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SQL analytics, machine learning, financial tools, and building data products for Africa.
We benchmarked DataLAB's journal testing engine on a 2.5 million row general ledger fixture and measured how quickly it surfaced duplicates, after-hours posting, weekend activity, threshold gaming, backdating, and other review populations.
How DataLAB uses SnapQL pipelines to turn one-off analysis into repeatable workflows for transformations, exports, reconciliation, and model-driven analytics.
How DataLAB's RECONCILE command replaces hours of spreadsheet matching with a clear SQL-shaped workflow.
A step-by-step tutorial on training, evaluating, and comparing machine learning models using DataLAB's canonical SnapQL syntax - no Python required.
We built a desktop-first analytics product around SnapQL so teams can query data, run transformations, build pipelines, and train models without leaving the SQL surface.