Be Confident In Your Data Integration By Quickly Validating Matching Records With data-diff
Data Engineering Podcast
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Is There a Hashing Algorithm?
M d is one of the faster hashing algorithms. It also has of the very real potential of generating hash collisions. In reality, i would love to use something like x x hash, right? Which is about a hundred times faster and can be simd than m d five. The reason why we're using emty five is just because is ubiquitous,. I think in reality, what we might do at some point is to not even pash it, but literally just try to som if their integers are floating points. Because for data verification, that's usually sufficient.
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Summary
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.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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
Interview
- 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?
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 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
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Links
- Datafold
- data-diff
- Autodesk
- Airbyte
- Debezium
- Napkin Math newsletter
- Airflow
- Dagster
- Great Expectations
- dbt
- Trino
- Preql
- Erez Shinan
- Fivetran
- md5
- CRC32
- Merkle Tree
- Locally Optimistic
- Presto
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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