Who should use the Track data lineage workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
Practical execution plan for track data lineage with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Proactive lineage monitoring with governance policies and alerting
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Proactive lineage monitoring with governance policies and alerting
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Atlan to complete inventory of upstream data assets with metadata and ownership. Then, you pass the output to dbt Cloud (AI-Powered) to automated lineage event generation from all critical data pipelines. Then, you pass the output to Coalesce Catalog to centralized repository of lineage metadata with complete event history. Then, you pass the output to FalkorDB to interactive lineage graph enabling root-cause analysis and impact assessment. Then, you pass the output to DQLabs to validated lineage with documented coverage and known exceptions. Finally, Atlan is used to proactive lineage monitoring with governance policies and alerting.
Map source systems and data assets
Complete inventory of upstream data assets with metadata and ownership
Instrument data pipelines with lineage hooks
Automated lineage event generation from all critical data pipelines
Collect and store lineage metadata
Centralized repository of lineage metadata with complete event history
Visualize and query data lineage
Interactive lineage graph enabling root-cause analysis and impact assessment
Validate lineage completeness and accuracy
Validated lineage with documented coverage and known exceptions
Establish lineage governance and alerting
Proactive lineage monitoring with governance policies and alerting
Identify all upstream data sources (databases, APIs, files) and catalog their schemas, tables, and fields. Document ownership, update frequency, and access patterns to establish a baseline for lineage tracking.
Why Atlan: Atlan is a dedicated data catalog and governance platform, directly matching the need for mapping source systems and data assets.
Add logging and metadata capture at each transformation step (ETL/ELT jobs, SQL views, dbt models). Use open-lineage standard or vendor-specific APIs to emit lineage events automatically.
Why dbt Cloud (AI-Powered): dbt Cloud supports lineage plugins and integrates with pipeline orchestration, aligning with instrumenting lineage hooks.
Centralize lineage events into a metadata store (e.g., Marquez, Apache Atlas, or a custom database). Ensure events include source, transformation, target, and timestamps for full traceability.
Why Coalesce Catalog: Atlan serves as a data catalog and governance platform capable of storing and managing lineage metadata.
Use the lineage backend’s UI or API to explore upstream and downstream dependencies for any dataset. Generate lineage graphs to trace data origin, transformations, and impact of changes.
Why FalkorDB: FalkorDB is a graph database optimized for storing and querying relationships, ideal for visualizing data lineage.
Cross-check lineage records against actual pipeline runs and data dictionaries. Identify gaps (e.g., missing sources, unlogged transformations) and reconcile with stakeholders to ensure trustworthiness.
Why DQLabs: DQLabs monitors pipeline health and detects anomalies, directly supporting validation of lineage completeness and accuracy.
Set up automated alerts for lineage breaks (e.g., source table dropped, schema change). Define ownership rules and periodic reviews to keep lineage metadata current as pipelines evolve.
Why Atlan: Atlan is a data governance platform that can enforce lineage policies and integrate with alerting systems.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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