Tiger Data Reviews & Product Details

Timescale is a powerful extension to Postgresql with special features for time series data storage and processing. It has proven very useful in our IoT projects, where the compression keeps disk usage to a minimum and the continuous aggregates give a very quick overview of the data. And since it's all Postgres - no need to learn a new query language.

The managed Timescale cloud service is a cost effective and stable alternative for us, since we don't have the resources to maintain the required infrastructure and installation. Added to this, there is a strong community and helpful support should one need guidance along the road to production. Review collected by and hosted on G2.com.

Well, not really a dislike, but at a first glance Timescale may possibly be perceived as an easy-to-use product where a few clicks of a button gives you an optimal setup. However, it's still a database with an extra layer of added functionality, which requires a few runs down various rabbitholes to utilize its full power. A helpful community, a responsive support and well-written docs are great aids during this exercise. Review collected by and hosted on G2.com.

I've been using Timescale for several months now, and I'm extremely impressed with its performance. The database excels in storing and retrieving time series data, making it an ideal choice for my work. One of its standout features is the ability to aggregate data, which has been incredibly useful for generating insights and reports. Additionally, the compression capabilities are quite powerful, enabling us to store a large amount of data without sacrificing performance.

But what really sets Timescale apart is its outstanding user support. Whenever I've had a question or issue, the team has been quick to respond and provide helpful solutions. Overall, I highly recommend Timescale to anyone in need of a reliable and efficient time series database. Review collected by and hosted on G2.com.

While Timescale's compression capabilities are powerful, some of the nuances and limitations can be a bit tricky to fully grasp at first. As a result, there may be a learning curve involved in leveraging the product to its fullest capabilities. However, once you become familiar with these nuances, Timescale can be a highly effective tool for managing and analyzing time series data. Review collected by and hosted on G2.com.

We recently migrated from self-hosted influxDB to Timescale Cloud and couldn't be happier. The transition was smooth and easy, and our engineers love the ability to use SQL instead of a custom query language. We've seen a significant performance increase just by using familiar SQL tricks. Review collected by and hosted on G2.com.

The only minor complaint we have is that the UI of the Cloud distribution, could use a bit more polishing, and that they are not yet listed on AWS marketplace. However, this hasn't affected the functionality or performance of the product, so it's not a significant issue. Review collected by and hosted on G2.com.

TimescaleDB is an extension of Postgres for time-series. As long-time Postgres users needing a time-series database, we viewed it as a great benefit that TimescaleDB is built on top of a tried and tested technology. In addition, we could continue to use ubiquitous SQL to perform our queries. The particular benefits of TimescaleDB include high compression ratios achieved through type-specific compression (we reached > 10x compression) along with much more performant time-series queries than standard Postgres. Finally, the suite of hyperfunctions in the TimescaleDB toolkit are particularly useful for our domain (high frequency financial tick data). The Timescale team has also been extremely helpful and supportive through the process of migrating to TimescaleDB. Review collected by and hosted on G2.com.

Migrating large volumes of data to the cloud (~100 TB uncompressed) is time-consuming and requires careful thought. That said, the Timescale team has been a great help to us in navigating this process. Review collected by and hosted on G2.com.

I use timescale cloud; it has been trivial to deploy into our production network (as well as our dev and staging networks).

All of the technical details are abstracted away, but you can get access to them if need be (such as server tuning, etc).

The ability to scale out at the click of a button is great, and the web-based metrics and alerting are also really useful from day one.

Performance seems incredibly good, even on the low-cost plans.

However, the most impressive feature has been the support, both with the personal customer service manager and the engineers' responsiveness and thoroughness (when I have needed to ask a technical question). The engineers are happy to answer questions about general design and best practices, as well as helping solve production issues. Review collected by and hosted on G2.com.

Timescale cloud is somewhat locked down, i.e. no direct superuser access, which can be a bit hard to get used to at first. However, it is workable - there's nothing I haven't been able to achieve so far using the standard cloud setup. Review collected by and hosted on G2.com.

As Timescale extends Postgres, managing both my time series and regular relational data in a single warehouse is effortless. In addition, Timescale's performance makes managing and working with that data much faster than other tools I've used. Finally, as it extends Postgres, I can easily extend its capabilities with its C-based user-defined functions. Review collected by and hosted on G2.com.

To leverage the user-defined functions, I need to manage my own installation of Timescale and can't leverage one of the managed instances. Review collected by and hosted on G2.com.

When I first started evaluating time series databases, Timescale was already on my list.

What I love about them is,

1. Natively built atop Postgresql, so one gets the best of both worlds

2. One can choose between their self-hosted, managed and cloud flavours

3. Excellent support and success teams that make sure you are set up, are good to go and help you with queries quickly

4. Excellent community, especially on slack, where you can ask/answer questions and support each other Review collected by and hosted on G2.com.

Sometimes the documentation is hard to navigate and get started with the samples. For example, the commands around routine jobs for continuous aggregates, how to check and manage them, etc. Again, this is if I were to be highly critical, but as I said earlier, they have a fantastic product and ecosystem. Review collected by and hosted on G2.com.

We’ve been using Timescale for a while now and I have to say, I’m impressed with their platform. They have a great and active community. Anytime I have a question or need help with something, I found someone to help me. The platform also has a lot of learning materials on their site and blog. I appreciate that they invest time and resources in educating their users, and I’ve learned a lot from their resources.

We were already familiar with postgres, so it was a natural fit for our business. The learning curve is very manageable. It has allowed us to keep scaling with minimal effort. All we had to do was add the timescale extension, and we were able to handle much more data with ease. This has been a game changer for our business.

It’s a great platform with a supportive community, excellent scalability, and plenty of learning materials to help you get started Review collected by and hosted on G2.com.

The compression feature in Timescale is not well explained, and it is difficult to update data after compression.

The managed hosting service offered by Timescale is expensive, which may not be feasible for small businesses or individuals.

If you are using hypertables in Timescale, you will lose foreign key constraints, which can be a significant limitation for some users.

Choosing Timescale over the more established and reliable option of PostgreSQL is a risky choice. However, if you do decide to go with Timescale, it should be relatively easy to revert back if necessary. Additionally, Timescale has raised a significant amount of funding, so it is likely to be around for a while. Review collected by and hosted on G2.com.

Uses SQL -> Super easy to get into

Time-series data -> We have tons of frequently generated data, and it is able to handle it with ease

Relational data -> One database to keep other data related/connected. Makes life extremely easy!

Support -> Top notch!

Pricing -> Not more than any other cheap database you could choose. Simply perfect! Review collected by and hosted on G2.com.

We have not come across anything that restricted us from making our cloud platform a success.

JSONB columns were a little bit slow when trying to do aggregations, so we had to change JSONB to another table structure, but this is just a limitation overall with any relational database, not specific to TimescaleDB! Review collected by and hosted on G2.com.

The fact that timescaledb is an extension of Postgres and integrates very well with our monitoring stack (OpenCensus) and since it is a SQL base timeseries, most of our developers find is easy to query data.

Hypertable, continuous aggregates provide a great way to speed up our customer-facing queries.

The compression functionality helped us to reduce our cloud cost by more than 50%.

If you are using Managed service or Cloud service, the support is very quick and helpful. Review collected by and hosted on G2.com.

There is no easy way to backfill historical data after compressing chunks, this will require a lot of custom code from our application and you must be careful when decompressing and updating aggregate to not impact the performance.

in general updating compressed chunks (Hypertable or Aggregates) is a bit painful and wish there is an easy way to update them without decompression. Review collected by and hosted on G2.com.