Mike Freedman - Timescale Blog

Achieving the best-of-both worlds: Ensuring up-to-date results with Real-Time Aggregation

Real-time aggregates (released with TimescaleDB 1.7) build on continuous aggregates' ability to increase query speed and optimize storage. Learn what's new, details about how they work, and how to get started.

Blog

CTO Corner: this month in data (April 2020 edition)

Mike Freedman, Timescale CTO, shares some of the most interesting articles and papers about databases and distributed systems he's found in the past month.

Engineering

Building columnar compression in a row-oriented database

How we achieved 91%-96% compression in the latest version of TimescaleDB

Engineering

Building a distributed time-series database on PostgreSQL

Today we are announcing the distributed version of TimescaleDB, which is currently in private beta (public version slated for later this year).

Product

TimescaleDB vs. InfluxDB: Purpose built differently for time-series data

An in-depth look into how two leading time-series databases stack up in terms of data model, query language, reliability, performance, ecosystem, operational management, and company/community support.

Product

TimescaleDB 1.2: Analytical functions, automated data lifecycle management, improved performance, and more

Milestone release includes open-source improvements as well as our first community and enterprise features

Product

TimescaleDB 1.0 is production ready

Since announcing our first release candidate in September, Timescale’s engineering team has merged over 50 PRs to harden the database, improving stability and ease-of-use.

Engineering

Time-series data: Why (and how) to use a relational database instead of NoSQL

These days, time-series data applications (e.g., data center / server / microservice / container monitoring, sensor / IoT analytics, financial data analysis, etc.) are proliferating.