Transform data by running a Python activity in Azure Databricks

• Azure Event Hubs: A fully managed, hyper-scale telemetry ingestion service designed to collect, transform, and store millions of events per second from connected devices and applications.

  • Send events to or receive events from event hubs by using Python
  • Capture Event Hubs data in Azure Storage and read it by using Python (azure-eventhub)

• HDInsight: A fully managed cloud service that runs popular open-source frameworks like Hadoop and Spark, backed by a 99.9% SLA for enterprise-grade big data analytics.

  • Use Spark & Hive Tools for Visual Studio Code

• Azure Databricks: A fully managed, fast, easy and collaborative Apache® Spark™ based analytics platform optimized for big data and AI workloads on Azure.

  • Connect to Azure Databricks from Excel, Python, or R
  • Get Started with Azure Databricks
  • Tutorial: Azure Data Lake Storage Gen2, Azure Databricks & Spark

• Azure Synapse Analytics: A fully managed analytics service that unifies data integration, enterprise data warehousing, and big data analytics into a single platform.

  • Quickstart: Use Python to query a database in Azure SQL Database or Azure SQL Managed Instance (includes Azure Synapse Analytics)