Microsoft at PGConf India 2026
I’m genuinely excited about PGConf India 2026. Over the past few editions, the conference has continued to grow year over year—both in size and in impact—and it has firmly established itself as one of the key events on the global PostgreSQL calendar. That momentum was very evident again in the depth, breadth, and overall quality of the program for PGConf India 2026. Microsoft is proud to be a diamond sponsor for the conference again this year. At Microsoft, we continue our contributions to the upstream PostgreSQL open-source project—as well as to serve our customers with our Postgres managed service offerings, both Azure Database for PostgreSQL and our newest Postgres offering, Azure HorizonDB . On the open-source front, Microsoft had 540 commits in PG18, including major features like Asynchronous IO. We’re also excited to grow our Postgres open-source contributors team, and so happy to welcome Noah Misch to our team. Noah is a Postgres committer who has deep expertise in PostgreSQL security and is focused on correctness and reliability in PostgreSQL’s core. Microsoft at PGConf India 2026: Highlights from Our Speakers PGConf India has several tracks, all of which have some great talks I am looking forward to. First, the plug. 😊 Microsoft has some amazing talks this year, and we have 8 different talks spread across all the tracks. Postgres on Azure : Scaling with Azure HorizonDB, AI, and Developer Workflows, by Aditya Duvuri & Divya Bhargov Resizing shared buffer pool in a running PostgreSQL server: important, yet impossible, by Ashutosh Bapat Ten Postgres Hacker Journeys—and what they teach us, by Claire Giordano How Postgres can leverage disk bandwidth for better TPS, by Nikhil Chawla AWSM FSM! Free Space Maps Decoded by Nikhil Sontakke Journey of developing a Performance Optimization Feature in PostgreSQL, by Rahila Syed Build Agentic AI with Semantic Kernel and Graph RAG on PostgreSQL, by Shriram Muthukrishnan & Palak Chaturvedi All things Postgres @ Microsoft (2026 edition) by Sumedh Pathak Claire is an amazing speaker and has done a lot of work over the last several years documenting and understanding PostgreSQL committers and hackers. Her talk will definitely have some key insights and nuggets of information. Rahila’s talk will go in depth on performance optimization features and how best to test and benchmark them, and all the tools and tricks she has used as part of the feature development. This should be a must-see talk for anyone doing performance work. Diving Deep: Case Studies & Technical Tracks One of the tracks I’m really excited about is the Case Study track. I see these as similar to ‘Experience’ papers in academia. An experience paper documents what actually happened when applying a technique or system in the real world, what worked, what didn’t, and why. One of the talks I’m looking forward to is ‘Operating Postgres Logical Replication at Massive Scale’ by Sai Srirampur from Clickhouse. Logical Replication is an extremely useful tool, and I’m curious to learn more about pitfalls and lessons learnt when running this at large scale. Another interesting one I’m curious to hear is ‘Understanding the importance of the commit log through a database corruption’ by Amit Kumar Singh from EDB. The Database Engine Developers track allows us to go deep into the PostgreSQL code base and get a better understanding of how PostgreSQL is built. Even if you are not a database developer, this track is useful to understand how and why PostgreSQL does things, helping you be a better user of the database. With the rise of larger machines and memory available in the Cloud, different and newer memory architectures/tiers and serverless product offerings, there is a lot of deep dive in PostgreSQL’s memory architecture. There are some great talks focused on this area, which should be must-see for anyone interested in this topic: Resizing shared buffer pool in a running PostgreSQL server: important, yet impossible by Ashutosh Bapat from Microsoft From Disk to Data: Exploring PostgreSQL's Buffer Management by Lalit Choudhary from PurnaBIT Beyond shared_buffers: On-Demand Memory in Modern PostgreSQL by Vaibhav Popat from Google Finally, the Database Administration and Application Developer tracks have some really great content as well. They cover a wide range of topics, from PII data, HA/DR, Query Tuning to connection pooling and understanding conflict detection and resolution. PostgreSQL in India: A Community Effort Worth Celebrating Conferences like these are a rich source of information, dramatically increasing my personal understanding of the product and the ecosystem. Separately, they are also a great way to meet other practitioners in the space and connect with people in the industry. For people in Bangalore, another great option is the PostgreSQL Bangalore Meetup, and I’m super happy that Microsoft was able to join the ranks of other companies to host the eighth iteration of this meetup. Finally, I would be remiss in not mentioning the hard work done by the PGConf India organizing team including Pavan Deolasse, Ashish Mehra, Nikhil Sontakke, Hari Kiran, and Rushabh Lathia who are making all of this happen. Also, a big shout out to the PGConf India Program Committee (Amul Sul, Dilip Kumar, Marc Linster, Thomas Munro, Vigneshwaran C) for putting together an amazing set of talks. I look forward to meeting all of you in Bangalore! Be sure to drop by the Microsoft booth to say hello (and to snag a free pair of our famous socks). I’d love to learn more about how you’re using Postgres.January 2026 Recap: Azure Database for PostgreSQL
We just dropped the 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟲 𝗿𝗲𝗰𝗮𝗽 for Azure Database for PostgreSQL and this one’s all about developer velocity, resiliency, and production-ready upgrades. January 2026 Recap: Azure Database for PostgreSQL • PostgreSQL 18 support via Terraform (create + upgrade) • Premium SSD v2 (Preview) with HA, replicas, Geo-DR & MVU • Latest PostgreSQL minor version releases • Ansible module GA with latest REST API features • Zone-redundant HA now configurable via Azure CLI • SDKs GA (Go, Java, JS, .NET, Python) on stable APIs Read the full January 2026 recap here and see what’s new (and what’s coming) - January 2026 Recap: Azure Database for PostgreSQLFrom Oracle to Azure: How Quadrant Technologies accelerates migrations
This blog was authored by Manikyam Thukkapuram, Director, Alliances & Engineering at Quadrant Technologies; and Thiwagar Bhalaji, Migration Engineer and DevOps Architect at Quadrant Technologies Over the past 20+ years, Quadrant Technologies has accelerated database modernization for hundreds of organizations. As momentum to the cloud continues to grow, a major focus for our business has been migrating on-premises Oracle databases to Azure. We’ve found that landing customers in Azure Database for PostgreSQL has been the best option both in terms of cost savings and efficiency. Azure Migrate is by far the best way to get them there. With Azure Migrate, we’re able to streamline migrations that traditionally took months, into weeks. As a Microsoft solutions partner, we help customers migrate to Azure and develop Azure-based solutions. We’re known as “the great modernization specialists” because many of our customers come to us with complex legacy footprints, outdated infrastructure, and monolithic applications that can be challenging to move to the cloud. But we excel at untangling these complex environments. And with our Q-Migrator tool, which is a wrapper around Azure Migrate, we’re able to automate and accelerate these kinds of migrations. Manual steps slowed down timelines In general, each migration we lead includes a discovery phase, a compatibility assessment, and the migration execution. In discovery, we identify every server, database, and application in a customer’s environment and map their interactions. Next, we assess each asset’s readiness for Azure and plan for optimal cloud configurations. Finally, we bring the plan to life, integrating applications, moving workloads, and validating performance. Before adopting Azure Migrate, each of these phases involved manual tasks for our team. During our discovery process we manually collected inventory and wrote custom scripts to track server relationships and database dependencies. Our engineers also had to dig through configuration files and use third-party assessment tools for aspects like VM utilization and Oracle schema. When we mapped compatibility, we worked from static data to predict cost estimates and sizing, as opposed to operating from real-time telemetry. By the time we reached the migration phase, fragmented tooling and inconsistent assessments made it difficult to maintain accuracy and efficiency. Hidden dependencies sometimes surfaced late in the process, causing unexpected rework and delays. Streamlining migrations with Azure Migrate To automate and streamline these manual tasks, we developed Q-Migrator, which is our in-house framework built around Azure Migrate. Now we can offer clients an efficient, agentless approach to discovery, assessment, and migration. As part of our on-premises database migration initiatives, we rely on Azure Migrate to seamlessly migrate a wide range of structured databases (including MySQL, Microsoft SQL Server, PostgreSQL, and Oracle) from on-premises environments to Azure IaaS and PaaS. For instance, for an on-premises PostgreSQL migration, we begin by setting up an Azure Migrate appliance in the client’s environment to automatically discover servers, databases, and applications. That generates a complete inventory and dependency map that identifies every relationship between servers and databases. From there, we run an assessment through Azure Migrate to check compatibility, identify blockers, and right-size target environments for Azure Database for PostgreSQL. By integrating Azure Database Migration Service (DMS), we can replicate data continuously until cutover, ensuring near-zero downtime. In addition, Azure DMS provides robust telemetry and analytics for deep visibility into every stage of the process. This unified and automated workflow not only replaces manual steps but also increases reliability and accelerates delivery. Teams benefit from a consolidated dashboard for planning, execution, and performance tracking, driving efficiency throughout the migration lifecycle. 75% faster deployment, 60% cost savings Since implementing Azure Migrate, which now facilitates discovery and assessment for on-premises PostgreSQL workloads, we’ve accelerated deployment by 75% compared to traditional migration methods. We’ve also reduced costs for our clients by up to 60 percent. Automated discovery alone reduces that phase by nearly 40%, and dependency mapping now takes a fraction of the effort. With the integrated dashboard in Azure Migrate we can also track progress across discovery, assessment, and migration in one place. This eliminates the need for multiple third-party tools. These efficiencies allow us to deliver complex migrations on tighter timelines without sacrificing quality or reliability. Rounding out the modernization journey with AKS As “the great modernization specialists,” we’re often asked which is the best database for landing Oracle workloads in the cloud. From our experience, Azure Database for PostgreSQL is ideal for enterprises seeking cost-efficient and secure PostgreSQL deployments. Its managed services reduce operational overhead while maintaining high availability, compliance, and scalability. Plus, seamless integration with Azure AI services allows us to innovate for clients and keep them ahead of the curve. We also recognize that database migration is only the first step for many clients—modernizing the application layer delivers even greater scalability, security, and manageability. When clients come to Quadrant for a broader modernization strategy, we often use Azure Kubernetes Service (AKS) to containerize their applications and break monoliths into microservices. AKS delivers a cloud-native architecture alongside database modernization. This integration supports DevOps practices, simplifies deployments, and allows customers to take full advantage of elastic cloud infrastructure. More innovation to come Overall, Azure Migrate and Azure Database for PostgreSQL, Azure Database for MySQL, and Azure SQL Database have redefined how we deliver database modernization, and our close collaboration with Microsoft has made it possible. By engaging early with Microsoft, we can validate migration architectures and gain insights into best practices for high-performance and secure cloud deployments. Access to Microsoft experts helps us fine-tune our designs, optimize performance, and resolve complex issues quickly. We’re also investing in AI-driven automation using Azure OpenAI in Foundry Models to analyze migration data, optimize queries, and predict performance outcomes. These innovations allow us to deliver more intelligent, adaptive solutions tailored to each customer’s unique environment.General Availability of Mirroring Azure Database for PostgreSQL in Microsoft Fabric Is Here!
Unlock Real-Time Analytics on Operational Data—Now Enterprise-Ready Few weeks ago at Microsoft Ignite 2025, we announced General Availability (GA) of Mirroring for Azure Database for PostgreSQL flexible server in Microsoft Fabric. This milestone marks a major leap forward in empowering organizations to seamlessly integrate their operational PostgreSQL data into the Microsoft Fabric analytics ecosystem—enabling near real-time analytics, machine learning, and business intelligence without the complexity of traditional ETL pipelines. Why Mirror Operational Databases in Microsoft Fabric? Accelerate Analytics Without ETL Fabric Mirroring eliminates the need for complex, custom ETL pipelines. Data from your operational PostgreSQL databases is continuously replicated into OneLake as Delta tables, making it instantly available for analytics, machine learning, and reporting. This means you can: Run advanced analytics and AI on live data without impacting production workloads. Empower data scientists to experiment and innovate with up-to-date data. Create real-time dashboards and cross-database queries for comprehensive business insights. Unify governance and security under OneLake, reducing risk and operational overhead. Enterprise-Grade Security and Compliance With support for Entra ID, VNETs, and Private Endpoints, organizations can enforce strict access controls and network isolation. Mirroring is designed to meet the needs of highly regulated industries, ensuring data privacy and compliance at every step. High Availability and Reliability The new HA support ensures that mirroring sessions remain resilient to failures, delivering uninterrupted analytics even during server failovers. This is essential for mission-critical applications where downtime is not an option. Cost Efficiency and Simplicity Mirroring is offered at no additional cost, dramatically reducing the total cost of ownership for analytics solutions. By removing ETL complexity, organizations can focus on extracting value from their data rather than managing infrastructure. What’s New in GA? Building on the momentum of our Public Preview, the GA release introduces several enterprise-grade enhancements: Microsoft Entra ID Authentication: Secure, centralized identity management for all mirroring operations. Entra ID authentication streamlines access control and compliance, making it easier for organizations to manage users and roles across their data estate. VNET and Private Endpoint Support: Mirroring now works with PostgreSQL Flexible Servers deployed behind Virtual Networks (VNETs) and Private Endpoints, ensuring secure, private connectivity with no public exposure. This is critical for regulated industries and enterprises with strict security requirements. High Availability (HA) Support: Mirroring is now compatible with HA-enabled servers, delivering business continuity and seamless failover for mission-critical workloads. For PostgreSQL 17+, replication slot failover ensures uninterrupted mirroring even during planned or unplanned outages. Performance and Reliability Enhancements: The replication engine has been optimized for smoother onboarding, improved error handling, and higher throughput—supporting initial snapshot rates up to ~1TB/hour and change data capture (CDC) with minimal latency (as low as 5 seconds under optimal conditions). For a full list of prerequisites and setup guidance, see the official documentation. Microsoft Entra ID Authentication Support for Entra ID database roles includes: Setting up initial replication Managing ongoing Fabric communications Enabling Entra ID authentication on Flexible Server Handling Entra ID roles within your databases Choosing your role type for Mirroring VNET and Private Endpoint Support This feature ensure secure and efficient connectivity for flexible servers within Microsoft Fabric. Connecting to a flexible server without public connectivity enhances security during both initial setup and ongoing operations. Establishing a Virtual Network Gateway on the target VNET facilitates encrypted traffic between networks, while subnet delegation allows specific resources within a subnet to be managed for specialized tasks. The system supports servers restricted by Virtual Network (VNET) and Private Endpoint configurations, enabling robust network isolation and protection from unauthorized access. High Availability (HA) Support Fabric Mirroring supports high availability by enabling seamless failover and enhanced fault tolerance for servers configured with HA. This feature requires PostgreSQL version 17 or later, as replication slot failover is only available in these versions. If you are using an earlier PostgreSQL version, you will need to manually reconfigure mirroring after each failover event to maintain replication. Beyond PostgreSQL: Interoperability Across Azure Databases Fabric Mirroring is not limited to PostgreSQL. The GA release also includes support for other databases like: SQL Server (2016–2025): Native mirroring for on-premises, Azure VMs, and non-Azure clouds, with secure connectivity and analytics-ready Delta tables. Snowflake: Mirroring for managed and Apache Iceberg tables, enabling high-performance analytics and open-format interoperability. Cosmos DB: Continuous change capture and mirroring for globally distributed NoSQL data, supporting real-time personalization, fraud detection, and IoT analytics. This interoperability allows organizations to consolidate data from diverse sources into OneLake, unlocking unified analytics and AI across their entire data estate. By leveraging shortcuts in Microsoft Fabric, customers can reference data stored in different mirrored databases and storage accounts as if it resided in a single location. This means users can build cross-database queries and analytics pipelines without physically moving or duplicating data, avoiding the need for complex ETL processes or data integration solutions. Shortcuts make it possible to seamlessly join, analyze, and visualize data from SQL Server, Snowflake, Cosmos DB, and more within OneLake, streamlining analytics workflows and accelerating time to insight while reducing storage costs and operational overhead. Getting Started Ready to experience the power of Mirroring for Azure Database for PostgreSQL Flexible Server in Microsoft Fabric? Step-by-step tutorial: https://learn.microsoft.com/azure/postgresql/flexible-server/concepts-fabric-mirroring#enable-fabric-mirroring-in-the-azure-portal Role-based access guidance: https://learn.microsoft.com/fabric/mirroring/azure-database-postgresql-tutorial#database-role-for-fabric-mirroring Monitor and troubleshoot: https://learn.microsoft.com/azure/postgresql/flexible-server/concepts-fabric-mirroring#troubleshooting Public documentation: Fabric Mirroring for Azure Database for PostgreSQL Future Enhancements Looking ahead to the next future, our focus will be on delivering a series of post-GA enhancements designed to make Mirroring for Azure Database for PostgreSQL Flexible Server even more robust, versatile, and user-friendly. Key advancements will be in the following areas: automatic replication for newly created database tables when operating in auto-mode, ensuring that your mirrored environments remain up to date with minimal manual intervention. enhanced support for advanced DDL operations, giving users greater flexibility and control when managing schema changes on mirrored databases. expanding compatibility with additional data types—such as JSON, arrays, ranges, and geometry—will open up new scenarios for analytics and data integration, accommodating a wider range of workloads and use cases. support for partitioned tables, TOAST tables, and views will allow organizations to mirror more complex database structures, further streamlining operational analytics. enable the ability to mirror databases hosted on Read Replicas, which will help organizations optimize their high-availability and scaling strategies without compromising data consistency. Collectively, these planned features underscore our commitment to continuous improvement and to meeting the evolving needs of our users as they harness the full power of Microsoft Fabric for unified data analytics and AI. Conclusion The General Availability of Mirroring for Azure Database for PostgreSQL Flexible Server in Microsoft Fabric represents a significant advancement for organizations seeking to unlock real-time analytics, AI, and BI on their operational data—securely, reliably, and without ETL complexity. With new enterprise features, proven customer success, and broad interoperability, now is the perfect time to bring your operational databases into the Microsoft Fabric analytics era. Learn more and get started today: Fabric Mirroring for Azure Database for PostgreSQLPostgreSQL for the enterprise: scale, secure, simplify
This week at Microsoft Ignite, along with unveiling the new Azure HorizonDB cloud native database service, we’re announcing multiple improvements to our fully managed open-source Azure Database for PostgreSQL service, delivering significant advances in performance, analytics, security, and AI-assisted migration. Let’s walk through nine of the top Azure Database for PostgreSQL features and improvements we’re announcing at Microsoft Ignite 2025. Feature Highlights New Intel and AMD v6-series SKUs (Preview) Scale to multiple nodes with Elastic Clusters (GA) PostgreSQL 18 (GA) Realtime analytics with Fabric Mirroring (GA) Analytical queries inside PostgreSQL with the pg_duckdb extension (Preview) Adding Parquet to the azure_storage extension (GA) Meet compliance requirements with the credcheck, anon & ip4r extensions (GA) Integrated identity with Entra token-refresh libraries for Python AI-Assisted Oracle to PostgreSQL Migration Tool (Preview) Performance and scale New Intel and AMD v6 series SKUs (Preview) You can run your most demanding Postgres workloads on new Intel and AMD v6 General Purpose and Memory Optimized hardware SKUs, now availble in preview These SKUs deliver massive scale for high-performance OLTP, analytics and complex queries, with improved price performance and higher memory ceilings. AMD Confidential Compute v6 SKUs are also in Public Preview, enabling enhanced security for sensitive workloads while leveraging AMD’s advanced hardware capabilities. Here’s what you need to know: Processors: Powered by 5th Gen Intel® Xeon® processor (code-named Emerald Rapids) and AMD's fourth Generation EPYC™ 9004 processors Scale: VM size options scale up to 192 vCores and 1.8 TiB IO: Using the NVMe protocol for data disk access, IO is parallelized to the number of CPU cores and processed more efficiently, offering significant IO improvements Compute tier: Available in our General Purpose and Memory Optimized tiers. You can scale up to these new compute SKUs as needed with minimal downtime. Learn more: Here's a quick summary of the v6 SKUs we’re launching, with links to more information: Processor SKU Max vCores Max Mem Intel Ddsv6 192 768 GiB Edsv6 192 1.8 TiB AMD Dadsv6 96 384 GiB Eadsv6 96 672 GiB DCadsv6 96 386 GiB ECadsv6 96 672 GiB Scale to multiple nodes with Elastic clusters (GA) Elastic clusters are now generally available in Azure Database for PostgreSQL. Built on Citus open-source technology, elastic clusters bring the horizontal scaling of a distributed database to the enterprise features of Azure Database for PostgreSQL. Elastic clusters enable horizontal scaling of databases running across multiple server nodes in a “shared nothing” architecture. This is ideal for workloads with high-throughput and storage-intensive demands such as multi-tenant SaaS and IoT-based workloads. Elastic clusters come with all the enterprise-level capabilities that organizations rely upon in Azure Database for PostgreSQL, including high availability, read replicas, private networking, integrated security and connection pooling. Built-in sharding support at both row and schema level enables you to distribute your data across a cluster of compute resources and run queries in parallel, dramatically increasing throughput and capacity. Learn more: Elastic clusters in Azure Database for PostgreSQL PostgreSQL 18 (GA) When PostgreSQL 18 was released in September, we made a preview available on Azure on the same day. Now we’re announcing that PostgreSQL 18 is generally available on Azure Database for PostgreSQL, with full Major Version Upgrade (MVU) support, marking our fastest-ever turnaround from open-source release to managed service general availability. This release reinforces our commitment to delivering the latest PostgreSQL community innovations to Azure customers, so you can adopt the latest features, performance improvements, and security enhancements on a fully managed, production-ready platform without delay. ^Note: MVU to PG18 is currently available in the NorthCentralUS and WestCentralUS regions, with additional regions being enabled over the next few weeks Now you can: Deploy PostgreSQL 18 in all public Azure regions. Perform in-place major version upgrades to PG18 with no endpoint or connection string changes. Use Microsoft Entra ID authentication for secure, centralized identity management in all PG versions. Enable Query Store and Index Tuning for built-in performance insights and automated optimization. Leverage the 90+ Postgres extensions supported by Azure Database for PostgreSQL. PostgreSQL 18 also delivers major improvements under the hood, ranging from asynchronous I/O and enhanced vacuuming to improved indexing and partitioning, ensuring Azure continues to lead as the most performant, secure, and developer-friendly PostgreSQL managed service in the cloud. Learn more: PostgreSQL 18 open-source release announcement Supported versions of PostgreSQL in Azure Database for PostgreSQL Analytics Real-time analytics with Fabric Mirroring (GA) With Fabric mirroring in Azure Database for PostgreSQL, now generally available, you can run your Microsoft Fabric analytical workloads and capabilities on near-real-time replicated data, without impacting the performance of your production PostgreSQL databases, and at no extra cost. Mirroring in Fabric connects your operational and analytical platforms with continuous data replication from PostgreSQL to Fabric. Transactions are mirrored to Fabric in near real-time, enabling advanced analytics, machine learning, and reporting on live data sets without waiting for traditional batch ETL processes to complete. This approach eliminates the overhead of custom integrations or data pipelines. Production PostgreSQL servers can run mission-critical transactional workloads without being affected by surges in analytical queries and reporting. With our GA announcement Fabric mirroring is ready for production workloads, with secure networking (VNET integration and Private Endpoints supported), Entra ID authentication for centralized identity management, and support for high availability enabled servers, ensuring business continuity for mirroring sessions. Learn more: Mirroring Azure Database for PostgreSQL flexible server Adding Parquet support to the azure_storage extension (GA) In addition to mirroring data directly to Microsoft Fabric, there are many other scenarios that require moving operational data into data lakes for analytics or archival. The complexity of building and maintaining ETL pipelines can be expensive and time-consuming. Azure Database for PostgreSQL now natively supports Parquet via the azure_storage extension, enabling direct SQL-based read/write to Parquet files in Azure Storage. This makes it easy to import and export data in Postgres without external tools or scripts. Parquet is a popular columnar storage format often used in big data and analytics environments (like Spark and Azure Data Lake) because of its efficient compression and query performance for large datasets. Now you can use the azure_storage extension to can skip an entire step: just issue a SQL command to write to and query from a Parquet file in Azure Blob Storage. Learn more: Azure storage extension in Azure Database for PostgreSQL Analytical queries inside PostgreSQL with the pg_duckdb extension (Preview) DuckDB’s columnar engine excels at high performance scans, aggregations and joins over large tables, making it particularly well-suited for analytical queries. The pg_duckdb extension, now available in preview for Azure Database for PostgreSQL combines PostgreSQL’s transactional performance and reliability with DuckDB’s analytical speed for large datasets. Together pg_duckdb and PostgreSQL are an ideal combination for hybrid OLTP + OLAP environments where you need to run analytical queries directly in PostgreSQL without sacrificing performance., To see the pg_duckdb extension in action check out this demo video: https://aka.ms/pg_duckdb Learn more: pg_duckdb – PostgreSQL extension for DuckDB Security Meet compliance requirements with the credcheck, anon & ip4r extensions (GA) Operating in a regulated industry such as Finance, Healthcare and Government means negotiating compliance requirements like HIPAA and PCI-DSS, GDPR that include protection for personalized data and password complexity, expiration and reuse. This week the anon extension, previously in preview, is now generally available for Azure Database for PostgreSQL adding support for dynamic and static masking, anonymized exports, randomization and many other advanced masking techniques. We’ve also added GA support for the credcheck extension, which provides credential checks for usernames, and password complexity, including during user creation, password change and user renaming. This is particularly useful if your application is not using Entra ID and needs to rely on native PostgreSQL users and passwords. If you need to store and query IP ranges for scenarios like auditing, compliance, access control lists, intrusion detection and threat intelligence, another useful extension announced this week is the ip4r extension which provides a set of data types for IPv4 and IPv6 network addresses. Learn more: PostgreSQL Anonymizer credcheck – PostgreSQL username/password checks IP4R - IPv4/v6 and IPv4/v6 range index type for PostgreSQL The Azure team maintains an active pipeline of new PostgreSQL extensions to onboard and upgrade to Azure Database for PostgreSQL For example, another important extension upgraded this week is pg_squeeze which removes unused space from a table. The updated 1.9.1 version adds important stability improvements. Learn more: List of extensions and modules by name Integrated identity with Entra token-refresh libraries for Python In a modern cloud-connected enterprise, identity becomes the most important security perimeter. Azure Database for PostgreSQL is the only managed PostgreSQL service with full Entra integration, but coding applications to take care of Entra token refresh can be complex. This week we’re announcing a new Python library to simplify Entra token refresh. The library automatically refreshes authentication tokens before they expire, eliminating manual token handling and reducing connection failures. The new python_azure_pg_auth library provides seamless Azure Entra ID authentication and supports the latest psycopg and SQLAlchemy drivers with automatic token acquisition, validation, and refresh. Built-in connection pooling is available for both synchronous and asynchronous workloads. Designed for cross-platform use (Windows, Linux, macOS), the package features clean architecture and flexible installation options for different driver combinations. This is our first milestone in a roadmap to add token refresh for additional programming languages and frameworks. Learn more, with code samples to get started here: https://aka.ms/python-azure-pg-auth Migration AI-Assisted Oracle to PostgreSQL Migration Tool (Preview) Database migration is a challenging and time-consuming process, with multiple manual steps requiring schema and apps specific information. The growing popularity, maturity and low cost of PostgreSQL has led to a healthy demand for migration tooling to simplify these steps. The new AI-assisted Oracle Migration Tool preview announced this week greatly simplifies moving from Oracle databases to Azure Database for PostgreSQL. Available in the VS Code PostgreSQL extension the new migration tool combines GitHub Copilot, Azure OpenAI, and custom Language Model Tools to convert Oracle schema, database code and client applications into PostgreSQL-compatible formats. Unlike traditional migration tools that rely on static rules, Azure’s approach leverages Large Language Models (LLMs) and validates every change against a running Azure Database for PostgreSQL instance. This system not only translates syntax but also detects and fixes errors through iterative re-compilation, flagging any items that require human review. Application codebases like Spring Boot and other popular frameworks are refactored and converted. The system also understands context by querying the target Postgres instance for version and installed extensions. It can even invoke capabilities from other VS Code extensions to validate the converted code. The new AI-assisted workflow reduces risk, eliminates significant manual effort, and enables faster modernization while lowering costs. Learn more: https://aka.ms/pg-migration-tooling Be sure to follow the Microsoft Blog for PostgreSQL for regular updates from the Postgres on Azure team at Microsoft. We publish monthly recaps about new features in Azure Database for PostgreSQL, as well as an annual blog about what’s new in Postgres at Microsoft.
