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.
Ascend.io, the Data Automation Cloud, provides the most advanced automation for data and analytics engineering workloads. Ascend.io unifies the core capabilities of data engineering—data ingestion, transformation, delivery, orchestration, and observability—into a single platform so that data teams deliver 10x faster. With 95% of data teams already at or over capacity, engineering productivity is a top priority for enterprises. Ascend’s Flex-code user interface empowers any member of the data team—from data engineers to data scientists to data analysts—to quickly and easily build and deliver on the data and analytics workloads they need. And with Ascend’s DataAware™ intelligence, data teams no longer spend hours carefully orchestrating brittle data workloads and instead rely on advanced automation to optimize the entire data lifecycle. Ascend.io runs natively on data lakes and warehouses and in AWS, Google Cloud and Microsoft Azure.
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Tonic.ai matches development and staging environments to production by rapidly equipping teams with high-quality data at scale. With regulations and breaches on the rise, production data is no longer safe (or legal) for developers to use, but creating test data in-house is a complex chore that eats into valuable engineering resources. With Tonic, teams no longer need to choose between productivity and security—they get both rapidly and with ease. Shorten your development cycle, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data. Through its data de-identification, advanced subsetting, and synthetic scaling technologies, Tonic makes it possible to create a true mirror of production in the safety of a developer landscape so you can work on real product and steer clear of surprises at release time.
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- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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