Summary

Five years of hosting the Data Engineering Podcast has provided Tobias Macey with a wealth of insight into the work of building and operating data systems at a variety of scales and for myriad purposes. In order to condense that acquired knowledge into a format that is useful to everyone Scott Hirleman turns the tables in this episode and asks Tobias about the tactical and strategic aspects of his experiences applying those lessons to the work of building a data platform from scratch.

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 being interviewed by Scott Hirleman about my work on the podcasts and my experience building a data platform

Interview

Introduction
How did you get involved in the area of data management?
Data platform building journey
- Why are you building, who are the users/use cases
- How to focus on doing what matters over cool tools
- How to build a good UX
- Anything surprising or did you discover anything you didn't expect at the start
- How to build so it's modular and can be improved in the future
General build vs buy and vendor selection process
- Obviously have a good BS detector - how can others build theirs
- So many tools, where do you start - capability need, vendor suite offering, etc.
- Anything surprising in doing much of this at once
- How do you think about TCO in build versus buy
- Any advice
Guest call out
- Be brave, believe you are good enough to be on the show
- Look at past episodes and don't pitch the same as what's been on recently
- And vendors, be smart, work with your customers to come up with a good pitch for them as guests...

Tobias' advice and learnings from building out a data platform:

Advice: when considering a tool, start from what are you actually trying to do. Yes, everyone has tools they want to use because they are cool (or some resume-driven development). Once you have a potential tool, is the capabilty you want to use a unloved feature or a main part of the product. If it's a feature, will they give it the care and attention it needs?
Advice: lean heavily on open source. You can fix things yourself and better direct the community's work than just filing a ticket and hoping with a vendor.
Learning: there is likely going to be some painful pieces missing, especially around metadata, as you build out your platform.
Advice: build in a modular way and think of what is my escape hatch? Yes, you have to lock yourself in a bit but build with the possibility of a vendor or a tool going away - whether that is your choice (e.g. too expensive) or it literally disappears (anyone remember FoundationDB?).
Learning: be prepared for tools to connect with each other but the connection to not be as robust as you want. Again, be prepared to have metadata challenges especially.
Advice: build your foundation to be strong. This will limit pain as things evolve and change. You can't build a large building on a bad foundation - or at least it's a BAD idea...
Advice: spend the time to work with your data consumers to figure out what questions they want to answer. Then abstract that to build to general challenges instead of point solutions.
Learning: it's easy to put data in S3 but it can be painfully difficult to query it. There's a missing piece as to how to store it for easy querying, not just the metadata issues.
Advice: it's okay to pay a vendor to lessen pain. But becoming wholly reliant on them can put you in a bad spot.
Advice: look to create paved path / easy path approaches. If someone wants to follow the preset path, it's easy for them. If they want to go their own way, more power to them, but not the data platform team's problem if it isn't working well.
Learning: there will be places you didn't expect to bend - again, that metadata layer for Tobias - to get things done sooner. It's okay to not have the end platform built at launch, move forward and get something going.
Advice: "one of the perennial problems in technlogy is the bias towards speed and action without necessarily understanding the destination." Really consider the path and if you are creating a scalable and maintainable solution instead of pushing for speed to deliver something.
Advice: consider building a buffer layer between upstream sources so if there are changes, it doesn't automatically break things downstream.

Tobias' data platform components: data lakehouse paradigm, Airbyte for data integration (chosen over Meltano), Trino/Starburst Galaxy for distributed querying, AWS S3 for the storage layer, AWS Glue for very basic metadata cataloguing, Dagster as the crucial orchestration layer, dbt

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.
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