
Explore data lineage as a trust building framework for governance and ai readiness, learning how to read lineage diagrams, trace data sources, and quantify roi across platforms.
Trace data lineage from origin through every transformation to the final report or model, enabling trust and compliance with audit trails and rapid root-cause analysis.
Explore data lineage through a cooking analogy, tracing ingredients, transformations, and outputs from raw data to dashboards and AI models, diagnosing issues and building trust in data.
Demonstrate the roi of data lineage by reducing data issue resolution time and compliance costs, while boosting trust in reports and clarifying governance and dependencies across the data ecosystem.
Read a data architecture diagram from left to right, tracing raw sources through ingestion and bronze, silver, and gold stages to consumption, with lineage extraction, UI visualization, and metadata governance.
Explore the four data lineage types—technical, business, operational, and end-to-end—and see how they enable governance, impact analysis, and trusted analytics.
Follow six steps—data source identification, metadata extraction, data movement and transformation tracking, lineage mapping and stitching, documentation and governance, and visualization and exploration—to connect data, processes, and people for lineage.
Use data lineage to boost sales accuracy and trust by enabling end-to-end traceability from customer relationship management systems to dashboards, improving KPIs, forecasting, attribution, and gdpr and dsar compliance.
Explore leading platforms for capturing and visualizing data lineage, from automated extraction in Informatica to open source Data Hub and DB2, the data build tool, plus enterprise tools like Collibra.
Explore a hands-on demo of a data catalog and data lineage platform using Data Hub, tracing assets upstream and downstream from Apache Airflow pipelines to Snowflake tables and dashboards.
Data lineage reveals how data moves and transforms across the enterprise, earning trust from engineers to the CDO and tracing SQL logic to support governance and audit readiness.
Data lineage has become a foundational capability in the age of AI, analytics, and cloud transformation — yet most professionals struggle to understand it clearly or apply it practically.
This course simplifies data lineage from first principles and takes you through real-world architectures, modern tooling, and practical implementation strategies. You’ll learn how data moves across systems, how it transforms, and how lineage enables trust, governance, and AI readiness.
Whether you’re working with dashboards, pipelines, or machine learning models, this course will help you confidently explain where data comes from, how it changes, and why it matters.
By the end of this course, you’ll be able to:
• Read and interpret data lineage diagrams
• Design lineage architecture for modern data stacks
• Understand tools like dbt, DataHub, Informatica, and Collibra
• Support audit, compliance, and AI initiatives with traceability
This is not theory — this is practical, enterprise-ready knowledge designed for real-world data teams. You will work through hands-on examples, realistic case studies, and step-by-step walkthroughs that mirror how lineage is implemented in actual enterprise environments. The focus is on clarity, repeatability, and building skills you can apply immediately in your current role or future projects. You will also gain confidence to communicate lineage concepts with business and technical stakeholders.