Summary

Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is critical that everyone is able to collaborate seamlessly. SQLMesh was designed as a unifying tool that is simple to work with but powerful enough for large-scale transformations and complex projects. In this episode Toby Mao explains how it works, the importance of automatic column-level lineage tracking, and how you can start using it today.

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 Toby Mao about SQLMesh, an open source DataOps framework designed to scale data transformations with ease of collaboration and validation built in

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what SQLMesh is and the story behind it?
- DataOps is a term that has been co-opted and overloaded. What are the concepts that you are trying to convey with that term in the context of SQLMesh?
What are the rough edges in existing toolchains/workflows that you are trying to address with SQLMesh?
- How do those rough edges impact the productivity and effectiveness of teams using those
Can you describe how SQLMesh is implemented?
- How have the design and goals evolved since you first started working on it?
What are the lessons that you have learned from dbt which have informed the design and functionality of SQLMesh?
For teams who have already invested in dbt, what is the migration path from or integration with dbt?
You have some built-in integration with/awareness of orchestrators (currently Airflow). What are the benefits of making the transformation tool aware of the orchestrator?
What do you see as the potential benefits of integration with e.g. data-diff?
What are the second-order benefits of using a tool such as SQLMesh that addresses the more mechanical aspects of managing transformation workfows and the associated dependency chains?
What are the most interesting, innovative, or unexpected ways that you have seen SQLMesh used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLMesh?
When is SQLMesh the wrong choice?
What do you have planned for the future of SQLMesh?

Contact Info

tobymao on GitHub
@captaintobs on Twitter
Website

Parting Question

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

Closing Announcements

Links

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

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Rudderstack: ![Rudderstack](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png) RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

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