Native column-oriented data warehouses designed for OLAP queries will always be faster. There are multiple alternatives from Clickhouse to Redshift that will be

Native column-oriented data warehouses designed for OLAP queries will always be faster. There are multiple alternatives from Clickhouse to Redshift that will be faster. Originally I didn't like Timescale because it didn't offer anything new but the product has improved greatly over the years. Today it's close on performance by using a custom column-oriented data layer that stores the actual chunks in PostgreSQL rows and has several time-related processing and analytical features (continuous aggregates, time bucketing, smoothing values, etc) that make it easier than doing it yourself in raw SQL. One of the big advantages is that it allows you to use Postgres which means you can continue to use it as your main OLTP operational database as well. This avoids a lot of complicated polyglot issues like syncing datasets or using different querying systems with different syntax. It's one of the better examples of using Postgres as a data platform rather than a simple database. There are other alternatives that combine this OLAP+OLTP functionality like Citus (another automatic sharding distributed database extension for Postgres), Vitess (automatic sharded mysql), TiDB (natively distributed mysql interface on top of key/value store), MemSQL (proprietary distributed mysql interface with ram-based rowstores and disk-based columnstores) and SQL Server (with hekaton column-stores, in-memory tables, and scale out).