Tiger Data

“By changing the compress-after window, we saved so much data. There were not that many queries after 22 days.”

“The reliability of Tiger Data manifests in the ease of horizontal scaling across multiple servers, which we are very interested in.”

“We shaved off months from our minimum viable product development time by choosing Timescale. The advanced time-series features enable us to produce top-notch market data feeds.”

“Tiger Data fits so well with what we do... It integrates so seamlessly into PostgreSQL.”

“With Tiger Cloud, we haven't experienced any issues since migrating, which has drastically improved our reliability and allowed us to avoid costly downtime.”

“Thanks to Tiger Data, we drastically shortened the time to process data and accelerated our progress in delivering quality results for the projects.”

“Tiger Data by itself is what allows for the hypertables, the query performance, and the compression. That's where it's something like $12,000 monthly storage savings.”

“We don't have a lot of skills around large data management,”

“It's actually helped us find bottlenecks and problems in our application by switching to Tiger Cloud. One of our processes is doing 8,000 queries to insert one report. If I send one query that inserts all of that data at once, it's going to massively speed things up.”

“We already used Postgres and Tiger Data was appealing to us because it provided a better way to manage the table partitioning and adapt it as our usage grew. We also evaluated Cassandra, Druid, and CockroachDB—but none seemed like a perfect fit.”

“Tiger Cloud solution is highly user-friendly, easy to maintain, and can upscale accordingly with the user’s demand.”

“By using Tiger Data, we were able to turn around a real-time dashboard for our API, handling up to 25 million requests a day with exceptional query performance.”

“Moving from SQL Server to Tiger Data allowed us to scale efficiently, manage our growing data needs, and maintain optimal performance without additional overhead.”

“The support and active community around TimescaleDB have been invaluable in helping us optimize our database performance and troubleshoot issues quickly.”

“We chose Tiger Data because it was cost-efficient, easy for analysts to use, and perfectly suited for our time-based queries and real-time operations.”

“After implementing Tiger Data, our AWS cloud costs went down about 35 % because it is cheaper to run than the AWS OpenSearch we used before. In addition, the query performance improved dramatically. A majority of queries take under 100 ms to complete.”

“Compression ratio is jaw-droppingly high. When compressing a single hypertable in Timescale, we receive 77 GB of compressed data from 1396 GB of uncompressed data. That is 94% compression ratio.”

“We have scaled from 200 sensors to 2000 in just a few months without encountering issues, thanks to Tiger Data.”

“Tiger Data allows us to scale our services without introducing completely new technologies to the mix. As PostgreSQL users, Tiger Data adds very little maintenance overhead compared to learning and maintaining a brand new database system.”

“The greatest Tiger Data feature for us is the ability to automatically downsample data thanks to continuous aggregates.”

“We leverage TimescaleDB’s continuous aggregates, which let us efficiently process large volumes of time-series data while maintaining performance. This has been essential for providing real-time insights to our customers.”

“Tiger Cloud has proven to be a key enabler of Octave’s data-driven Battery Cloud technology allowing us to collect and analyze millions of data points daily while dramatically saving disk space and delivering lightning-fast queries.”

“I’m using Tiger Data because it’s the same as PostgreSQL but magically faster.”

“In order for predictive maintenance and collision avoidance to provide contextualized and accurate results, we must gather and process 100M+ data points per machine. We use hypertables to handle these large datasets. We've saved lives using Tiger Data.”

“By using TimescaleDB, we have significantly improved our data management efficiency, achieving query times of 25 milliseconds, reducing our database size from 200 GB to 55 GB with compression, and maintaining high insertion rates.”