Relational databases are historically either OLTP like Postgres, MySQL, SQLite, etc; or OLAP like Vertica, Clickhouse, Greenplum, Redshift, etc.
The latter group is designed to analyze lots of data (calculating aggregations across billions of rows) and have developed features like storing data as columns, using compression, batch/vectorized processing, scaling out across multiple servers, and other techniques to get that performance. Timescale is an extension to Postgres that brings these capabilities to Postgres and is one of a very few relational databases that offer OLTP+OLAP in a single product.
The time-series niche is what they targeted first, and the product offers lots of useful features around time-related data, but it's also a generic analytical database offering at this point.
The latter group is designed to analyze lots of data (calculating aggregations across billions of rows) and have developed features like storing data as columns, using compression, batch/vectorized processing, scaling out across multiple servers, and other techniques to get that performance. Timescale is an extension to Postgres that brings these capabilities to Postgres and is one of a very few relational databases that offer OLTP+OLAP in a single product.
The time-series niche is what they targeted first, and the product offers lots of useful features around time-related data, but it's also a generic analytical database offering at this point.