Auditing Your Data

At Nielsen, our data goes through a robust Kafka architecture into several ETLs to receive, transform, and store the data.

While we understood our ETLs’ workflow, we had no visibility into what parts of the data, if any, were lost or duplicated, and in which stage or stages of the workflow.

I will present the design process behind our Data Auditing system, Life Line, which uses Kafka, Avro, Spark, Lambda functions, and complex SQL queries. We’ll cover:

  • Avro Audit header
  • Auditing heartbeat – designing your metadata
  • Designing and optimizing your auditing table
  • Creating an alert-based monitoring system
  • Answering the most important question of all – is it the end of the day yet?

Ready to Get Started? Here Are Some Resources to Help

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Harness Snowflake Data’s Full Potential with Dremio

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Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse

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Dremio Upgrade Testing Framework

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