The Tension of Automation Versus Correctness

Summary

All of the advancements in our technology is based around the principles of abstraction. These are valuable until they break down, which is an inevitable occurrence. In this episode the host Tobias Macey shares his reflections on recent experiences where the abstractions leaked and some observances on how to deal with that situation in a data platform architecture.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
• Your host is Tobias Macey and today I'm sharing some thoughts and observances about abstractions and impedance mismatches from my experience building a data lakehouse with an ELT workflow

Interview

• Introduction
• impact of community tech debt
  • hive metastore
  • new work being done but not widely adopted
• tensions between automation and correctness
• data type mapping
  • integer types
  • complex types
  • naming things (keys/column names from APIs to databases)
• disaggregated databases - pros and cons
  • flexibility and cost control
  • not as much tooling invested vs. Snowflake/BigQuery/Redshift
• data modeling
  • dimensional modeling vs. answering today's questions
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on your data platform?
• When is ELT the wrong choice?
• What do you have planned for the future of your data platform?

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.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
• To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

• dbt
• Airbyte
  • Podcast Episode
• Dagster
  • Podcast Episode
• Trino
  • Podcast Episode
• ELT
• Data Lakehouse
• Snowflake
• BigQuery
• Redshift
• Technical Debt
• Hive Metastore
• AWS Glue

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

Sponsored By:

• Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

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