Summary
Working with data is a complicated process, with numerous chances for something to go wrong. Identifying and accounting for those errors is a critical piece of building trust in the organization that your data is accurate and up to date. While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. In this episode Maayan Salom explores the approach that she has taken to bring observability, enhanced testing capabilities, and anomaly detection into every step of the dbt developer experience.
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 Maayan Salom about how to incorporate observability into a dbt-oriented workflow and how Elementary can help
Interview
Introduction
How did you get involved in the area of data management?
Can you start by outlining what elements of observability are most relevant for dbt projects?
What are some of the common ad-hoc/DIY methods that teams develop to acquire those insights?
What are the challenges/shortcomings associated with those approaches?
Over the past ~3 years there were numerous data observability systems/products created. What are some of the ways that the specifics of dbt workflows are not covered by those generalized tools?
What are the insights that can be more easily generated by embedding into the dbt toolchain and development cycle?
Can you describe what Elementary is and how it is designed to enhance the development and maintenance work in dbt projects?
How is Elementary des