- contact@insightxform.com
In-warehouse compute on Snowflake, BigQuery, Databricks, and Redshift. Preview every transformation on a sample before it touches production. Lineage propagates to your catalog automatically. AI-augmented where deterministic rules can’t keep up.
Built for teams who ship clean data
Cleansing tools that pull data out, transformations you can’t preview, and lineage that lives in a separate tool — that’s the field. InsightXform fixes all three.
Pushes compute into Snowflake, BigQuery, Databricks, or Redshift — not into a separate ETL plane. No data movement, no cross-perimeter hop, no parallel compute bill.
Every cleansing rule and transformation previews on a representative sample before touching production. Diff-view before commit. No more "we pushed a fix at 4pm and corrupted the export at 6pm".
Every transformation, every rule firing, every reviewer override emits OpenLineage events that flow into your catalog. Downstream consumers know what changed and why.
A four-step agentic pipeline. Each step is independently configurable, previewable, and reversible.
Scan incoming data for null rates, distributions, outliers, format violations, duplicates, and reference-data mismatches. Surface them as a punch list, not a 60-page PDF.
Apply deterministic rules where they belong (formats, currencies, units) and AI assistance where they don’t (entity resolution, fuzzy matches, anomalous values). Preview every fix on a sample.
Business-rule library, statistical drift detection, golden-query comparison, distribution stability. Failures route to a reviewer queue or block the pipeline, your choice.
Write back to the warehouse as a versioned table, publish a data contract, emit OpenLineage events — downstream consumers know exactly what they’re getting.
Every step is versioned. A bad cleansing pass can be rolled back per-table or per-rule without rerunning the upstream pipeline. The audit trail records every state transition.
Teams typically start with one and add others once the platform is wired into the warehouse.
Customer / product / supplier masters built from messy multi-source records with AI-assisted matching and reviewer-confirmable merges.
Cleanse and reconcile data before, during, and after migrations — row-count, distribution, and golden-query diffs between source and target.
Clean, balance, and feature-standardise training datasets — with leakage detection, class-imbalance scoring, and reproducible sampling.
Currency, units, time zones, region-level rollups — the cross-cutting normalisations that turn raw data into consistent reports.
Stitch customer records across CRM, billing, support, and product telemetry — with AI-assisted matching and reviewable golden records.
Treat cleansed datasets as data products with SLAs. Continuous monitoring + auto-remediation for known issue patterns; escalation for the rest.
Built to slot into your warehouse, orchestrator, quality tooling, and catalog — not replace them.
Pushdown SQL on the warehouse you already pay for. No separate Spark cluster, no data egress.
Drop InsightXform steps into the orchestrator your team already uses — not yet-another-scheduler.
Emit validation results into the quality tools your platform team already trusts.
Lineage events flow into your catalog — every transformation, every reviewer override, every SLA breach.
The control plane is small — the heavy compute already happens in your warehouse. Cloud SaaS for quick starts; self-hosted when sovereignty matters.
Aggregated outcomes from enterprise deployments. We’ll size the ROI for your data platform on the discovery call.
Faster onboarding of new data sources
Less rule maintenance overhead
Data egress — runs in your warehouse
Lineage coverage on every transform
What we hear most often from platform leads and architects. More in our discovery call.
Pushes compute into your warehouse via SQL pushdown. Data never moves to a separate ETL plane. Your Snowflake / BigQuery / Databricks credits do the heavy lifting; the InsightXform control plane is small.
Yes. Compose InsightXform steps as dbt models, Airflow tasks, or Dagster ops. We don’t replace your orchestrator — we plug into the one your team already runs.
Yes — every pass writes a versioned table, not in-place. Roll back per-table or per-rule without rerunning upstream. Full audit trail of who applied what, when, and why.
GE and Soda are quality-assertion tools — they tell you something’s wrong. InsightXform is a transformation engine that fixes what’s wrong, then emits results into GE / Soda for downstream visibility. They complement, they don’t overlap.
Every run emits OpenLineage events captured by your catalog (Insight Catalog, DataHub, Atlan, Collibra). Per-column transformation history, reviewer overrides, and SLA breaches all flow to the same place.
In your warehouse. The control plane (self-host or SaaS) only sees metadata, lineage events, and the small samples used for preview — never raw production rows.
Bring a messy table from your Snowflake or BigQuery. We’ll profile, cleanse, and validate it live in a 30-minute walkthrough — preview-before-apply included.
Field notes on in-warehouse transformation, reversible cleansing, and lineage-native data products.
The economics, the latency, and the security story behind in-warehouse cleansing — and what we gave up to make it work.
Why most data-cleansing tools skip preview, what teams give up when they do, and the operating model that gets it right.
How OpenLineage events from a cleansing engine change the catalog’s role from documentation to operational truth.