Beyond Pretty Graphs: How End-to-End Lineage Drives Better Actions

In this webinar, Databand’s VP of Product Shani Keynan and Product Evangelist Ryan Yackel pull together Airflow, dbt, Spark and Redshift into a real-world example of the power of end-to-end data lineage.

What's inside

Seems like everyone is talking about data lineage these days, and for a good reason. Data lineage helps ensure better data quality across your modern data stack.

But not everyone speaks the same lineage language.

Data engineers use lineage for impact and root cause analysis. Analysts and Analytics engineers use lineage to trace jobs and transformations in their warehouses. And consumers use lineage to understand why data never reached their expected destination.

This results in a narrow, siloed view lineage in which only one group benefits.

It’s time to stop using siloed lineage views for pretty graphs and start using end-to-end lineage to drive focused actions.

In the talk, you will learn:

  • How data quality tailors to specific needs of data engineers, analysts, & consumers
  • How data lineage should drive actions vs pretty graphs
  • A real-world example of end-to-end data lineage with Airflow, dbt, Spark, and Redshift

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With so much hinging on having good, reliable data in place, data engineering teams need to be able to detect and resolve data issues as early on as possible. 

Data observability meets those challenges, not only by identifying problems, but by providing the context necessary to solve and prevent issues from recurring.

In this guide, we’ll help you approach proactive data observability, covering the essentials of:

  • What is data observability
  • Challenges you’ll face on the path to data observability
  • 5 steps to achieve proactive data observability
  • Why proactive data observability matters

Keep up with the Databand community

All Blogs

What is Data Lineage?

A new era of data management has arrived. Learn about the different types of data lineage and how they work.