Be Confident In Your Data Integration By Quickly Validating Matching Records With data-diff - Episode 303


The perennial challenge of data engineers is ensuring that information is integrated reliably. While it is straightforward to know whether a synchronization process succeeded, it is not always clear whether every record was copied correctly. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. In this episode they explain how the utility is implemented to run quickly and how you can start using it in your own data workflows to ensure that your data warehouse isn’t missing any records from your source systems.

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  • Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to today to give it a try!
  • Data teams are increasingly under pressure to deliver. According to a recent survey by, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
  • RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at
  • Your host is Tobias Macey and today I’m interviewing Gleb Mezhanskiy and Simon Eskildsen about their work to open source the data diff utility that they have been building at Datafold


  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what the data diff tool is and the story behind it?
    • What was your motivation for going through the process of releasing your data diff functionality as an open source utility?
  • What are some of the ways that data-diff composes with other data quality tools? (e.g. Great Expectations, Soda SQL, etc.)
  • Can you describe how data-diff is implemented?
    • Given the target of having a performant and scalable utility how did you approach the question of language selection?
  • What are some of the ways that you have seen data-diff incorporated in the workflow of data teams?
  • What were the steps that you needed to do to get the project cleaned up and separated from your internal implementation for release as open source?
  • What are the most interesting, innovative, or unexpected ways that you have seen data-diff used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on data-diff?
  • When is data-diff the wrong choice?
  • What do you have planned for the future of data-diff?

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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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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