Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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14 November 2021

Data Quality Starts At The Source - E238

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The most important gauge of success for a data platform is the level of trust in the accuracy of the information that it provides. In order to build and maintain that trust it is necessary to invest in defining, monitoring, and enforcing data quality metrics. In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found.


  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to today to start a 30-day trial of Datafold.
  • Your host is Tobias Macey and today I’m interviewing Michael Harper about definitions of data quality and where to define and enforce it in the data platform


  • Introduction
  • How did you get involved in the area of data management?
  • What is your definition for the term "data quality" and what are the implied goals that it embodies?
    • What are some ways that different stakeholders and participants in the data lifecycle might disagree about the definitions and manifestations of data quality?
  • The market for "data quality tools" has been growing and gaining attention recently. How would you categorize the different approaches taken by open source and commercial options in the ecosystem?
    • What are the tradeoffs that you see in each approach? (e.g. data warehouse as a chokepoint vs quality checks on extract)
  • What are the difficulties that engineers and stakeholders encounter when identifying and defining information that is necessary to identify issues in their workflows?
  • Can you describe some examples of adding data quality checks to the beginning stages of a data workflow and the kinds of issues that can be identified?
    • What are some ways that quality and observability metrics can be aggregated across multiple pipeline stages to identify more complex issues?
  • In application observability the metrics across multiple processes are often associated with a given service. What is the equivalent concept in data platform observabiliity?
  • In your work at Databand what are some of the ways that your ideas and assumptions around data quality have been challenged or changed?
  • What are the most interesting, innovative, or unexpected ways that you have seen Databand used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working at Databand?
  • When is Databand the wrong choice?
  • What do you have planned for the future of Databand?

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Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?


The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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