Trust Your Medallion Architecture

You select any end table from the lineage graph — typically a Gold-layer business table or KPI model — and DataBuck traverses the lineage to automatically discover and include every upstream contributing table, regardless of which layer it resides in. The pipeline is created and executed in a single click, with all configuration handled smartly by DataBuck, and is saved as a named, versioned, reusable governance artifact.

Upon running a pipeline, DataBuck automatically generates and executes validation rules on every table in the dependency chain — across Bronze, Silver, and Gold — based on table metadata and historical data patterns. Quality is scored per table as a percentage and aggregated at the layer level, with historical trend tracking to surface regressions over time.

DataBuck performs matching between every table-pair combination in the pipeline — Bronze-to-Silver, Silver-to-Gold, and Bronze-to-Gold — to confirm data integrity through each transformation. This includes row count reconciliation, key and referential integrity matching, and value-level reconciliation on critical business metrics, with results expressed as percentage match rates per table pair.

DataBuck performs automated schema comparison across layers to detect column additions, removals, data type changes, and naming inconsistencies. When schema drift is detected, it triggers an alert and surfaces a side-by-side column presence and type comparison so teams can assess the impact before it propagates downstream.

When a Gold-layer table fails a quality or reconciliation check, the engine traces the failure backward through the lineage chain and evaluates each upstream table. It determines whether the issue stems from a data migration failure, source system error, transformation logic defect, or schema change, and applies impact scoring to quantify how much each upstream issue contributed to the end-table failure.

DataBuck performs automated schema comparison across layers to detect column additions, removals, data type changes, and naming inconsistencies. When schema drift is detected, it triggers an alert and surfaces a side-by-side column presence and type comparison so teams can assess the impact before it propagates downstream.

Yes. Pipelines run automatically according to the rules and controls you define, requiring no manual triggering once configured. DataBuck continuously monitors each run and maintains ongoing visibility into pipeline health throughout its lifecycle.

DataBuck issues real-time, configurable alerts on events such as a table's quality score falling below its threshold, schema drift detection, reconciliation failures, SLA breaches, or pipeline execution anomalies. Alerts surface immediately through the monitoring feed so issues are caught at the source rather than discovered downstream after impacting consumers.

A single screen presents the overall pipeline health score, per-table quality scores color-coded by layer, reconciliation match percentages per table pair, an interactive root cause trace, layer-level summary cards, column-level lineage drill-down, the schema matching panel, and the live alerts feed. This consolidates the full operational picture without requiring engineers to switch between multiple tools.

Yes. Any rule or control can be reviewed and edited directly from a single centralized screen, so adjusting thresholds, validation logic, or reconciliation parameters is fast and requires no tool-switching. Changes apply to subsequent automated runs without rebuilding the pipeline.

DataBuck provides both table-level and column-level lineage, mapping how individual columns propagate and transform across all three layers and across multiple databases registered within each layer. It layers onto your existing Medallion Architecture as a governance and observability overlay, with no wholesale platform migration required.