Creating A Unified Experience For The Modern Data Stack At Mozart Data
Data Engineering Podcast
How to Build a High Power Data Engineer or Data Scientist to a Data Warehouse?
You have access to snufling warehouse, so anything that you want to connect. I think high ceiling is the part that's interesting, which is how as somebody picks up additional data proficiencies and has additional sort of data interests or data needs. And this is a challenge for any company building any tool, how can you both serve a novice, but yet expose these sort of escape hatches?
Summary
The modern data stack has been gaining a lot of attention recently with a rapidly growing set of managed services for different stages of the data lifecycle. With all of the available options it is possible to run a scalable, production grade data platform with a small team, but there are still sharp edges and integration challenges to work through. Peter Fishman and Dan Silberman experienced these difficulties firsthand and created Mozart Data to provide a single, easy to use option for getting started with the modern data stack. In this episode they explain how they designed a user experience to make working with data more accessibly by organizations without a data team, while allowing for more advanced users to build out more complex workflows. They also share their thoughts on the modern data ecosystem and how it improves the availability of analytics for companies of all sizes.
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 interviewing Peter Fishman and Dan Silberman about Mozart Data and how they are building a unified experience for the modern data stack
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Mozart Data is and the story behind it?
- The promise of the "modern data stack" is that it’s all delivered as a service to make it easier to set up. What are the missing pieces that make something like Mozart necessary?
- What are the main workflows or industries that you are focusing on?
- Who are the main personas that you are building Mozart for?
- How has that combination of user persona and industry focus informed your decisions around feature priorities and user experience?
- Can you describe how you have architected the Mozart platform?
- How have you approached the build vs. buy decision internally?
- What are some of the most interesting or challenging engineering projects that you have had to work on while building Mozart?
- What are the stages of the data lifecycle that you work the hardest to automate, and which do you focus on exposing to customers?
- What are the edge cases in what customers might try to do in the bounds of Mozart, or areas where you have explicitly decided not to include in your features?
- What are the options for extensibility, or custom engineering when customers encounter those situations?
- What do you see as the next phase in the evolution of the data stack?
- What are the most interesting, innovative, or unexpected ways that you have seen Mozart used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Mozart?
- When is Mozart the wrong choice?
- What do you have planned for the future of Mozart?
Contact Info
- Peter
- @peterfishman on Twitter
- Dan
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.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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