June 2025 Summaries

With AWS ceasing new user registrations for Amazon Timestream as of June 2025, existing users face a sunset period for the service, prompting discussions around migration options. The original Timestream for LiveAnalytics, launched by AWS in 2020, has seen limited updates, while a newer Timestream for InfluxDB, released in 2024, is based on the outdated InfluxDB OSS 2.x, lacking future development. Instead, migrating to TimescaleDB on Tiger Cloud is presented as a more robust and future-proof solution. TimescaleDB, an extension of PostgreSQL, offers high-performance time-series analytics with features like hypertables, compression, and continuous aggregates, all within a fully managed Tiger Cloud environment that integrates seamlessly with AWS services. Tiger Cloud promises ease of use, with automatic scaling, tiered storage, and deep observability, making it an attractive option for users seeking long-term stability and advanced analytical capabilities.

Kamil Ruczyński addresses the challenge of improving database performance for handling billions of rows of time-series data, initially stored in MySQL without partitioning, by exploring TimescaleDB, a PostgreSQL extension designed for time-series data. He highlights how TimescaleDB's features, such as automatic partitioning, retention policies, and continuous aggregates, can significantly enhance data retrieval speed and efficiency by pre-computing query aggregations and facilitating real-time data access. Ruczyński demonstrates the process of setting up and querying TimescaleDB, emphasizing the advantages of using its continuous aggregates to reduce query time dramatically, compared to MySQL, and discusses the use of retention policies to manage data storage efficiently. He concludes that while TimescaleDB adds complexity, it is a valuable tool for application engineers dealing with time-series data, particularly for those already using PostgreSQL, as it leverages familiar technology with added benefits.

The article delves into the integration of Bloom filters in TimescaleDB's columnstore to enhance the speed of point-lookups, particularly in scenarios involving large-scale time-series data and analytics workloads. It explains how traditional columnstores struggle with queries on unsorted fields due to the need to decompress and scan every block, a process that can be time-consuming. Bloom filters are introduced as a solution that allows the database to quickly determine whether a value is definitely not in a batch, thus significantly reducing the number of blocks that need to be scanned. This approach is particularly effective for queries involving non-temporal identifiers, such as UUIDs or transaction IDs, leading to performance improvements of up to 100x. The article further explains the mechanics of Bloom filters, including their efficiency and limitations, and highlights TimescaleDB's implementation of "blocked Bloom filters" which optimize performance by reducing I/O operations. While Bloom filters excel in exact match queries, they have limitations in handling range queries or not-equal searches, but they offer a substantial boost in speed for many common use cases without requiring manual configuration, aligning with TimescaleDB's goal of offering speed without sacrificing flexibility.

Tiger Data, formerly known as Timescale, has undergone a transformation from a time-series database to a modern PostgreSQL platform, designed to meet the needs of contemporary analytical and agentic workloads. Over the past eight years, the company has grown significantly, boasting 2,000 customers, substantial annual recurring revenue growth, and $180 million in funding. The renaming reflects its evolution rather than a change in mission, emphasizing its commitment to providing a highly performant database solution without sacrificing speed. Tiger Data serves a diverse range of customers, including major companies such as Nvidia, Tesla, and NASA, offering solutions for real-time analytics, AI-native applications, and more. The company continues to innovate with new features and enhancements, aiming to address the demands of modern applications that require real-time data processing, hybrid operational databases, and seamless integration with the Lakehouse architecture. Tiger Data's rebranding is a recommitment to its mission, inviting developers and customers to engage with its evolving database platform.

Products & Services Support Services for your use case, infrastructure, and budget are offered. A reliable PostgreSQL cloud is available for workloads, along with open-source extensions and tools. Time series and real-time analytics capabilities are also provided. Security features include a scanner, and support is available for demanding businesses. Various industries rely on this service, and documentation is available to learn about PostgreSQL. Benchmarks show how well Timescale performs against competitors.

This platform offers a range of products and services designed to support demanding businesses with reliable PostgreSQL cloud solutions, security scanners, and time series analytics. It also provides documentation and learning resources for those looking to improve their PostgreSQL skills. The platform is geared towards industries that rely heavily on databases, including those using hypertables or Timescale, which can help scale database performance by 2x. With a focus on security, reliability, and support, this platform aims to cater to the needs of businesses with complex use cases, infrastructure, and budget requirements.

This company offers a reliable PostgreSQL cloud service that caters to the needs of various businesses and workloads, including time series and real-time analytics. Their support services include security scanning and reliability assurance, making it suitable for demanding businesses. The company also provides documentation and tutorials on learning PostgreSQL, as well as articles and benchmarks on scaling database performance.

Products & Services Support Services Workloads Open-Source Extensions and Tools Time Series and Real-Time Analytics A reliable PostgreSQL cloud for your workloads AI and Vector Security Scanner Security, reliability, and support for demanding businesses Industries That Rely On Us Featured Articles Scale PostgreSQL via Partitioning: A Dev’s Intro to Hypertables Read more Boosting Postgres INSERT Performance by 2x With UNNEST Read more Documentation Learn PostgreSQL Learn the PostgreSQL basics and scale your database performance Timescale Benchmarks See how Timescale performs against the competition Category All posts Jun 02, 2025