Introduction

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
• Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
• You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
• This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold
• 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

• LinkedIn
• Join our community to discuss further

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.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

• dlt
  • Harness Success Story
• Our guiding product principles
• Ecosystem support
• From basic to complex, dlt has many capabilities
• Singer
• Airbyte
  • Podcast Episode
• Meltano
  • Podcast Episode
• Matillion
  • Podcast Episode
• Fivetran
  • Podcast Episode
• DuckDB
  • Podcast Episode
• OpenAPI
• Data Mesh
  • Podcast Episode
• SQLMesh
  • Podcast Episode
• Airflow
• Dagster
  • Podcast Episode
• Prefect
  • Podcast Episode
• Alto

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

Sponsored By:

• Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
• Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!
• Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today!

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