Data Engineering Podcast

Eliminate The Overhead In Your Data Integration With The Open Source dlt Library

Sep 3 '23

Summary

Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines 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 Adrian Brudaru about dlt, an open source python library for data loading

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what dlt is and the story behind it?
- What is the problem you want to solve with dlt?
- Who is the target audience?
The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. in the commercial space. What are the complexities or limitations of those tools that leave an opening for dlt?
Can you describe how dlt is implemented?
What are the benefits of building it in Python?
How have the design and goals of the project changed since you first started working on it?
How does that language choice influence the performance and scaling characteristics?
What problems do users solve with dlt?
What are the interfaces available for extending/customizing/integrating with dlt?
Can you talk through the process of adding a new source/destination?
What is the workflow for someone building a pipeline with dlt?
How does the experience scale when supporting multiple connections?
Given the limited scope of extract and load, and the composable design of dlt it seems like a purpose built companion to dbt (down to the naming). What are the benefits of using those tools in combination?
What are the most interesting, innovative, or unexpected ways that you have seen dlt used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on dlt?
When is dlt the wrong choice?
What do you have planned for the future of dlt?

Contact Info

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

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