Bringing The Modern Data Stack To Everyone With Y42
Data Engineering Podcast
00:00
Machine Learning - What Are Some of the Most Unexpected Ways That You've Been Using It?
The y forty two tool allows users to run machine learning models on top of some tables that they create. It basically allows them to understand the business in ways they haven't been able before. There are some funny stuff that happens also, but it just points to the complexity that's inherent in all these systems.
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Summary
Cloud services have made highly scalable and performant data platforms economical and manageable for data teams. However, they are still challenging to work with and manage for anyone who isn’t in a technical role. Hung Dang understood the need to make data more accessible to the entire organization and created Y42 as a better user experience on top of the "modern data stack". In this episode he shares how he designed the platform to support the full spectrum of technical expertise in an organization and the interesting engineering challenges involved.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl
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- Your host is Tobias Macey and today I’m interviewing Hung Dang about Y42, the full-stack data platform that anyone can run
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Y42 is and the story behind it?
- How would you characterize your positioning in the data ecosystem?
- What are the problems that you are trying to solve?
- Who are the personas that you optimize for and how does that manifest in your product design and feature priorities?
- How is the Y42 platform implemented?
- What are the core engineering problems that you have had to address in order to tie together the various underlying services that you integrate?
- How have the design and goals of the product changed or evolved since you started working on it?
- What are the sharp edges and failure conditions that you have had to automate around in order to support non-technical users?
- What is the process for integrating Y42 with an organization’s data systems?
- What is the story for onboarding from existing systems and importing workflows (e.g. Airflow dags and dbt models)?
- With your recent shift to using Git as the store of platform state, how do you approach the problem of reconciling branched changes with side effects from changes (e.g. creating tables or mutating table structures in the warehouse)?
- Can you describe a typical workflow for building or modifying a business dashboard or activating data in the warehouse?
- What are the interfaces and abstractions that you have built into the platform to support collaboration across roles and levels of experience? (technical or organizational)
- With your focus on end-to-end support for data analysis, what are the extension points or escape hatches for use cases that you can’t support out of the box?
- What are the most interesting, innovative, or unexpected ways that you have seen Y42 used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Y42?
- When is Y42 the wrong choice?
- What do you have planned for the future of Y42?
Contact Info
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- 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 iTunes and tell your friends and co-workers
Links
- Y42
- CDTM (Center for Digital Technology and Management)
- Meltano
- Airflow
- Singer
- dbt
- Great Expectations
- Airbyte
- Grouparoo
- Terraform
- OpenTelemetry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- PostHog:  PostHog is an open source, product analytics platform. PostHog enables software teams to understand user behavior – auto-capturing events, performing product analytics and dashboarding, enabling video replays, and rolling out new features behind feature flags, all based on their single open source platform. The product’s open source approach enables companies to self-host, removing the need to send data externally. Try it out today at [dataengineeringpodcast.com/posthog](https://www.dataengineeringpodcast.com/posthog)
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