TimescaleDB, an open-source time-series database powered by PostgreSQL, has achieved significant momentum since its launch in April 2017. With over 1 million downloads and production deployments at companies like Bloomberg, Comcast, and Cray, TimescaleDB has proven itself to be fast, flexible, and scalable, making it a reliable choice for businesses with large-scale time-series data. The database's features, such as automatic space-time partitioning, hypertable abstraction layer, and adaptive chunk sizing, have enabled it to outperform other databases like InfluxDB, Cassandra, and MongoDB in performance benchmarks. As part of its development process, TimescaleDB has also released several new updates, including a scheduler framework, telemetry, and multi-extension support. The database is now officially releasing its first release candidate for TimescaleDB 1.0, which promises to bring even more features and improvements to the platform, making it an enterprise-ready solution for businesses looking for a performant, easy-to-use time-series database.