SQL made scalable for time-series data.

An open-source time-series database optimized for fast ingest and complex queries. Looks, feels, speaks like Postgres.

                  

How we scaled SQL

Time-series workloads are different. TimescaleDB introduces special partitioning and distributed query optimizations to unlock new possibilities for SQL.

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Horizontal scalability for high write rates, distributed query optimizations for performant complex queries.

Engineered up from PostgreSQL. Inherits 20+ years of reliability work and tooling.

Supports full SQL. Can connect with any client or tool that speaks to PostgreSQL.


An open-source database for time-series data

Scaling SQL for high ingest rates and complex queries

Engineered up from PostgreSQL. Packaged as an extension.

Rock-solid reliability

Trust in PostgreSQL’s 20-year open-source record and strong developer community

Mature ecosystem

Connect via standard ODBC, JDBC, or postgres for 3rd-party viz tools, BI tools, web platforms and ORMs

Standard interface

Leverage your team’s existing comfort with SQL and Postgres

Operational ease

Reuse known and trusted methods for backups, snapshots, replication, and other operational tasks

Connect third-party tools

via standard ODBC, JDBC, Postgres connectors

“We’re now using the data from Timescale heavily for our customer support operations. We’re really happy with it and are really benefitting from the service. Thank you!”

- Dan P., Kuna Systems

Business stories

  • Industrial Machine Learning

  • Smart Home

  • Transportation and Logistics

  • IoT Platforms

Industrial Machine Learning

Data scientists developing a predictive maintenance service at one industrial sensing company were faced with two problems: how to interactively sift through their raw data; and how to measure their model effectiveness over time. In particular, they wanted to compare the performance of various model iterations to measure improvement.

With TimescaleDB, not only are they now able to store their raw data, but also capture metadata related to the model’s performance: accuracy, latency, etc. Because TimescaleDB supports pure SQL, they use their existing tools to query the data. Our complex query support means they can monitor model effectiveness by cohort, providing a real-time machine learning dashboard.

Upcoming presentations

Strata Data Conf - When boring is awesome...

New York, NY, September 27, 2017

Time Series Predictions using...

Brno, Czech Republic, May 31, 2017

From IoT to AI: Applications...

Prague, Czech Republic, May 29, 2017

PGCon

Ottawa, Canada, May 25-26, 2017

Data Science with Time-Series...

Ljubljana, Slovenia, May 25, 2017

Past presentations

PyData Warsaw #10: Deep & Machine...

Warsaw, Poland, May 22, 2017

IoT World, Booth #1256

Santa Clara, CA, May 16-18, 2017

IoT NY Meetup

New York City, NY, May 11, 2017

A.I. Meetup

Warsaw, Poland, May 11, 2017

Percona Live - What the heck is...

Santa Clara, CA, April 27, 2017

Percona Live - Building a scalable...

Santa Clara, CA, April 26, 2017

Percona Live - Open Source Database...

Santa Clara, CA, April 25, 2017

GCT Engineering Talk Series

New York, NY, April 20, 2017

NYC PostgreSQL User Group

New York, NY, April 18, 2017

PostgreSQL Conference US

Jersey City, NJ, March 30, 2017

Eclipse Converge

San Jose, CA, March 21, 2017

O'Reilly Strata + Hadoop World

San Jose, CA, March 15, 2017