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
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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?
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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