Watch the webinar recording to learn more about how to create a fast and cost effective repository for your historical monitoring data.

Last week, we hosted the Using DevOps Monitoring Data to Improve System Stability webinar. The purpose of this webinar was to showcase the importance of leveraging historical data to better determine the stability and the performance of the application you are monitoring.

During the course of the webinar, we focused on:

  • Real-time monitoring (the what, why, and how)
  • Leveraging monitoring metrics to improve long system stability
  • How to use Prometheus and TimescaleDB together

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Finally, at the end of the webinar we received questions from attendees. Here is a selection plus some additional tidbits:

Why should you use TimescaleDB over just Postgres for storage?

Great question. TimescaleDB is built on top of Postgres and is designed to accommodate the high velocity of data produced by time-series workloads. Essentially, TimescaleDB is able to handle time-series data better than vanilla Postgres. You can learn more from this blog.

Given the examples you used, can you do more advanced analysis on this data?

Absolutely! The examples in the webinar are just a small scale demonstration of what you can build. You can use the same foundation to build a more advanced system.

I’m concerned about storage costs. How could you help manage this?

We have some good news for you! In TimescaleDB 1.5 (scheduled for release later this year), we are introducing native compression which will help you significantly reduce storage costs.

Do you have a cloud offering or can I set this up in the cloud myself?

Yes, both! Our own managed cloud offering, Timescale Cloud, supports the pg_prometheus extension. There are several other cloud providers (AWS, GCP, Azure, Digital Ocean) that have the OSS version of TimescaleDB available and you can use the extension here as well.

Is TimescaleDB open source and where can I go for support?

Yes! The core of TimescaleDB is open source and you can check it out on GitHub. For support, we recommend joining our active Slack community of 3,000 users and posting any questions you might have there.  

We hope you enjoyed learning about historical monitoring data! If you have other questions, feel free to leave a comment in the section below.