Practical Database Engineering Newsletter: June 2025 First Half Edition

Welcome to your bi-weekly dose of practical database engineering insights! The world of data is evolving at an incredible pace, with cloud providers and the open-source community pushing the boundaries of what's possible. In this edition, we'll dive into the latest innovations from Azure, AWS, and GCP, explore how AI is fundamentally reshaping database strategy, and share actionable tips to keep your data operations efficient and secure.

What's Inside this Edition:

• Cloud Provider Database Updates: The latest features and enhancements from Microsoft Azure, Amazon Web Services, and Google Cloud Platform.
• Feature Article Deep Dive: Unpacking how AI is revolutionizing databases and strategic recommendations.
• Quick Tips & Best Practices: Actionable advice for resilience, performance, and cost management.
• Industry Insights & Trends: Key technological shifts and market developments.
• Community Spotlight: Upcoming events and valuable resources.

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Disclaimer:

1) The following article is based on publicly available documentation as of June 2025 and is intended to provide a high-level overview for architectural planning.

2) The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of Microsoft. The author is a Microsoft employee.

I. Cloud Provider Database Updates

The past few weeks have seen significant advancements from the major cloud providers, enhancing capabilities across performance, AI integration, and operational efficiency for PostgreSQL and other database services.

Microsoft Azure

Azure continues to solidify PostgreSQL as a strategic platform, with new features aimed at simplification, security, and AI enablement.

• In-place Major Version Upgrade to PostgreSQL 17 (Public Preview): This feature allows seamless upgrades from PostgreSQL versions 11, 12, 13, 14, 15, or 16 directly to version 17 without requiring changes to your server endpoint, application reconfigurations, or manual data migration. This significantly minimizes downtime and operational complexity, ensuring your organization can stay current with the latest PostgreSQL capabilities and community support, which extends until November 2029 for PG17.
• Azure Data Factory Managed Identity Support (Generally Available): You can now use both user-assigned and system-assigned managed identities for Entra authentication when connecting your Azure Database for PostgreSQL instance with Azure Synapse Analytics and Azure Data Factory. This streamlines and enhances the security of your data integration pipelines by enabling passwordless management and simplifying identity management, removing the need to handle secrets.
• pg_cron Extension in PG 17 (Generally Available): The pg_cron extension, a powerful, built-in job scheduler using standard cron syntax, is now officially supported in PostgreSQL 17 on Azure Database for PostgreSQL Flexible Server. This simplifies the automation of routine database maintenance and tasks directly within the PostgreSQL environment, improving operational efficiency and reducing reliance on external scheduling tools.

Amazon Web Services (AWS)

AWS is pushing the boundaries of scalability and availability, particularly with its Aurora offerings and new open-source contributions.

• Amazon Aurora DSQL Generally Available: AWS has announced the general availability of Amazon Aurora DSQL, a serverless, distributed SQL database designed for virtually unlimited scalability and high availability. Its active-active distributed architecture boasts 99.99% single-Region and 99.999% multi-Region availability with strong consistency, allowing independent scaling of reads, writes, compute, and storage. This offers effortless scaling and resilience for always-available applications.
• Open Sourcing pgactive: Active-Active Replication Extension for PostgreSQL: AWS has open-sourced pgactive, a PostgreSQL extension for active-active replication. Building on PostgreSQL's logical replication features, pgactive enables asynchronous active-active replication, supporting writers in different regions and simplifying multi-active instance scenarios. This provides enhanced resiliency and flexibility for data movement and availability.
• Amazon Aurora now supports PostgreSQL Major Version 17: Amazon Aurora now supports PostgreSQL 17.4, bringing community improvements alongside Aurora-specific enhancements. These include enhanced memory management, faster storage metadata initialization during failovers, and optimizations for write-heavy workloads on new Graviton 4 instances. The update also includes key extensions like pgvector 0.8.0 and PostGIS 3.5.1, crucial for modern AI and geospatial applications.

Google Cloud Platform (GCP)

GCP is focusing on AI-assisted development, enhanced analytical capabilities, and significant performance improvements for its database services.

• New MCP Integrations to Google Cloud Databases for AI-Assisted Development: Google Cloud has announced expanded capabilities for its MCP (Model Context Protocol) Toolbox for Databases. This open-source server allows generative AI agents to connect directly to various Google Cloud databases (including Cloud SQL for PostgreSQL, AlloyDB, Spanner, BigQuery) within developers' IDEs. Developers can use natural language prompts to perform tasks like code generation, schema design, refactoring, and data exploration, accelerating the development lifecycle from days to minutes.
• Bigtable Spark Connector Generally Available with Apache Iceberg Support: The Bigtable Spark connector is now generally available, offering direct read and write capabilities for Bigtable data using Apache Spark. This integration includes query optimizations like join pushdowns and dynamic column filtering, with enhanced support for Apache Iceberg. It accelerates data science workflows by allowing direct interaction with operational data for ML model training and low-latency serving of predictions without impacting production applications, especially when combined with Bigtable Data Boost.
• Bigtable Single-Row Read Throughput Improved by 70%: Bigtable has significantly increased its single-row read throughput, now supporting up to 17,000 point reads per second (a 1.7x improvement) while maintaining low latency. These gains are attributed to improved row caching, more efficient single-row read operations, and smarter scheduler improvements, including user-configurable request prioritization for hybrid transactional/analytical processing (HTAP) workloads. This means each Bigtable node can handle 70% more traffic, improving cluster efficiency and reducing costs.

II. Feature Article Deep Dive: Unlocking the Future: How AI is Reshaping Database Engineering and Strategy

Artificial intelligence is no longer a futuristic concept but a core driver of modern business value, and databases are rapidly becoming its strategic foundation. The POSETTE 2025 briefing highlighted a clear trend: databases are transforming into intelligent engines for enterprise innovation, with deep integration and enhancements from cloud providers.

Key Technical Points:

• Vector Databases & Advanced Indexing: The pgvector extension is transforming PostgreSQL into a "great vector database," enabling applications to understand meaning and context through numerical representations of documents (vectors). This powers semantic search and applications like intelligent chatbots. Breakthroughs like Microsoft Research's DiskANN are providing state-of-the-art vector indexing, significantly outperforming previous methods like HNSW and IVFFlat for efficient vector search at any scale, especially crucial for demanding AI workloads.
• LLM & Graph Integration: Beyond traditional data types, PostgreSQL is expanding to support advanced AI models directly. The new azure_ai extension allows embedding Large Language Model (LLM) reasoning power directly into SQL queries, enabling functionalities like text generation and semantic re-ranking. Furthermore, direct graph capabilities via extensions like Apache AGE enable sophisticated GraphRAG (Retrieval Augmented Generation) architectures. This allows for the extraction of relationships and entities from data to form graph queries, which can significantly improve the accuracy of information retrieval and address common LLM hallucination problems.
• AI-Assisted Development: A significant shift is the rise of AI-assisted development for databases. Google Cloud's MCP Toolbox for Databases connects generative AI agents directly to databases, integrating with IDEs like VS Code. This allows AI assistants to understand database schemas and context, enabling them to write complex SQL queries from natural language prompts, design new schemas, refactor existing code when data models change, and even generate test data.

Key Business Value Points:

• Accelerating AI Initiatives: By embedding vector search and LLM capabilities directly into the database, organizations can rapidly build and scale AI-driven applications like smarter product recommendations, intelligent chatbots, and sophisticated fraud detection systems. This integration makes the power of AI accessible to all enterprises securely and at scale.
• Enhanced Productivity & Innovation: AI-assisted development tools dramatically accelerate the database development lifecycle. Tasks that previously took days or more for a developer, even those familiar with complex SQL, can now be completed in minutes using natural language commands. This frees up valuable engineering resources to focus on higher-level innovation rather than repetitive coding or schema management tasks.
• Improved Data Utilization and Accuracy: AI allows applications to understand the meaning and context of data, not just keywords, leading to more relevant answers and insights. GraphRAG specifically enhances the accuracy of LLM responses by providing structured, factual context from proprietary data, overcoming common issues like hallucinations. This turns data into a true competitive advantage by making it more accessible and intelligently leverageable.

In essence, databases are no longer just storage repositories; they are becoming strategic platforms for growth, offering a clear path to consolidating diverse workloads, unlocking next-generation AI applications, and providing the enterprise-grade security and intelligent management layers that leaders demand.

III. Quick Tips & Best Practices

Here are some actionable tips to optimize your database operations, enhance security, and streamline development:

• Prioritize Identity Management for Security: For PostgreSQL on Azure, prioritize Azure Entra ID (formerly Azure AD) authentication over traditional PostgreSQL authentication for passwordless management. This simplifies identity management, a critical component of enterprise security, by allowing you to map Entra ID users directly to PostgreSQL roles. Additionally, for compliance, standardize your development vs. production deployments (avoiding burstable tiers for production) and apply resource locks to prevent accidental modifications or deletions.
• Leverage Automated Index Tuning for Performance: Don't guess about index optimization. Platforms like Azure Database for PostgreSQL are developing features that provide automated index recommendations by analyzing query performance and identifying opportunities to speed up queries, even estimating the "hypothetical improvement percentage". Activating these features can significantly reduce query execution time.
• Implement Robust Backup and DR Strategies: Ensure proper point-in-time retention (up to 35 days in Azure Database for PostgreSQL Flexible Server by default) and consider long-term backup strategies for compliance, retaining data for up to 10 years. Regularly test your backup and restore procedures to validate their effectiveness. For multi-tenant applications, Row-Level Security (RLS) in PostgreSQL and Citus can restrict data visibility based on user identity or other criteria, crucial for data isolation.
• Optimize for Operational Efficiency with Monitoring and Pooling: Continuously monitor your PostgreSQL and OS logs for unusual errors, and use tools like pg_psi for fine-tuned observability and early detection of resource bottlenecks (CPU, I/O, memory pressure). For high connection loads and reduced latency, always use connection poolers like PgBouncer. This is crucial for managing database resources efficiently, especially in read-heavy scenarios.
• Embrace AI-Assisted Development Tools: Explore new tools like Microsoft's VS Code Extension for PostgreSQL, which integrates database tools directly into your development environment. Its deep Copilot integration allows for natural language interaction to query and modify databases, generate SQL, create databases, import data, and even build FastAPI servers and frontends, drastically improving developer productivity.

IV. Industry Insights & Trends

The database landscape is undergoing a profound transformation, driven by innovative architectural patterns and strategic partnerships.

• Emerging Technology: Serverless PostgreSQL: A significant trend is the rise of truly serverless PostgreSQL offerings. Companies like Neon are pioneering this by fundamentally decoupling storage from compute. Their architecture uses a proxy and control plane to enable instances to start and shut down on demand, achieving sub-second cold start times by pooling pre-formed VMs. This approach allows for independent scaling of compute and storage, making read scaling particularly efficient as multiple read-only replicas can share the same storage layer without data duplication. This minimizes local state and offers unprecedented flexibility and cost-efficiency for fluctuating workloads.
• Market News: Databricks Announces Strategic AI Partnership with Google Cloud: A major development highlighting the convergence of data and AI is the new strategic product partnership between Databricks and Google Cloud. This collaboration brings the latest Gemini models natively to the Databricks Data Intelligence Platform. This means organizations can now build, deploy, and scale AI agents securely on their proprietary enterprise data directly within their Databricks environment. This partnership addresses the challenge of fragmented AI deployments by offering a seamless and unified governance model, with Gemini models accessible directly through SQL queries and model endpoints, eliminating data duplication. According to Databricks CEO Ali Ghodsi, this "agentic" AI era, where databases are increasingly created by agents rather than humans, is poised to disrupt the entire database industry.

V. Community Spotlight / Upcoming Events

The PostgreSQL community and cloud providers continue to offer a wealth of learning and networking opportunities.

• POSETTE 2025 Briefings and YouTube Playlist: The recent POSETTE 2025 briefing provided a comprehensive overview of PostgreSQL's evolution and innovation, showcasing its deep integration with Microsoft Azure as a core business strategy. The event highlighted how PostgreSQL is being extended for diverse workloads, activating enterprise AI with pgvector, and offering enterprise-grade security and intelligent management on Azure. Many of the detailed technical talks and presentations from POSETTE 2025 are available for viewing on the official YouTube playlist.
• AWS Summits and re:Inforce: AWS continues its global Summit Season, offering free in-person events with keynotes, technical sessions, demos, and workshops across major cities in June. Additionally, AWS re:Inforce is scheduled for June 16–18 in Philadelphia, PA, focusing on AWS security solutions, cloud security, compliance, and identity. These events are excellent opportunities to connect with cloud experts and stay updated on the latest innovations.

VI. "Food for Thought" & Call to Action

As databases continue to evolve with AI integration, new architectural paradigms, and increasingly complex workloads, what do you believe is the single most critical skill for database professionals to develop to stay relevant and contribute strategically?

Thank you for reading this edition of the Practical Database Engineering Newsletter! We hope these updates provide valuable insights for your strategic decisions. If you found this content helpful, please like, share, or leave a comment. Subscribe to stay informed on the latest in database engineering.

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Sources used in this Edition:

1. Azure Updates - Databases - June 2025
2. What's New with AWS
3. Google Cloud Database News
4. POSETTE Conference 2025