Summary

One of the most impactful technologies for data analytics in recent years has been dbt. It’s hard to have a conversation about data engineering or analysis without mentioning it. Despite its widespread adoption there are still rough edges in its workflow that cause friction for data analysts. To help simplify the adoption and management of dbt projects Nandam Karthik helped create Optimus. In this episode he shares his experiences working with organizations to adopt analytics engineering patterns and the ways that Optimus and dbt were combined to let data analysts deliver insights without the roadblocks of complex pipeline management.

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 Nandam Karthik about his experiences building analytics projects with dbt and Optimus for his clients at Sigmoid.

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Sigmoid is and the types of projects that you are involved in?
- What are some of the core challenges that your clients are facing when they start working with you?
An ELT workflow with dbt as the transformation utility has become a popular pattern for building analytics systems. Can you share some examples of projects that you have built with this approach?
- What are some of the ways that this pattern becomes bespoke as you start exploring a project more deeply?
What are the sharp edges/white spaces that you encountered across those projects?
Can you describe what Optimus is?
- How does Optimus improve the user experience of teams working in dbt?
What are some of the tactical/organizational practices that you have found most helpful when building with dbt and Optimus?
What are the most interesting, innovative, or unexpected ways that you have seen Optimus/dbt used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbt/Optimus projects?
When is Optimus/dbt the wrong choice?
What are your predictions for how "best practices" for analytics projects will change/evolve in the near/medium term?

Contact Info

LinkedIn

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.
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Links

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

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Datafold: ![Datafold](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/zm6x2tFu.png) Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code. Visit our site at [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today to book a demo with Datafold.

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