Tiger Data Reviews & Product Details

- Timescale is a PostgreSQL extension, so the team was able to leverage all of our previous knowledge of PostgreSQL and standard SQL

- Hypertables and continuous aggregates deliver a massive performance boost for both data ingest and data queries

- Unlike many other time-series databases, which seem to be optimised purely for IoT-like use cases, Timescale was able to handle *mutable* time-series data.

- Active and helpful community (on Slack) Review collected by and hosted on G2.com.

- Managed hosting options (Timescale Cloud and MST) can get expensive, especially as resource requirements grow

- Difficult to retrieve logs & metrics for a specific date range via MST console Review collected by and hosted on G2.com.

I like very much the continuous aggregates and the jobs one can define to regularly update them. Review collected by and hosted on G2.com.

There isn't something I dislike. The online documentation is not perfect. I believe it has some room for improvement. Review collected by and hosted on G2.com.

I did not have to learn any groundbreaking technology to become an expert at analyzing time series data hosted with TimescaleDB. That in itself makes TSDB groundbreaking. Review collected by and hosted on G2.com.

You will end up putting TimescaleDB proprietary query logic into your system. There is no way around it unless you build your own custom interface against Timescale. Review collected by and hosted on G2.com.

Timescale has predictable cost anchored in familiar reality; it is driven by storage volume and system load. You don't have that familiar, awful cardinality problem that is common to _every_ tag-set-series data model system. It performs very well and predictably. It's really awesome.

It's open source and self-hosting is easy: It is postgresql. You already know what self-hosting implies from that one statement and whether you're willing to do it. If you're not, you can pay Timescale to do it for you with Timescale Cloud. In my experience, Timescale Cloud was very effective for the months my team used it.

Their community is great, and the Timescale maintainers actually address issues reported by the community (including me personally)! It was a welcome 180 degree change from the seemingly antagonistic stance certain other related open source projects take toward their community. Their people are really good. Review collected by and hosted on G2.com.

There's no well-defined guidance about how time series data should be generally modeled in Postgresql. There are helpful discussions about EAV and wide schemas, but up to now, Timescale seems to shy from taking a stance.

Also, ingesting data is a pain if you don't already have some postgresql tie-in for your service. It's not really the best way to ingest time series data from disparate service hosts though; you'll have connection count issues and weird back pressure. Upgrades become very difficult that way (just ask Promscale about that, RIP). I would love to see real direct RPC integrations with de-facto standards like opentelemetry (gag) and better standards like goodmetrics on the TimescaleDB host process itself. This would make TimescaleDB's time series ingest from service hosts perfectly seamless, and would establish common standards for data modeling. Review collected by and hosted on G2.com.

We loved the ease of installation and the familiarity of Timescale with PostgreSQL. It was easy to get started, and it has been easy to maintain the database. Most importantly, the ingestion rate is INSANE, even on a small server instance. The time_bucket() and time_bucket_gapfill() functions in queries make retrieval of our data a trivial issue, so we can focus on our business needs instead of lengthy development cycles. Also, Timescale maintains an active Slack channel where we can find the support we need. Review collected by and hosted on G2.com.

I'm wracking my brain to find anything I dislike about using TimescaleDB. The only issues we experienced during the implementation and upkeep of our self-hosted TimescaleDB instances have all been addressed either by small code changes or by the improved TimescaleDB version releases. Review collected by and hosted on G2.com.

We were evaluating technologies for hosting time series data and came across TimescaleDB and Timescale Cloud. We were able to get up and running in minutes, to be able to evaluate the technology, and we have been running it in production for the past year now.

The support has been stellar, and the service does exactly what we needed. We haven't had any issues with performance or downtime and currently host ~15 instances with Timescale Cloud. Review collected by and hosted on G2.com.

Some of the enterprise features around Disaster Recovery are still in development.

It can be tedious to size down databases after doing an initial historical load of data and compressing it. Review collected by and hosted on G2.com.

Timescale enabled us to reduce complexity in our codebase by using its built-in functions.

Achieved 50% cost savings while even improving performance.

Great docs; they not only help you to get a PoC running (where documentation typically starts to thin out) but also cover what you need to run in production.

Customer success team really lives up to its name. Got us access to engineers when it was necessary and helped to prioritise some features we needed. Review collected by and hosted on G2.com.

Backfilling data into already compressed chunks could be more performant Review collected by and hosted on G2.com.

- I have personally only had positive experiences from Timescale's support. They have been helpful and responsive in answering our questions and helping us optimize our instances, for example by setting up compression.

- Flexible and PostgreSQL based.

- Good documentation and open source. Review collected by and hosted on G2.com.

They do not offer the same functionality in their Managed and Cloud services. Unfortunately, a portion of the functionalty that would be useful for us is not available in Managed, and Coud is not available in our region. I know they are working on this so this might change in the future! Review collected by and hosted on G2.com.

- Performance for time series real time data processing.

- Relational database as a service -> less system skills and sysadmin tasks

- Support responsiveness Review collected by and hosted on G2.com.

- lack of superuser rights preventing the use of some extensions such as pgTap or pg_cron

- no easy solution to trigger processing outside the database. Review collected by and hosted on G2.com.

Basically no weird issues that one often finds with newish software. Haven't had to jump through strange SQL hoops, or weird commands. Stuff just works more or less like any old relational DB. Review collected by and hosted on G2.com.

Only odd thing is the get-size commands are not obvious. Since you are actually creating many tables, some of the commands on normal tables need something different. Review collected by and hosted on G2.com.