To be fair, the skepticism wasn't without merits given the lengths TimescaleDB goes to make timeseries work. From their blog entries [0][1], it is evident that

To be fair, the skepticism wasn't without merits given the lengths TimescaleDB goes to make timeseries work. From their blog entries [0][1], it is evident that they essentially shoe-horn techniques from columnar stores like Apache Druid / Kudu, and file-types like Apache ORC / Parquet into Poatgres' row-based data-model. Reminds me of BigTable / HBase, in a way, too. TimescaleDB's biggest feat here is of course pulling the engineering magic rabbit out of the hat by chipping away at it for 4+ years, and effectively answering the skepticism by delivering on their promise. Note though, Amazon Redshift is built on Postgres, and (allegedly) so is Amazon Timestream. [0] https://blog.timescale.com/blog/building-columnar-compression-in-a-row-oriented-database/ https://blog.timescale.com/blog/building-columnar-compressio... [1] https://blog.timescale.com/blog/time-series-compression-algorithms-explained/ https://blog.timescale.com/blog/time-series-compression-algo...