# Tiger Data Reviews **Vendor:** Tiger Data (creators of TimescaleDB) **Category:** [Time Series Databases](https://www.g2.com/categories/time-series-databases) **Average Rating:** 4.6/5.0 **Total Reviews:** 33 ## About Tiger Data Tiger Data, from the creators of TimescaleDB, is the #1 Postgres time-series database for developers, devices and agents. Keep sensor, on-chain, and customer data fresh while retaining years of history, all queryable in standard SQL. For IoT, Web3, and AI. Why teams choose Tiger Data: - Trusted by thousands of developers. 3M+ active databases, 2k+ customers - Up to 95% Compression. Keep years of history online at a fraction of the cost. - Production-ready without the operational pain. Multi-AZ HA, PITR, cross-region backups, SOC 2/HIPAA/GDPR, deep observability. - Scale effortlessly. Disaggregated compute & storage. Never pay for idle capacity. - Unified data architecture. Connect any data source and automatically sync it between your operational database, and data lake. - Hyperscaler procurement. Available on AWS Marketplace and Azure Marketplace. Key capabilities: - Automatic partitioning Ingest millions of data points per second without manual table management or sharding. - Incremental materialized views Precompute and cache rollups for instant dashboards and APIs. - Hybrid row/column storage Fast writes, compressed reads, optimized for real-time and historical queries. - Compression (up to 95%) Columnar encodings apply filters & aggregates directly on compressed data for faster queries and big savings. - Tiered Storage Automatically move older or less-frequently accessed data to low-cost object storage while keeping it fully queryable through the same SQL interface. - Fully managed Postgres Cloud Scale compute and storage independently, tier S3 storage to manage costs, deploy globally, and skip database ops. Industry verticals: Developers and platform teams in Industrial IoT, manufacturing, Crypto, SaaS/ML and DevOps tooling rely on Tiger to combine operational and historical data for real-time dashboards and mission-critical insights, queryable in standard-SQL. How to get started: Try Tiger Cloud for 1-month free with no credit card needed, or use us indefinitely as part of our free plan. Get started now - https://console.cloud.timescale.com/signup?utm\_source=g2&utm\_medium=referral&utm\_campaign=free-trial-g2 ## Tiger Data Pros & Cons **What users like:** - Users value the **clean and intuitive UI** of Tiger Data, making navigation and analytics effortless. (8 reviews) - Users find the **easy setup** of Tiger Data enables rapid launch and management of complex analytical tasks effortlessly. (5 reviews) - Users highlight the **extremely fast setup** of Tiger Data, making it easy to start and scale workloads quickly. (5 reviews) - Users appreciate the **ease of access, analysis, and visualization of data** that Tiger Data offers. (4 reviews) - Users commend the **impressive performance** of Tiger Data, enhancing data analysis and decision-making with speed and accuracy. (4 reviews) - Speed (4 reviews) - Users love the **ease of setup and user-friendly cloud interface** of TigerData, enhancing their database management experience. (3 reviews) - Users praise the **excellent customer support** at TigerData, noting helpful resources and an active community for assistance. (3 reviews) - Dashboard Usability (3 reviews) - Users value the **easy integrations** with various plugins, facilitating swift setup and management of databases. (3 reviews) **What users dislike:** - Users find the **pricing to be expensive** , particularly for smaller projects or startups, affecting budget flexibility. (4 reviews) - Users find the **expensive licensing** of Tiger Data burdensome, particularly when budgeting for longer-term projects. (3 reviews) - Users often face **missing features** , such as advanced visualizers and incomplete Terraform support, limiting functionality and convenience. (3 reviews) - Users often find the **UI to be slow and cumbersome** , particularly when handling large datasets or many tables. (3 reviews) - Users report **slow performance** in the UI when handling large volumes of data, hindering overall efficiency. (3 reviews) - Latency Issues (2 reviews) - Performance Issues (2 reviews) - Poor UI Design (2 reviews) - Slow Loading (2 reviews) - Complexity (1 reviews) ## Tiger Data Reviews ### 1. Efficient and powerful database platform for scalable analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** September 26, 2025 **What do you like best about Tiger Data?** TigerData stands out for its extremely fast setup, reliable ingestion speeds, and intuitive cloud interface, making it easy to start and scale even complex analytical workloads. Its full PostgreSQL compatibility—plus handy vector database tools—enables seamless migrations and flexible querying without the need to learn new languages or disrupt existing workflows. Active community support through Discord and Slack, plus robust documentation, mean help is always available for developers and admins. Features like continuous aggregation, compression, and automatic partitioning allow teams to optimize performance and save on cloud costs, while its metrics dashboard provides clear insights into database health and usage. **What do you dislike about Tiger Data?** Although TigerData is highly performant, the UI can become slow to load when managing many tables, which impacts workflow efficiency for larger projects. Some users note the absence of advanced visualizers for vector data—features seen in competing products—which can limit analytic visualization capabilities. On rare occasions, initial self-hosted deployments may require extra troubleshooting, but most issues are quickly addressed by updates or community help. Additionally, TigerData’s licensing and query costs can be higher compared to certain open-source or basic database offerings, so budgeting is important when scaling up. **What problems is Tiger Data solving and how is that benefiting you?** TigerData enables reliable and high-speed storage and analysis for massive time series data, solving scaling bottlenecks and ingestion speed limitations experienced with traditional databases. It helps the team efficiently run real-time analytics, minimizes downtime, and saves on cloud costs thanks to automated compression and partitioning features. This has improved decision-making speed and operational reliability for data-driven products. ### 2. Good Service for good storage price **Rating:** 3.5/5.0 stars **Reviewed by:** Juan H. | Fullstack and Blockchain Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 17, 2025 **What do you like best about Tiger Data?** Easy set up and creating of instances and services. **What do you dislike about Tiger Data?** Payments and pricing strategies for services. **What problems is Tiger Data solving and how is that benefiting you?** it does offered a db with good functionalities like creating some cron jobs in it, or transformations. ### 3. Effective tool for data insights **Rating:** 4.0/5.0 stars **Reviewed by:** Kartikey A. | Associate Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** September 29, 2025 **What do you like best about Tiger Data?** It is easy to access, analyze and visualize data. I really liked how it processes large datasets and provides clear, actionable insights to look for. The dashboard for pc version is also user friendly and customizable, and helps to stay updated for the changing trends in the market. It has definitely improved the speed and accuracy of decision-making in my work. Also, i preferred it's former name (timescale) but new name is also good. **What do you dislike about Tiger Data?** there are times when the interface feels a bit heavy, especially when dealing with large volumes of data. The mobile version could also be more optimized for smoother usage on the go with better UI. **What problems is Tiger Data solving and how is that benefiting you?** Currently, I am catering a client where i have to deal and analyze good size data where tigerdata comes into the picture. ### 4. Efficient easy to use platform **Rating:** 4.0/5.0 stars **Reviewed by:** Dalius K. | PHP Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 24, 2025 **What do you like best about Tiger Data?** The platform provides various easy-to-use tools for analytics, making analytics simplier **What do you dislike about Tiger Data?** Need some time to learn features, have slow customer support. **What problems is Tiger Data solving and how is that benefiting you?** TigerData allows us to efficiently store and analyze large volumes of time-series and relational data. It reduces query times, simplifies data management, and provides actionable insights, improving decision-making and operational efficiency ### 5. My Experience using tiger data **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer & Network Security | Small-Business (50 or fewer emp.) **Reviewed Date:** September 26, 2025 **What do you like best about Tiger Data?** I like the clean and intuitive UI the most. It’s easy to navigate, and I appreciate the ability to pause services whenever needed. The availability of connectors, especially for Amazon S3 and Kafka, makes integration smooth and very useful. **What do you dislike about Tiger Data?** The pricing feels a bit high and could be more flexible, especially for smaller projects or startups. **What problems is Tiger Data solving and how is that benefiting you?** TigerData solves the usual trade-offs between real-time and analytical workloads: I get fast queries on fresh and historical data both, without needing to build and maintain complex pipelines. Its compression and tiered storage help keep costs down even as data volume grows. Also, the native integrations (lakehouse / S3) reduce overhead, making it easier to focus on insights rather than infrastructure. ### 6. Great Vector Database Solution **Rating:** 4.5/5.0 stars **Reviewed by:** Thomas C. | Lead AI Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 16, 2025 **What do you like best about Tiger Data?** The ease of set up with the pgai, pgvector, and pgvectorscale plugins makes setting up and running of a highly scalable vector database solution very quick and easy. They have good resources for beginers and advanced people alike and an active discord to help out user issues. The UI/UX of managing the databases are good and there is even a free month when just starting out. The nodeJS implementation for postgres SDK is great, and the ability to write standard postgresql for queries and database managment makes this much more flexible and easier to manage than traditional vector databases. I have used multiple vector databases and this is my faviout one so far for scalability. I use the UI every day when checking up on the health of my vector database and love the metrics it provides. It only took 1 development week to fully switch from a different Vector Database, but I have a massive codebase with a lot of functionality, I'm confident someone with a new codebase could integrate in a day. **What do you dislike about Tiger Data?** I think the UI can be very slow to load sometimes, especially when you have many tables, definetly could improve there. I also miss a vector Visializer like that of QDRANT. **What problems is Tiger Data solving and how is that benefiting you?** Scalable vector database with flexible quering, need I say more ;) ### 7. A great time series database with a great cloud offering **Rating:** 5.0/5.0 stars **Reviewed by:** Louis C. | Lead Platform Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 16, 2025 **What do you like best about Tiger Data?** Product is very performant, cloud interface is easy to use but still offers a lot of control, Terraform provider makes setup a breeze and the team has been awesome **What do you dislike about Tiger Data?** Terraform provider not complete yet, missing some open source options to send cloud telemetry instead of Cloudwatch and Datadog **What problems is Tiger Data solving and how is that benefiting you?** Time series database for high performance ingress and egress for realtime dashboard monitoring ### 8. Great out of the box solution for any PostgreSQL user **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.) **Reviewed Date:** January 20, 2025 **What do you like best about Tiger Data?** Easy to start, easy to maintain, easy to scale **What do you dislike about Tiger Data?** Recent pricing model change is not ideal **What problems is Tiger Data solving and how is that benefiting you?** Allows to store and query large about of financial time series data ### 9. Great but can be costly for small hobby application **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** January 16, 2025 **What do you like best about Tiger Data?** Reliability and ease of use and integration. Decent documentation **What do you dislike about Tiger Data?** The pricing. $50 per month for hobby project adds up. **What problems is Tiger Data solving and how is that benefiting you?** Time series based data storage and retrieval. ### 10. Switched from AWS **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** January 17, 2025 **What do you like best about Tiger Data?** Pricing compared with AWS is slightly better **What do you dislike about Tiger Data?** None (so far), everything works fine as expected **What problems is Tiger Data solving and how is that benefiting you?** Pricing and accessibility with ease of use as administrator ### 11. Great database for time series data **Rating:** 5.0/5.0 stars **Reviewed by:** Daniel R. | Small-Business (50 or fewer emp.) **Reviewed Date:** October 26, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** Ingestion, processing and storage of IoT time series data as a foundation for various services. ### 12. Efficient Time Series Database with Powerful Aggregation and Exceptional User Support **Rating:** 5.0/5.0 stars **Reviewed by:** Dustin S. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 01, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** At our organization, we rely on Timescale to collect and manage meter and site data from a variety of energy management systems across the country. Thanks to Timescale's robust capabilities, we're able to effectively track and analyze this data. ### 13. Smooth Migration and Improved Performance with Timescale Cloud **Rating:** 4.5/5.0 stars **Reviewed by:** Eudald A. | System Design Teacher, Small-Business (50 or fewer emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** At Bloobirds, we were facing several challenges with our previous self-hosted influxDB solution. One of the biggest challenges was that we had to use a custom query language, which required a significant amount of time and resources to learn and use effectively. Additionally, our influxDB solution was not as performant as we needed it to be, especially as our data volumes continued to grow. By migrating to Timescale Cloud, we were able to address these challenges and benefit from a number of key features. For example, Timescale Cloud allows us to use SQL to query our time-series data, which is much more familiar and easier for our engineers. This has saved us a significant amount of time and resources and has made it much easier for us to get insights from our data. In addition, Timescale Cloud provides excellent performance, even with large volumes of data. This has allowed us to handle our growing data volumes without experiencing any slowdowns or other performance issues. Overall, Timescale Cloud has been a major benefit to our organization, allowing us to manage and analyze our time-series data more effectively and efficiently. ### 14. A performant time-series database built on the rock-solid Postgres DB, with stellar support to boot **Rating:** 5.0/5.0 stars **Reviewed by:** Michael S. | Enterprise (> 1000 emp.) **Reviewed Date:** February 24, 2023 **What do you like best about Tiger Data?** 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 Tiger Data?** 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 Tiger Data 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. ### 15. A high quality time series database that is in production within minutes **Rating:** 5.0/5.0 stars **Reviewed by:** Carl C. | Head Of Information Technology, Small-Business (50 or fewer emp.) **Reviewed Date:** February 21, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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 Tiger Data 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. ### 16. Easily extend Timescale to solve your problems. **Rating:** 5.0/5.0 stars **Reviewed by:** Andrew E. | Head, Data Science Solutions, Small-Business (50 or fewer emp.) **Reviewed Date:** February 20, 2023 **What do you like best about Tiger Data?** 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 Tiger Data?** 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 Tiger Data 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. ### 17. Time series databases have never been so easier **Rating:** 5.0/5.0 stars **Reviewed by:** Hariharan R. | Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** 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 **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** We've a time-series data use case that Timescale solves for us superbly. ### 18. Postgres but faster **Rating:** 5.0/5.0 stars **Reviewed by:** Florian H. | Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** February 19, 2023 **What do you like best about Tiger Data?** 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 **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data 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. ### 19. Best time-series database **Rating:** 5.0/5.0 stars **Reviewed by:** Istvan H. | Small-Business (50 or fewer emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** 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! **What do you dislike about Tiger Data?** 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! **What problems is Tiger Data 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. ### 20. A timeseries for IoT **Rating:** 4.5/5.0 stars **Reviewed by:** Anthony C. | Mid-Market (51-1000 emp.) **Reviewed Date:** February 13, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** We are injecting/storing a lot of sensors data in timescale (it is our primary timeseries database that serves all our services), Previously we were using OpenTSDB and the lack of updates, Go library and the management made it very difficult to work with so we decided to move away. One of the main points that made us choose Timescale was the Hypertable feature, Continuous Aggregates, and compression. with this alone we are able to have a very performing timeseries that is able to inject a lot of sensor data, perform aggregation and manage retention policy very easely. ### 21. Data warehouse for time-series data **Rating:** 5.0/5.0 stars **Reviewed by:** Victor L. | Small-Business (50 or fewer emp.) **Reviewed Date:** February 12, 2023 **What do you like best about Tiger Data?** - 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) **What do you dislike about Tiger Data?** - 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 **What problems is Tiger Data solving and how is that benefiting you?** We store massive amounts of marketplace data in Timescale. Previously, on other RDBMS systems, performance for both data ingest and data query become exponentially worse as data volumes increase. With Timescale, we have been able to maintain a high data ingestion rate over time, and leverage capabilities like hypertables and continuous aggregates to deliver decent performance for real-time queries, even over extended time ranges. ### 22. Excellent performance in terms of speed and storage dimensions **Rating:** 5.0/5.0 stars **Reviewed by:** Panos M. | Small-Business (50 or fewer emp.) **Reviewed Date:** October 04, 2023 **What do you like best about Tiger Data?** I like very much the continuous aggregates and the jobs one can define to regularly update them. **What do you dislike about Tiger Data?** There isn't something I dislike. The online documentation is not perfect. I believe it has some room for improvement. **What problems is Tiger Data solving and how is that benefiting you?** It solves the problem of building up time-based reports on multiple dimensions. We use these to prepare to present our creators with analytics on how listeners they listen to audio content published. ### 23. It really is "just PostgreSQL" for time series data **Rating:** 5.0/5.0 stars **Reviewed by:** DeltaSquared . | Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** Timescale efficiently and fully aggregates multiple time windows of any domain data I throw at it. It would be such a tedious development task to maintain that feature. Yet, because TimescaleDB solves this at the database later I don't have to worry about it at all on my application layer! All of my business logic can relate to WHY the time series data relates to each other rather than how I manage the relation. ### 24. The best time series database in 2023 is not a time series database **Rating:** 4.0/5.0 stars **Reviewed by:** Kenny C. | Small-Business (50 or fewer emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** Internal service operations metrics. Monitoring and alerting on microservice performance, errors and the like. Root causing bad system behaviors via rich dimensionality for metrics data and expressive SQL. ### 25. The Easiest, Fastest and Most Cost Effective Time Series Database - Period **Rating:** 5.0/5.0 stars **Reviewed by:** Ken F. | Mid-Market (51-1000 emp.) **Reviewed Date:** February 23, 2023 **What do you like best about Tiger Data?** 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 Tiger Data?** 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 Tiger Data 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. ### 26. Timescale Cloud got us up and running quick and easy **Rating:** 5.0/5.0 stars **Reviewed by:** Logan N. | Director of Software Engineering, Small-Business (50 or fewer emp.) **Reviewed Date:** March 07, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** We store operational data for our SaaS product in Postgres alongside time series data, like SCADA, for analytics and visualization. Timescale makes storing and managing the time series data simple, performant, and efficient. ### 27. A migration we like to think back to **Rating:** 4.5/5.0 stars **Reviewed by:** Mathias P. | Small-Business (50 or fewer emp.) **Reviewed Date:** February 14, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** Backfilling data into already compressed chunks could be more performant **What problems is Tiger Data 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. ### 28. Flexible database service and g **Rating:** 3.5/5.0 stars **Reviewed by:** Simon . | Automation Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 14, 2023 **What do you like best about Tiger Data?** - 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. **What do you dislike about Tiger Data?** 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 Tiger Data 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. ### 29. Real time tracking app for watersport enthusiasts build on a time series & geo-spatial database. **Rating:** 4.5/5.0 stars **Reviewed by:** Patrick P. | Fondateur et Président, Small-Business (50 or fewer emp.) **Reviewed Date:** February 22, 2023 **What do you like best about Tiger Data?** - Performance for time series real time data processing. - Relational database as a service -> less system skills and sysadmin tasks - Support responsiveness **What do you dislike about Tiger Data?** - 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. **What problems is Tiger Data 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. ### 30. Great support, questions are answered almost immediately **Rating:** 5.0/5.0 stars **Reviewed by:** C P. | Small-Business (50 or fewer emp.) **Reviewed Date:** March 01, 2023 **What do you like best about Tiger Data?** 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. **What do you dislike about Tiger Data?** 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. **What problems is Tiger Data solving and how is that benefiting you?** We store huge amounts of financial data ### 31. Timescale vastly improves the efficiency of our operations with time series data **Rating:** 4.5/5.0 stars **Reviewed by:** Alan D. | Engineering Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** February 15, 2023 **What do you like best about Tiger Data?** Compression is an excellent tool for cost-saving while balancing functionality **What do you dislike about Tiger Data?** 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 Tiger Data 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 ### 32. TimeSeries IoT Use Case **Rating:** 4.0/5.0 stars **Reviewed by:** Jamieson T. | Lead Data Scientist, Small-Business (50 or fewer emp.) **Reviewed Date:** October 13, 2022 **What do you like best about Tiger Data?** We utilize timescale as our data warehouse for IoT device time series data. GREAT platform and quick query time! **What do you dislike about Tiger Data?** The apps that are used to manipulate the data. We currently use DBeaver - and it is clunky. Not the easiest to maneuver through. **What problems is Tiger Data solving and how is that benefiting you?** We initially used MS SQL Server for our Time Series data - which called complex queries and LONG run time. TimeScale has fixed that for us. ### 33. Good. But more focus on performance would be nice **Rating:** 4.0/5.0 stars **Reviewed by:** Lars Riis O. | Lead Quantitative Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 28, 2023 **What do you like best about Tiger Data?** The feature set (especially cont queries and the sql extensions) **What do you dislike about Tiger Data?** Performance is lacking compared to questdb and clickhouse **What problems is Tiger Data solving and how is that benefiting you?** Storing of market data and energy meter data - [View Tiger Data pricing details and edition comparison](https://www.g2.com/products/tiger-data/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-22+08%3A07%3A47+-0500&secure%5Bsession_id%5D=822523e5-cd66-4f6b-a0ce-cf515494b682&secure%5Btoken%5D=824282b3e8e03e90090557341d7d648bb8bf536a7e3c85aa54f48c5ea05a471b&format=llm_user) ## Tiger Data Integrations - [Airbyte](https://www.g2.com/products/airbyte/reviews) - [Amazon CloudWatch](https://www.g2.com/products/amazon-cloudwatch/reviews) - [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews) - [Amazon Web Services AI](https://www.g2.com/products/amazon-web-services-ai/reviews) - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews) - [Apache Beam](https://www.g2.com/products/apache-beam/reviews) - [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews) - [Apache Spark for Azure HDInsight](https://www.g2.com/products/apache-spark-for-azure-hdinsight/reviews) - [Appsmith](https://www.g2.com/products/appsmith/reviews) - [Appsmith](https://www.g2.com/products/appsmith-appsmith/reviews) - [Auth0](https://www.g2.com/products/auth0/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [Azure Data Studio](https://www.g2.com/products/azure-data-studio/reviews) - [Azure Functions](https://www.g2.com/products/azure-functions/reviews) - [Azure Monitor](https://www.g2.com/products/azure-monitor/reviews) - [Confluent](https://www.g2.com/products/confluent/reviews) - [Cube](https://www.g2.com/products/cube-2023-07-31/reviews) - [Dash0](https://www.g2.com/products/dash0/reviews) - [DBeaver](https://www.g2.com/products/dbeaver/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [Deepnote](https://www.g2.com/products/deepnote/reviews) - [Deno](https://www.g2.com/products/deno/reviews) - [Django](https://www.g2.com/products/django/reviews) - [Estuary](https://www.g2.com/products/estuary/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - [Flink](https://www.g2.com/products/flink/reviews) - [Forest Admin](https://www.g2.com/products/forest-admin/reviews) - [Golang Container Solution](https://www.g2.com/products/golang-container-solution/reviews) - [Golang Development Services](https://www.g2.com/products/anurag-gupta-golang-development-services/reviews) - [Google Cloud AI Infrastructure](https://www.g2.com/products/google-cloud-ai-infrastructure/reviews) - [Google Cloud Console](https://www.g2.com/products/google-cloud-console/reviews) - [Grafana Labs](https://www.g2.com/products/grafana-labs/reviews) - [Hasura](https://www.g2.com/products/hasura/reviews) - [HighByte Intelligence Hub](https://www.g2.com/products/highbyte-intelligence-hub/reviews) - [IBM Instana](https://www.g2.com/products/ibm-instana/reviews) - [Jaeger](https://www.g2.com/products/jaeger/reviews) - [Java Container Solution](https://www.g2.com/products/java-container-solution/reviews) - [Java Development](https://www.g2.com/products/java-development/reviews) - [Kubernetes](https://www.g2.com/products/kubernetes/reviews) - [Kubernetes](https://www.g2.com/products/american-cloud-kubernetes/reviews) - [LangChain](https://www.g2.com/products/langchain-langchain/reviews) - [Liquibase](https://www.g2.com/products/liquibase/reviews) - [Looker](https://www.g2.com/products/looker/reviews) - [Metabase](https://www.g2.com/products/metabase/reviews) - [n8n](https://www.g2.com/products/n8n/reviews) - [Neon](https://www.g2.com/products/neondatabase/reviews) - [New Relic](https://www.g2.com/products/new-relic/reviews) - [Node.js](https://www.g2.com/products/node-js/reviews) - [Okta](https://www.g2.com/products/okta/reviews) - [OpenTelemetry](https://www.g2.com/products/opentelemetry/reviews) - [pgAdmin](https://www.g2.com/products/pgadmin/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Power BI Solutions](https://www.g2.com/products/power-bi-solutions/reviews) - [Prisma](https://www.g2.com/products/prisma-prisma/reviews) - [Prisma](https://www.g2.com/products/prisma-mediaocean/reviews) - [Prometheus](https://www.g2.com/products/prometheus/reviews) - [Pulumi](https://www.g2.com/products/pulumi/reviews) - [Python](https://www.g2.com/products/python/reviews) - [Redash](https://www.g2.com/products/redash/reviews) - [Redpanda Streaming](https://www.g2.com/products/redpanda-streaming/reviews) - [Render](https://www.g2.com/products/render-render/reviews) - [Retool](https://www.g2.com/products/retool/reviews) - [Rsyslog server - Unlimited Connections](https://www.g2.com/products/rsyslog-server-unlimited-connections/reviews) - [RubyGPT](https://www.g2.com/products/rubygpt/reviews) - [StepZen](https://www.g2.com/products/stepzen/reviews) - [Stitch](https://www.g2.com/products/stitch-2025-07-15/reviews) - [Stitch](https://www.g2.com/products/stitch-stitch/reviews) - [Stitch](https://www.g2.com/products/stitch/reviews) - [Stitch](https://www.g2.com/products/stitch-2026-01-09/reviews) - [Striim](https://www.g2.com/products/striim/reviews) - [Superset](https://www.g2.com/products/superset-superset/reviews) - [Superset](https://www.g2.com/products/superset/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [ToolJet](https://www.g2.com/products/tooljet/reviews) - [Zapier](https://www.g2.com/products/zapier/reviews) ## Tiger Data Features **Management** - Data dictionary - Data Replication - Query Language - Data Modeling - Performance Analysis **Management ** - Data Schema - Query Language - ACID - Complaint - Data Replication **Storage** - Data Model - Data Types **Data Indexing** - Semantic Search - Indexing Data **Query latency** - Lower query latency - Continuous queries **Development** - Application Deployment - Development Tools - Development Environment - Language Support - Testing **Configuration** - Application Performance - Orchestration - Database Monitoring - Anomaly Detection - Network Security **Maintenance** - Data Migration - Backup and Recovery - Multi-User Environment **Support ** - Text Search - Data Types - Languages - Operating Systems **Availability** - Auto Sharding - Auto Recovery - Data Replication **Data latency** - Lower data latency - Data pipeline performance **Database** - Database Management - Analytics - Auto Scaling - Backup / Recovery - Storage **Database Administration** - Provisioning - Governance - Auditing **Security** - Data Encryption - User Access Control **Security** - Database Locking - Access Control - Encryption - Authentication **Performance** - Integrated Cache **Filters** - Accurate Search - Single Stage Filtering - Vector Database **Connectors** - Faster ingestion - Built-in connectors **Infrastructure** - Networking - Virtual Machines - Security **Availability** - Scalability - Backup - Archiving - Indexing **Security** - Data Masking - Authentication And Single Sign-On - Data Anonymization **Performance ** - Disaster Recovery - Data Concurrency - Workload Management - Advanced Indexing - Query Optimizer **Security** - Role-Based Authorization - Authentication - Audit Logs - Encryption **Scale** - Linearly scalable database - Storage management **Data Management** - Data Replication - Advanced Data Analytics **Support** - Multi-Model - Operating Systems **Architecture** - Data security - Lockless architecture **Database Features** - Storage - Availability - Stability - Scalability - Security - Data Manipulation - Query Language ## Top Tiger Data Alternatives - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (690 reviews) - [InfluxDB](https://www.g2.com/products/influxdata-influxdb/reviews) - 4.4/5.0 (98 reviews) - [Google Cloud SQL](https://www.g2.com/products/google-cloud-sql/reviews) - 4.5/5.0 (354 reviews)