Your database shouldn't struggle to draw a simple chart.
ETL pipelines and lakehouses are powerful — and often overkill. Too complex, too expensive, too much to maintain for the few reports you actually need.
Host your own analytical engine on the resources you already run. Materialize once. Query many times. Production stays quiet.
Sound familiar?
- A heavy
GROUP BYslows or stalls your app - Syncing everything to a warehouse feels absurd for one dashboard
- You want SQL analytics without becoming a data platform team
How it works
Run your defining query once. Snapshot lands as Parquet on disk.
Charts and reports read the snapshot via a sidecar columnar engine.
Live tables are not scanned on every refresh. You choose when to update.
Unlike accelerators that query live tables every time, AxialDB separates workloads: analytics on frozen snapshots, transactions on your database.
Common questions
Why is my database slow on a simple GROUP BY?
Transactional databases scan row-oriented storage. A heavy GROUP BY over large tables
reads columns you do not need for the aggregate and competes with inserts and reports for CPU,
memory, and disk I/O on the same engine.
Do I need a warehouse or ETL for one dashboard?
Not always. Warehouses and sync pipelines make sense at scale, but they are often overkill for a handful of reports. AxialDB lets you materialize one analytical view from SQL on the database you already run — without replicating your whole schema.
How does AxialDB keep analytics off production?
CREATE AXIALDB VIEW snapshots your query to Parquet once. Repeat SELECTs
on the view run in a sidecar columnar helper on that snapshot — live OLTP tables are not scanned on
every chart refresh. You choose when to recreate the view.
Built for small teams
Create on demand — one view for the report you need, not a pipeline for every table. Stop using it? Unused snapshots can expire on their own.
Honest tradeoff: CREATE touches live data once; numbers are as-of until you recreate the view.