Data mining
Turn messy sources into trustworthy datasets your team can query and build on.
We profile sources, define joins and grain, and build repeatable extraction and transformation jobs — SQL, Python, or your warehouse-native tooling.
Deliverables are BI- and ML-ready: documented schemas, data quality checks, and clear ownership so analytics and product aren’t guessing what a column means.
Where spreadsheets are still the “source of truth,” we design a migration path: shadow runs, reconciliation reports, and cutover playbooks so finance signs off.
Profiling before we promise a dashboard
We quantify null rates, cardinality, time zones, and duplicate keys before modeling. Surprises surface in a readout deck — not in production.
Grain is agreed in writing: one row per user per day, per order line, etc., so metrics cannot be double-counted by accident.
Idempotent jobs you can replay after incidents
Backfills are first-class: we design partitions and watermarking so reruns do not duplicate revenue.
Alerts go to Slack or PagerDuty with actionable context — not “job failed.”
Phases you can track in demos and invoices.
Source audit
Access, contracts, PII map, and SLAs for freshness.
Conceptual model
Entities, keys, and conformed dimensions agreed with stakeholders.
Build & test
Jobs in staging with reconciliation against legacy reports.
Handoff
Docs, example SQL, and BI connection support.
We meet you where your stack already lives — then standardize the pieces that reduce risk (CI, previews, observability). Below is a typical palette for this lane; exact tools are confirmed in discovery.
Momentum in adjacent launches.
What we deliver
- Source audit, profiling, and recommended target model
- ETL / ELT jobs with scheduling, idempotency, and failure alerts
- Feature or mart tables aligned to business questions
- Handoff: lineage notes, dictionary, and example queries
What you get out of it
- One source of truth for core metrics
- Faster answers from analytics and less spreadsheet glue
- Foundation for ML or deeper reporting later
Often yes. We stage Sheets as slowly-changing dimensions with audit columns and pull Postgres via incremental extracts.
Your analysts can own transforms with our style guide, or we stay on a light retainer for schema drift and new sources.
When required we add streaming with clear latency SLOs; otherwise batch wins on cost and simplicity.
We implement in the tool you already pay for first, and only recommend switches when limits block core questions.
Ready to scope data mining for your product or team?
Book a call