January 2021 Summaries

We're excited to announce Timescale Analytics, a new project that aims to combine all the capabilities SQL needs for time-series analytics into one Postgres extension. This will enable every software developer to store, analyze, and build on top of their time-series data, making it easier to measure what matters in various domains such as IoT devices, IT systems, marketing analytics, user behavior, financial metrics, and more. The project is focused on identifying, building, and combining the necessary functionality for SQL to perform time-series analysis into a single extension, which will be a "one stop shop" for time-series analytics in PostgreSQL. We're looking for feedback from the community on what analytical functionality would be most useful. The project is built as a PostgreSQL extension, allowing for separate development and experimentation without affecting the core TimescaleDB extension. By building this extension separately, we can reduce barriers for community contributions and make it easier for developers to get involved.

Timescale Forge is a feature-rich platform for managing time-series data, offering features like DIY multi-node support, new parameter config features, and exciting updates to TimescaleDB 2.0. The platform provides resources such as tutorials, documentation, and virtual sessions to help developers make the most of their time-series data. Additionally, the community is actively involved in sharing educational resources and participating in discussions through channels like Slack and Twitter. New content includes benchmarking results comparing TimescaleDB to Amazon Timestream, PostgreSQL optimization tips, and a new Rust tool for evaluating TimescaleDB ingest performance. The platform also offers office hours for Q&A sessions with engineers and community members. Overall, Timescale Forge aims to support developers in using time-series data effectively.