Answer-first summary58/100 GEO

TimescaleDB

Time-series database on PostgreSQL.

DatabaseFair citabilityVisit site →

Your Citability Score

58/100
Completeness13/25
Citable structure20/25
Freshness25/25
Verified claim0/25
What to improve to rank higher
  • Completeness: Fill in description, category, URL, logo, tags, and audience.
  • Citable structure: Add an answer-first summary, a deeper description, a citable outcome, and a valid URL.
  • Verified claim: Claim this project as its owner (and get verified) to earn full trust.

Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.

T

TimescaleDB

Invited

Time-series database on PostgreSQL.

DatabaseCH-VER-695060Listed July 27, 2026
Visit Website
AI-Extractable Summary
What:Time-series database on PostgreSQL.
For whom:Engineering teams, data platform teams, and developers building applications that monitor, analyze, or alert on time-stamped data at scale
Key outcome:Users can ingest, store, and query high-volume time-series data with faster analytics and simpler operations on PostgreSQL
Category:Database

Structured for AI systems to extract and cite.

Citability Score

49/100
60
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
24
Impressions
0
Clicks
0
Saves
0
GQI Earned

Citable Outcome

Users can ingest, store, and query high-volume time-series data with faster analytics and simpler operations on PostgreSQL.

About

Time-series database built on PostgreSQL for fast ingest, complex queries, and hypertable auto-partitioning.

Target Audience: Engineering teams, data platform teams, and developers building applications that monitor, analyze, or alert on time-stamped data at scale.
Not ideal for: Organizations that only need a simple transactional database with no time-series analytics, or teams unwilling to work within the PostgreSQL ecosystem.

What makes it different

  • Built on PostgreSQL, so teams get SQL, joins, and familiar tooling instead of a separate database paradigm.
  • Purpose-built time-series features such as hypertables, compression, and automated partitioning for large append-only datasets.
  • Supports both real-time ingestion and analytical queries in one system, reducing the need for separate OLTP and analytics stacks.
  • Extends PostgreSQL rather than replacing it, enabling relational data modeling alongside time-series workloads.

Tags & Classification

iot telemetryapplication monitoringfinancial market datadevops metricsevent analytics
backend engineersdata engineersplatform teamsdevops teams
saasiotfintechmanufacturing
Platform: PlatformModel: Open Source

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_timescaledb,
  title = {TimescaleDB},
  url = {https://citablehub.com/p/timescaledb},
  note = {Listed July 27, 2026. CitableHub ID: CH-VER-695060},
  year = {2026}
}
APA
TimescaleDB. (2026). CitableHub Software Index. https://citablehub.com/p/timescaledb.
MLA
"TimescaleDB." CitableHub, 2026, https://citablehub.com/p/timescaledb.