September 2021 Summaries

Time-series data is ubiquitous, driving decision-making across various industries. Developers collect vast amounts of time-series data and struggle with basic analysis due to the lack of specialized tools and skills. Excel, R, and Python are commonly used for data analysis but are not optimized for speed or efficiency, particularly when dealing with large datasets. A database built specifically for time-series data can alleviate these issues. TimescaleDB, a relational database for time-series data, offers continuous aggregates, compression, and hyperfunctions, allowing developers to perform data munging tasks directly within the database, reducing the need for additional tools like Excel or Python. This enables efficient management of scripts, easy adoption of new technologies, and improved collaboration among team members. By leveraging TimescaleDB functionality, developers can streamline their data analysis workflow and tackle complex problems more effectively.

The TimescaleDB team has reached 7K+ members on their Slack channel, celebrating this milestone with updates, new resources, and community engagement. Recent releases include TimescaleDB 2.4 with an experimental schema, new SQL functions for simplifying time-series data analysis, and support for Prometheus multi-tenancy in Promscale. The team has also launched a new interactive demo on Timescale Cloud and released educational resources such as tutorials and blog posts on topics like time-weighted averages and PostgreSQL aggregation. Additionally, the team is hosting virtual sessions and events to help developers get started with their data and sharing community spotlights and tips for using TimescaleDB effectively.