(DevRel at Timescale) The resurfacing of this article today is particularly interesting as I work on a new blog post and video talking about managing all aspec

(DevRel at Timescale) The resurfacing of this article today is particularly interesting as I work on a new blog post and video talking about managing all aspects large-scale data, specifically geared towards time-series, but certainly applicable otherwise. Fast ingesting, compressing data, partitioning, pre-computed aggregates, etc. which shadows (unknowingly) a lot of the sound advice in this article. Nice work Petr! Timescale recognizes that time-series data is often a particular challenge in many of these areas, which is why we're primarily focused on bringing time-series superpowers to PostgreSQL. Sometimes we're fortunate enough to include enhancements like SkipScan[1] that work on any ordered index, regardless if time-series data. TimescaleDB addresses many of these scaling issues by providing functionality to auto-partition data, natively compress it into columnar form (which often improves historical queries saves upwards of 97% of storage), create continuous aggregates to pre-compute data for reporting and downsampling, set up easy data-tiering functionality, data retention, and straightforward horizontal scaling for time-series data. It's great to see confirmation of many of the features within a related context. Thanks again for the article and getting it in front of folks today. [1]: https://blog.timescale.com/blog/how-we-made-distinct-queries-up-to-8000x-faster-on-postgresql/ https://blog.timescale.com/blog/how-we-made-distinct-queries...