November 2025 Summaries

TimescaleDB's latest releases, 2.22 and 2.23, offer significant enhancements in performance, storage efficiency, and developer experience for time-series and event-driven systems. The updates include zero-config hypertables and automatic columnstore policies, simplifying the setup process while supporting high-speed analytics. Notable features in version 2.23 include compatibility with PostgreSQL 18, direct-to-columnstore inserts, and faster, trigger-free continuous aggregates. Version 2.22 introduces UUIDv7 partitioning, which combines timestamps with unique identifiers, enhancing event-driven analytics and query performance. Additionally, configurable sparse indexes and adaptive compression settings improve control over data organization, while multi-column SkipScan optimizes DISTINCT queries across large datasets. These advancements aim to make TimescaleDB a more flexible and efficient database solution, aligning PostgreSQL capabilities with modern data demands.

The text explores the limitations and challenges of using MongoDB as an architectural choice, illustrating how its initial flexibility and schema-less design can lead to technical debt and operational burdens as projects scale. It contrasts MongoDB's approach of adding features through bolt-on solutions with Postgres's integrated, foundational design, which has evolved through community-driven innovation to support a wide range of functionalities. The text highlights the operational ease and scalability of Postgres, which has become the preferred choice for many developers due to its ability to efficiently handle complex queries and workloads without incurring the same level of technical debt. Benchmarks demonstrate Postgres's superior performance and predictability compared to MongoDB, especially in transaction-heavy and analytical scenarios. The narrative concludes by positioning MongoDB as a short-term solution that struggles with long-term scalability, while Postgres is presented as a robust, compounding architecture that aligns with the strategic needs of modern software development.

Tiger Data, the company behind TimescaleDB and Agentic Postgres, has formed a strategic collaboration agreement with Amazon Web Services (AWS) to create a unified Postgres-based infrastructure catering to developers, IoT devices, and AI agents, announced on October 31, 2025. This partnership aims to integrate AWS services deeply, enhancing developers' ability to connect Postgres data to various AWS analytics and machine learning tools, such as Amazon Athena and Amazon SageMaker, through familiar SQL interfaces. By expanding joint market programs and technical enablement, the collaboration seeks to provide a scalable and production-ready architecture that supports real-time data streams, AI experimentation, and operational data consolidation, enabling enterprises to gain actionable insights without compromising on reliability or scalability. Tiger Data's Postgres platform is particularly adept at managing time-series and event data, while its Agentic Postgres allows AI agents to test and operate autonomously, ensuring safe and efficient experimentation.

APIs, while neutral in their design, often fall short in guiding models to make sound engineering decisions, leading to technical debt when executing commands. The current approach of using API wrappers assumes that models have the necessary judgment to write efficient code, which is not always the case. This challenge is being addressed by introducing AI Guides, which are designed to embed expert judgment and guide models through structured workflows, rather than merely providing access to tools. An example of this is pg-aiguide, a reasoning layer for Postgres that helps models design schemas by validating entities, checking for missing keys, and evaluating data types, among other tasks, thereby reducing the potential for errors and improving code quality. The industry trend is moving towards portable MCP tools that maintain consistent logic and quality across different models, ensuring that engineering standards are upheld regardless of the platform used. This shift from "dumb wrappers" to intelligent guides aims to build more reliable agents that can be trusted in production environments.

The text discusses the development of pg-aiguide, a tool designed to enhance AI coding agents' ability to generate high-quality, production-ready Postgres SQL by providing Postgres-specific judgment and best practices. The tool addresses the limitations of AI models trained on a wide array of SQL examples from various databases, which often results in suboptimal Postgres code. By incorporating curated skills and semantic search capabilities, pg-aiguide ensures that AI-generated code adheres to Postgres best practices, such as using appropriate data types and indexing strategies. This approach aims to mitigate common issues that arise when AI-generated SQL is "almost right" but not precise enough for efficient database management. The development of pg-aiguide is part of a broader effort to create a comprehensive Postgres guide for AI, leveraging community expertise to expand its library of skills and documentation. The tool is open-source and invites contributions from developers to refine and enhance its capabilities further.

The benchmark study conducted using RTABench, an open-source tool designed for evaluating real-time analytics performance, highlights the superior speed of Tiger Cloud over TimescaleDB and vanilla PostgreSQL on Azure infrastructure. Tiger Cloud exhibited a median query time of 390 milliseconds, notably faster than TimescaleDB's 1.4 seconds and vanilla PostgreSQL's 6 seconds, due to its comprehensive support for time-series features like continuous aggregates and compression. The study emphasized that Tiger Cloud's integration with Azure allows for efficient handling of real-time workloads, overcoming the limitations of the Apache 2 Edition of TimescaleDB on Azure Database for PostgreSQL. Despite the absence of support for Kusto Query Language in RTABench, Tiger Cloud's performance in running real-time workloads on Azure offers a compelling alternative for users needing enhanced speed and full feature support, especially for those familiar with PostgreSQL.

Tiger Cloud on Azure is a newly available fully managed service in the Azure Marketplace that offers the complete features of TimescaleDB natively within the Azure ecosystem, addressing key challenges faced by Azure developers in handling time-series workloads. This integration allows users to maintain their existing Postgres and Azure environment while achieving significantly improved performance, with queries running up to 200 times faster and storage reduced by 95% through native compression. The service provides advanced features such as hypercore columnar storage, incremental continuous aggregates, and hyperfunctions, enabling real-time analytics without the need to offload workloads or rewrite queries. Tiger Cloud fits seamlessly into existing Azure architecture, supporting the same data sources, ingest layers, and stream processing tools, while offering a straightforward upgrade path and various payment options, including Pay-As-You-Go and Annual Commit through the Azure Marketplace.

Mechademy, which monitors crucial assets for large energy companies, dramatically reduced its hybrid digital twin infrastructure costs by 87% after transitioning from MongoDB to Tiger Data, powered by TimescaleDB on AWS. Initially, Mechademy used MongoDB for its flexibility and rapid iteration capabilities, crucial for developing its Turbomechanica platform that fuses physics-based models with machine learning to create digital twins for industrial assets. However, as the need for scalable diagnostics grew, MongoDB's lack of native time-series data support led to operational and financial strain, with high CPU utilization and expensive maintenance. By switching to Tiger Data, Mechademy leveraged native time-series support, continuous aggregates, and compression to streamline data handling, resulting in a 50× increase in workload capacity and significantly reduced maintenance overhead. This strategic transformation not only cut costs and complexity but also allowed Mechademy to focus on delivering intelligence rather than managing infrastructure.