SingleBench

Queries that would time out completely under ElasticSearch are now processing in less than 10 seconds with SingleStore, and some clock in under 1.5 seconds. We moved from the entire data pipeline including ElasticSearch costing more than $1 million annually to paying a fraction of that for SingleStore, reducing our data pipeline cost by 70%.

It felt like SingleStore was the first breath of fresh air that wasn’t a compromise. It was like, that’s exactly what customers want: get the data in there fast and get results out fast.

Our experience with Twingo was phenomenal. They showed up wherever and whenever we needed them. They brought top-tier technical skills, with deep expertise in the big data landscape. And what set them apart was their intimate, in-depth understanding of SingleStore’s internals. Bottom line? The combination of Twingo’s expertise and SingleStore’s cutting-edge capabilities empowered us to reach every one of our goals.

It is like a really fast version of Snowflake or Databricks. It is easy to get up and going with SingleStore pipelines (no external tools like Kafka Connect or Apache Flink are required since they have SingleStore pipelines), and customer support is great too when I wasn't able to figure it out on my own.

It is really good to have a data platform that can model relational Postgres data with big data, right off the bat. It’s hard to put a price on ‘It just works.’ With SingleStore, we can set it and forget it.

We evaluated SingleStore against MySQL, PostgreSQL, and Druid. We have also quickly looked at ClickHouse and Pinot. We found SingleStore was a polished installation and operation was a breeze. That, coupled with the great performance, led us to select SingleStore very quickly in our evaluation.