Tiger Cloud - Annual Commit
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Best time-series database
What do you like best about the product?
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!
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!
What do you dislike about the product?
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!
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!
What problems is the product solving and how is that benefiting you?
Extremely frequent data. We got 50 different values rolling in every second per "device". That is a lot of data for most databases, but Timescale is able to handle it with ease.
A performant time-series database built on the rock-solid Postgres DB, with stellar support to boot
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
The storage and analysis of large volumes of high-frequency financial tick data (market data). These data are the foundation of our analyses as an electronic trading quant team.
The Easiest, Fastest and Most Cost Effective Time Series Database - Period
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
We needed to find a time series database solution with a high ingestion rate due to the speed of telemetry data coming from our devices. The added benefit of the fact that the software is essentially free for the community edition is the icing on the cake.
Real time tracking app for watersport enthusiasts build on a time series & geo-spatial database.
What do you like best about the product?
- Performance for time series real time data processing.
- Relational database as a service -> less system skills and sysadmin tasks
- Support responsiveness
- Relational database as a service -> less system skills and sysadmin tasks
- Support responsiveness
What do you dislike about the product?
- 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.
- no easy solution to trigger processing outside the database.
What problems is the product solving and how is that benefiting you?
Ingesting, cleaning, contextualizing and visualizing in realtime a lot of navigation data coming from a lot of different sources.
This is a core technology, simply critical to grow our business.
This is a core technology, simply critical to grow our business.
A high quality time series database that is in production within minutes
What do you like best about the product?
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.
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
We need to store high volumes of time series data, compress this data, retain some but not all of it, have it searchable in an efficient way, and also aggregate the raw data into daily/hourly summaries. Timescale does all of that.
Easily extend Timescale to solve your problems.
What do you like best about the product?
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.
What do you dislike about the product?
To leverage the user-defined functions, I need to manage my own installation of Timescale and can't leverage one of the managed instances.
What problems is the product solving and how is that benefiting you?
As a data scientist, I spend much of my time performing feature engineering to extract information that will help my models perform better. This often requires me to process large amounts of data with a time component (such as panel data). Before using Timescale, I would store the data in Postgres, extract it to my Python environment, and have memory and performance issues. With Timescale, I have been able to push these calculations into the database generating significant performance improvements.
Postgres but faster
What do you like best about the product?
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
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
What do you dislike about the product?
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.
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.
What problems is the product solving and how is that benefiting you?
We are building an analytics product built specifically for the website building and hosting company Webflow. We are processing millions of events from various websites and turning them into insightful dashboards.
As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.
Like many other companies, Nocodelytics started with PostgreSQL. In the beginning, it worked. But the size of the database grew very, very fast. Eventually, with millions of rows, our dashboards became sluggish. Queries for customers with a lot of traffic would take several minutes or even time-out.
My first choice was ClickHouse, which seems to have better performance than Timescale for our use case—but keep reading as there's more to it.
Not everything was great about ClickHouse: It does a lot, which can get confusing, and I’d rather stick with PostgreSQL, which I’ve used for years and know works.
The best feature of TimescaleDB: it's all PostgreSQL, always has been. All your tools, all the existing libraries, and your code already work with it. I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster.
As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.
Like many other companies, Nocodelytics started with PostgreSQL. In the beginning, it worked. But the size of the database grew very, very fast. Eventually, with millions of rows, our dashboards became sluggish. Queries for customers with a lot of traffic would take several minutes or even time-out.
My first choice was ClickHouse, which seems to have better performance than Timescale for our use case—but keep reading as there's more to it.
Not everything was great about ClickHouse: It does a lot, which can get confusing, and I’d rather stick with PostgreSQL, which I’ve used for years and know works.
The best feature of TimescaleDB: it's all PostgreSQL, always has been. All your tools, all the existing libraries, and your code already work with it. I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster.
Timescale vastly improves the efficiency of our operations with time series data
What do you like best about the product?
Compression is an excellent tool for cost-saving while balancing functionality
What do you dislike about the product?
There is an issue with them transiting between two managed products, resulting in a mismatch of feature/location options, but I believe they are quickly resolving this.
What problems is the product solving and how is that benefiting you?
Scaling with IoT data has been greatly enhanced by our use of Timescale. Feature like time buckets and continuous aggregates really expand our ability to offer greater functionality
A migration we like to think back to
What do you like best about the product?
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.
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.
What do you dislike about the product?
Backfilling data into already compressed chunks could be more performant
What problems is the product solving and how is that benefiting you?
Storing a lot of IoT data, running analytics against it, and visualizing raw data on demand. Initally used MS SQL but had to write a lot of code for partitioning and some of the more complex queries. Timescale takes care of that for us now.
Flexible database service and g
What do you like best about the product?
- 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.
- Flexible and PostgreSQL based.
- Good documentation and open source.
What do you dislike about the product?
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!
What problems is the product solving and how is that benefiting you?
High data ingest and performant queries. With Timescale we can be flexible and it's quick to for example set up new aggregations for our use cases.
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