Slow analytics? Look at the architecture, not the data
From 3.5 seconds to around 30 milliseconds on the same workload: that's not an optimisation, it's a different performance class.
We keep blaming “big data” for slow analytics, while the real problem usually lies elsewhere. Software engineer Arsh Goyal demonstrated this with a benchmark on a 6-million-row TPC-H dataset: the same analytical query took 3.5 seconds in PostgreSQL, 125 milliseconds in DuckDB and around 30 milliseconds in Exasol Personal.
Going from 3.5 seconds to ~30 milliseconds on exactly the same workload is no longer an optimisation: it's a completely different performance class.
The uncomfortable truth: many teams still work with the wrong tools and then label it a data problem. At C-data we see this every day. The biggest delay rarely lies in data volume, but almost always in architectural choices.
That's exactly why we like to work with partners such as Exasol. Analytics calls for technology designed from the ground up for speed, scale and complexity, not for workarounds on systems built for something else. That lets teams focus on insights instead of tuning queries or explaining delays.
What is really holding your queries back: the amount of data, or the way the platform is built?