Check out the full Azure Databricks Series playlist here 🎥👉 🚀 Welcome to another exciting episode in the Azure Databricks Series! In this video, we’ll walk through a step-by-step guide to create a Catalog in Azure Databricks using tables from Azure PostgreSQL. 🐘 You’ll learn how to connect Azure Databricks to PostgreSQL, configure Public Access, and ensure secure communication using a Private Endpoint — all in one smooth and practical workflow! 💡 This video is perfect for data engineers, architects, and Azure professionals who want to build a real-world data integration setup between Databricks and Azure PostgreSQL. Whether you’re working on analytics, data warehousing, or modern lakehouse architecture — this session will show you how to make PostgreSQL data available in your Databricks Catalog in just a few easy steps. 🔗 Error testing connection summary: [CANNOT_ESTABLISH_CONNECTION] Cannot establish connection to remote POSTGRESQL database. Please check connection information and credentials e.g. host, port, user, password and database options. ** If you believe the information is correct, please check your workspace's network setup and ensure it does not have outbound restrictions to the host. Please also check that the host does not block inbound connections from the network where the workspace's Spark clusters are deployed. ** Detailed error message: : Connect timed out. SQLSTATE: 08001 Your request failed with status FAILED: [BAD_REQUEST] The connection attempt failed. 💻 What You’ll Learn in This Video ✨ How to configure Azure PostgreSQL for Public Access ✨ How to create a Private Endpoint to establish secure connectivity ✨ How to connect Azure Databricks to PostgreSQL using JDBC ✨ How to create a Catalog in Databricks that uses PostgreSQL as a source ✨ How to query PostgreSQL tables directly within the Databricks environment ✨ Best practices for secure, scalable, and production-grade integration 🌐 Why This Setup Matters Many organizations store structured data in Azure PostgreSQL, but use Databricks for advanced analytics, machine learning, and data engineering. Integrating these two platforms allows you to: 🔹 Build a unified data lakehouse by bringing PostgreSQL tables into Databricks 🔹 Simplify ETL pipelines by connecting directly to source systems 🔹 Avoid manual data exports or duplicated storage 🔹 Govern data centrally using Databricks Unity Catalog 🔹 Maintain security and compliance with Private Endpoints By the end of this tutorial, you’ll have a fully working setup that demonstrates how to bring PostgreSQL tables directly into your Databricks Catalog — making your analytics faster, cleaner, and more manageable. ⚡ 🔒 Public Access + Private Endpoint Explained Even though Azure PostgreSQL allows Public Access for simplicity, we’ll use a Private Endpoint to make sure that communication between Databricks and PostgreSQL happens securely within your virtual network. 🌩️ This ensures that no sensitive traffic leaves the Azure backbone, aligning your setup with enterprise security best practices. We’ll go through the configuration step by step so you understand how each component interacts — from Databricks workspace settings to PostgreSQL networking configurations. 🧭 🧠 Real-World Scenario Let’s say your organization’s application stores transactional data in Azure PostgreSQL, and you want to perform analytical transformations using Databricks. Instead of exporting CSV files or relying on intermediate storage, you can directly connect Databricks to PostgreSQL and query live data inside the Databricks workspace. With the Unity Catalog integration, this becomes part of your governed data ecosystem, where access controls, schema management, and data lineage are automatically handled. 🗂️ This approach saves time, improves data freshness, and simplifies your entire data engineering process. 💪 🔧 Technologies Used in This Demo Azure Databricks 🧠 for data engineering and catalog management Azure PostgreSQL 🐘 for relational data storage Azure Private Endpoint 🔒 for secure network communication JDBC Connector ⚙️ for connectivity between Databricks and PostgreSQL 💬 Key Takeaways ✅ Learn how to configure secure connections between Databricks and Azure PostgreSQL ✅ Understand the role of Public Access vs. Private Endpoint ✅ Discover how to create and manage a Databricks Catalog ✅ Query PostgreSQL data directly from Databricks notebooks ✅ Gain practical knowledge you can apply to production environments 🌟 Best Practices Shared in This Video Always test connectivity using Databricks notebook commands before catalog creation Use managed identities or secret scopes instead of storing credentials in plain text Keep Public Access restricted and rely on Private Endpoints for production environments Regularly audit your Databricks connections for security compliance