Enforce governance policies where change happens
Define and enforce policies where data is built so standards, access, and compliance keep pace with delivery. Quality and governance are embedded in the transformation workflow—not bolted on after the fact.
- Define policies where data is built and enforce them automatically as pipelines run
- Standardize work with org-wide libraries, reusable templates, and rule inheritance through lineage
- Control access with RBAC, classify sensitive data automatically, and capture audit trails
- Formalize data quality contracts between producers and consumers and track compliance
Quality where code is born
Author, version, and run tests as part of your transformation workflows. Gate deployments on pass criteria. Block bad data before it ships, not after dashboards break.
- Write and manage tests alongside transformations and CI/CD
- Define schema/column contracts, freshness/volume checks, and business-rule tests at the node level
- Block merges and releases when critical tests or SLOs fail
- Detect schema, volume, freshness, and distribution anomalies automatically
Resolve faster with lineage-linked incidents
Unify lineage, alerts, and metadata so teams can quickly pinpoint root cause and understand downstream impact. When failures occur, they’re tied to owners, SLAs, and business impact in one place—so you resolve issues quickly and prevent recurrences.
- Enrich alerts with owners, SLAs, and schema context from your Catalog.
- Track reliability with metadata-backed KPIs, Data Downtime trends, and quality scorecards
- Run impact analysis across producers, consumers, and BI tools using unified metadata
- Shorten MTTR and reduce re-runs with policy-driven quality
Surface trust where analysts and AI agents work
Surface quality signals where analysts work with AI-assisted catalog search and collaborative documentation. Quality scores, owners, and run history appear in the Catalog—so consumers can prefer certified, reliable assets automatically.
- Use AI to search across assets, columns, and business terms
- Certify assets based on quality scores, freshness, and adoption
- Surface quality badges so AI agents and analysts prefer “green” data automatically
- Reduce Slack pings and accelerate time to insight with provable trust
Coalesce embeds testing, monitoring, lineage, and governance in the transformation and catalog workflows—so quality lives in the same workflow as development. This eliminates context switching, catches issues pre-merge, and connects failures to lineage and owners for faster resolution.
Yes. Automated anomaly detection monitors schema changes, volume shifts, freshness delays, and distribution anomalies. Alerts are lineage-aware and enriched with owner, SLA, and business impact context—so teams can identify root cause before dashboards or AI outputs are impacted.
Policies are enforced at the pipeline level, not documented after the fact. Automated data classification, RBAC, and audit trails enable proactive governance and simplify compliance reporting. Lineage is audit-ready by default.
Right in the AI-powered Catalog, with documentation, certification, quality badges, freshness, and usage context. Analysts and AI agents can automatically prefer certified, “green” assets for faster, more confident decisions.
The opposite. Finding issues pre-merge is cheaper than rolling back production. Teams that shift quality left report fewer incidents, faster cycles, and lower re-run costs. Quality where code is born means speed and safety.