How much faster are analytical queries with AxialDB?

On representative workloads — large tables, heavy analytical queries, repeat dashboard refreshes — eval customers have seen up to 40× faster repeat queries (up to 4000% improvement) versus running the same analytics on live OLTP tables every time. Speed comes from columnar snapshots and a dedicated analytical engine; results still depend on data size, shape, and refresh pattern.

Your database shouldn't struggle to draw a simple chart.

Q: How does AxialDB deliver fast analytics without hammering production?

CREATE AXIALDB VIEW runs your defining query once and materializes results to columnar Parquet on disk. Repeat analytical queries on the view run through your own axialdb-engine sidecar on that frozen snapshot — not on live OLTP rows every refresh. That separation protects production and gives columnar-class speed on the hardware you already run.

Sync 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 analytical queries to columnar snapshots once, query them many times — production stays quiet while repeat analytics run on fast columnar storage.

40×

Faster repeat analytical queries on materialized views — because they run through your columnar engine on frozen Parquet snapshots, not on live OLTP rows every refresh (up to 4000% improvement on representative workloads).

Workload-dependent. Eval builds let you measure on your own data before you commit.

Download How it works

Sound familiar?

Heavy analytical queries slow or stall your app
A dashboard report takes forever to load
Syncing everything to a warehouse feels absurd for one dashboard
You want SQL analytics without becoming a data platform team

Your engine, your columnar storage, your resources

AxialDB is not a rented warehouse or always-on sync pipeline — it is an analytical engine and columnar snapshot store on the infrastructure you already operate. Materialize in SQL, serve repeat queries from Parquet, and keep OLTP off the hot path.

Materialize in SQL, not another stack. One CREATE AXIALDB VIEW runs your defining query once and lands a columnar Parquet snapshot on disk.
Columnar engine on your host. Repeat analytical queries run through axialdb-engine on frozen snapshots tuned for scans and aggregates — the speed comes from columnar storage, not from hammering row-oriented OLTP.
Production stays quiet. Dashboards, BI tools, and ad hoc analytics read the snapshot; live tables are touched only when you choose to refresh the view.

How it works

CREATE AXIALDB VIEW

Run your defining query once. Data materializes to columnar Parquet on disk.

SELECT from the view

Charts and reports read the snapshot through your columnar engine — fast repeat analytical queries on Parquet, no warehouse hop.

OLTP stays protected

Live tables are not scanned on every refresh. You choose when to update the snapshot.

One coherent path: materialize to columnar Parquet, query through your analytical engine, leave transactions on row storage. Speed and isolation are the same design — not separate products.

See the architecture overview — how the database extension, sidecar engine, and on-disk columnar snapshots fit together.

Common questions

How does AxialDB deliver fast analytics without hammering production?

CREATE AXIALDB VIEW runs your defining query once and materializes results to columnar Parquet on disk. Repeat analytical queries on the view run through axialdb-engine on that frozen snapshot — not on live OLTP rows every chart refresh. That separation protects production and is what delivers columnar-class speed on the hardware you already run.

How much faster are analytical queries?

On representative workloads — large tables, heavy analytical queries, repeat dashboard refreshes — customers in eval have seen up to 40× faster queries on materialized AxialDB views (up to 4000% improvement) compared with running the same analytics on live OLTP tables every time. Your results depend on data size, query shape, indexes on the source tables, and how often you recreate the view.

Why is my database slow on analytical queries?

Transactional databases scan row-oriented storage. Heavy analytical queries over large tables read columns you do not need for the aggregate and compete with inserts and reports for CPU, memory, and disk I/O on the same engine.

Do I need a warehouse or data pipeline for one dashboard?

Not always. Warehouses and sync pipelines make sense at scale, but they are often overkill for a handful of reports. AxialDB gives you an analytical engine and columnar snapshot store on the database you already run — materialize one view in SQL without replicating your whole schema to a separate platform.

Built for small teams

Create on demand — one view for the report you need, not a pipeline for every table. Your analytical engine and columnar snapshots on hardware you already pay for — without hiring a data platform team. 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.