Data, in motion, with clarity.
Your data already lives somewhere — ERP, CRM, property register, market feed. The question is not which engine to use, but how fast you can get it into a relational form where it becomes queryable, joinable, and auditable. That's where value unlocks.
First principle: Get data into an RDBMS as early as possible. Relational storage is the most efficient foundation for stream processing, analytics, and reporting. Everything after that — DuckDB for ad-hoc, Polars for batch, streaming for real-time — is a downstream optimisation of a clean upstream source.
How we work
Source & Stage
From any input — CSV, API, JSON, legacy extract — into a normalised relational schema. Deduplicated, typed, timestamped. The foundation of everything that follows.
Analyse & Model
SQL-first analytics, window functions, address normalisation, repeat-sale detection, percentile distributions. No black boxes. Every result is reproducible from the source.
Surface & Share
Static HTML dashboards, embedded charts, parameterised reports. No server to maintain, no dashboard licence to renew. Push to deploy. Your stakeholders get a link, not a PDF.
Featured work: PPR Engine Benchmark
Ireland's Property Price Register — 783,755 transactions from 2010 through May 2026. Five query engines benchmarked across 11 analytical stress queries, from simple aggregations to self-joins, rolling windows, and 5-dimensional GROUP BYs.
11 queries · 783K rows · 92 MB · 5 engines · 4 cores · Static deploy — Full dashboard →
Why SQL-first?
Every analytical engine in the benchmark above — DuckDB, Polars, Pandas, even Zig — shares one thing: they perform best when the data is already clean, typed, and relational. The gap between them (0.46s for Zig, 1.03s for Polars, 1.47s for DuckDB) is meaningful at scale, but dwarfed by the cost of not having the data in a queryable form at all.
A well-designed SQL schema with normalised dimensions, indexed temporal keys, and validated constraints is the single highest-leverage investment a data function can make. Everything else — the dashboard, the ML pipeline, the weekly report — sits on top of it.