Answer-first summary52/100 GEO
TimescaleDB
Time-series database on PostgreSQL.
Your Citability Score
52/100Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
- Identity: Add: Founder / Team, Social links.
- Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
- Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
- Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
- Classification: Add: At least 2 tags.
Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.
Frequently asked questions
- What is TimescaleDB?
- Time-series database on PostgreSQL.
- What does TimescaleDB do?
- Time-series database built on PostgreSQL for fast ingest, complex queries, and hypertable auto-partitioning.
- Who is TimescaleDB for?
- Engineering teams, data platform teams, and developers building applications that monitor, analyze, or alert on time-stamped data at scale.
- Is TimescaleDB verified?
- TimescaleDB is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
TimescaleDB
InvitedTime-series database on PostgreSQL.
DatabaseCH-VER-695060Listed September 11, 2026
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
52/100
75
Identity45
Evidence15
Trust50
Freshness80
Classification64
Impressions
0
Clicks
0
Likes
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
Cite this Project
BibTeX
@misc{citablehub_timescaledb,
title = {TimescaleDB},
url = {https://citablehub.com/p/timescaledb},
note = {Listed September 11, 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.