Supercharge your analytics engineering with the power of automated CI checks. Learn how FINN, a global car subscription service, has harnessed the capabilities of automated CI checks to maintain the delicate balance between swift development and robust data pipeline quality as they've scaled their data teams. Dive into insights and strategies to ensure quality without sacrificing speed and discover how to improve your data operations.

Speakers: Chiel Fernhout, Software Engineer, Datafold; Jorrit Posor, Tech Lead Data Engineering, FINN GmbH; Felix Kreitschmann, Senior PM, Data, FINN Auto

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As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling.

This panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.

Speakers: Matt Housley, CTO, Halfpipe Systems; Mehdi Ouazza, Developer Advocate, MotherDuck; Sung Won Chung, Solutions Engineer, Datafold; Louise de Leyritz, Host, The Data Couch podcast

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Join the team from Moody's Analytics as they take you on a personal journey of optimizing their data pipelines for data quality and governance. Like many data practitioners, Ryan and Ravi understand the frustration and anxiety that comes with accidentally introducing bad code into production pipelines—they've spent countless hours putting out fires caused from these unexpected changes.

In this session, Ryan and Ravi recount their experiences with a previous data stack that lacked standardized testing methods and visibility into the impact of code changes on production data. They also share how their new data stack is safeguarded by Datafold's data diffing and continuous integration (CI) capabilities, which enables their team to work with greater confidence, peace of mind, and speed.

Speakers: Gleb Mezhanskiy, CEO, Datafold; Ravi Ramadoss, Director of Data Engineering, Moody's Analytics CRE; Ryan Kelly, Data Engineer, Moody's Analytics CRE

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A significant proportion of dbt Cloud users do not have a dbt CI job set up. Among those who do, many don’t leverage powerful functionality like state comparison and deferral to implement Slim CI, likely causing teams to miss errors and building unnecessary tables. Setting up Slim CI in dbt Cloud can be especially challenging for larger-scale data organizations who have multiple data environments, git branches, and targets. Watch this session to learn how you can build and evolve a strong, lasting data environment using Slim CI.

Speakers: Leo Folsom, Solutions Engineer, Datafold

Register for Coalesce at https://coalesce.getdbt.com