Summary

Business intellingence has been chasing the promise of self-serve data for decades. As the capabilities of these systems has improved and become more accessible, the target of what self-serve means changes. With the availability of AI powered by large language models combined with the evolution of semantic layers, the team at Zenlytic have taken aim at this problem again. In this episode Paul Blankley and Ryan Janssen explore the power of natural language driven data exploration combined with semantic modeling that enables an intuitive way for everyone in the business to access the data that they need to succeed in their work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Paul Blankley and Ryan Janssen about Zenlytic, a no-code business intelligence tool focused on emerging commerce brands

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Zenlytic is and the story behind it?
Business intelligence is a crowded market. What was your process for defining the problem you are focused on solving and the method to achieve that outcome?
Self-serve data exploration has been attempted in myriad ways over successive generations of BI and data platforms. What are the barriers that have been the most challenging to overcome in that effort?
- What are the elements that are coming together now that give you confidence in being able to deliver on that?
Can you describe how Zenlytic is implemented?
- What are the evolutions in the understanding and implementation of semantic layers that provide a sufficient substrate for operating on?
- How have the recent breakthroughs in large language models (LLMs) improved your ability to build features in Zenlytic?
- What is your process for adding domain semantics to the operational aspect of your LLM?
For someone using Zenlytic, what is the process for getting it set up and integrated with their data?
Once it is operational, can you describe some typical workflows for using Zenlytic in a business context?
- Who are the target users?
- What are the collaboration options available?
What are the most complex engineering/data challenges that you have had to address in building Zenlytic?
What are the most interesting, innovative, or unexpected ways that you have seen Zenlytic used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Zenlytic?
When is Zenlytic the wrong choice?
What do you have planned for the future of Zenlytic?

Contact Info

Paul Blankley (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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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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