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.
- 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
- 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?
- 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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- Data Lakehouse
- Technical Debt
- Hive Metastore
- AWS Glue