Why Use TimescaleDB over NoSQL?
Compared to general NoSQL databases (e.g., MongoDB, Cassandra) or even
more specialized time-oriented ones (e.g., InfluxDB, KairosDB),
TimescaleDB provides both qualitative and quantitative differences:
- Normal SQL: TimescaleDB gives you the power of standard SQL
queries on time-series data, even at scale. Most (all?) NoSQL
databases require learning either a new query language or using
something that's at best "SQL-ish" (which still breaks compatibility
with existing tools).
- Operational simplicity: With TimescaleDB, you only need to manage one
database for your relational and time-series data. Otherwise, users
often need to silo data into two databases: a "normal" relational
one, and a second time-series one.
- JOINs can be performed across relational and time-series data.
- Query performance is faster for a varied set
of queries. More complex queries are often slow or full table scans
on NoSQL databases, while some databases can't even support many
- Manage like PostgreSQL and inherit its support for varied datatypes and
indexes (B-tree, hash, range, BRIN, GiST, GIN).
- Native support for geospatial data: Data stored in TimescaleDB
can leverage PostGIS's geometric datatypes, indexes, and queries.
- Third-party tools: TimescaleDB supports anything that speaks
SQL, including BI tools like Tableau.
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