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technologyJul 20, 20211:01:07pending
Strategies For Proactive Data Quality Management
About this episode
Summary
Data quality is a concern that has been gaining attention alongside the rising importance of analytics for business success. Many solutions rely on hand-coded rules for catching known bugs, or statistical analysis of records to detect anomalies retroactively. While those are useful tools, it is far better to prevent data errors before they become an outsized issue. In this episode Gleb Mezhanskiy shares some strategies for adding quality checks at every stage of your development and deployment workflow to identify and fix problematic changes to your data before they get to production.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy!
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- Your host is Tobias Macey and today I’m interviewing Gleb Mezhanskiy about strategies for proactive data quality management and his work at Datafold to help provide tools for implementing them
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what you are building at Datafold and the story behind it?
- What are the biggest factors that you see contributing to data quality issues?
- How are teams identifying and addressing those failures?
- How does the data platform architecture impact the potential for introducing quality problems?
- What are some of the potential risks or consequences of introducing errors in data processing?
- How can organizations shift to being proactive in their data quality management?
- How much of a role does tooling play in addressing the introduction and remediation of data quality problems?
- Can you describe how Datafold is designed and architected to allow for proactive management of data quality?
- What are some of the original goals and assumptions about how to empower teams to improve data quality that have been challenged or changed as you have worked through building Datafold?
- What is the workflow for an individual or team who is using Datafold as part of their data pipeline and platform development?
- What are the organizational patterns that you have found to be most conducive to proactive data quality management?
- Who is responsible for identifying and addressing quality issues?
- What are the most interesting, innovative, or unexpected ways that you have seen Datafold used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datafold?
- When is Datafold the wrong choice?
- What do you have planned for the future of Datafold?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Datafold
- Autodesk
- Airflow
- Spark
- Looker
- Amundsen
- dbt
- Dagster
- Change Data Capture
- Delta Lake
- Trino
- Presto
- Parquet
- Data Quality Meetup
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Special Guest: Gleb Mezhanskiy.
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