Adding Context And Comprehension To Your Analytics Through Data Discovery With SelectStar
Data Engineering Podcast
What Have You Learned From Building Select Star?
The contexts of the data unlocks a lot of value for our customers, whether they are small or large. We also have very small companies using select star because they want to make sure that they are building their data bottles in the right way. That was like one kind of discovery that we had that we didn't realize before. The other part are related to more of different use cases where data discovery can be helpful. Those are some unexpected usage of a select star that we started learning about,. which we are all so very excited to support.
Summary
Companies of all sizes and industries are trying to use the data that they and their customers generate to survive and thrive in the modern economy. As a result, they are relying on a constantly growing number of data sources being accessed by an increasingly varied set of users. In order to help data consumers find and understand the data is available, and help the data producers understand how to prioritize their work, SelectStar has built a data discovery platform that brings everyone together. In this episode Shinji Kim shares her experience as a data professional struggling to collaborate with her colleagues and how that led her to founding a company to address that problem. She also discusses the combination of technical and social challenges that need to be solved for everyone to gain context and comprehension around their most valuable asset.
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 Shinji Kim about SelectStar, an intelligent data discovery platform that helps you understand your data
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what SelectStar is and the story behind it?
- What are the core challenges that organizations are facing around data cataloging and discovery?
- There has been a surge in tools and services for metadata collection, data catalogs, and data collaboration. How would you characterize the current state of the ecosystem?
- What is SelectStar’s role in the space?
- Who are your target customers and how does that shape your prioritization of features and the user experience design?
- Can you describe how SelectStar is architected?
- How have the goals and design of the platform shifted or evolved since you first began working on it?
- I understand that you have built integrations with a number of BI and dashboarding tools such as Looker, Tableau, Superset, etc. What are the use cases that those integrations enable?
- What are the challenges or complexities involved in building and maintaining those integrations?
- What are the other categories of integration that you have had to implement to make SelectStar a viable solution?
- Can you describe the workflow of a team that is using SelectStar to collaborate on data engineering and analytics?
- What have been the most complex or difficult problems to solve for?
- What are the most interesting, innovative, or unexpected ways that you have seen SelectStar used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SelectStar?
- When is SelectStar the wrong choice?
- What do you have planned for the future of SelectStar?
Contact Info
- @shinjikim on Twitter
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.
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Links
- SelectStar
- University of Waterloo
- Kafka
- Storm
- Concord Systems
- Akamai
- Snowflake
- BigQuery
- Looker
- Tableau
- dbt
- OpenLineage
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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