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Title & Speakers	Event
Zenlytic Is Building You A Better Coworker With AI Agents 2024-05-19 · 01:00 Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Tobias Macey – host Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Ryan Janssen and Paul Blankley about their experiences building AI powered agents for interacting with your data Interview Introduction How did you get involved in data? In AI? Can you describe what Zenlytic is and the role that AI is playing in your platform? What have been the key stages in your AI journey? What are some of the dead ends that you ran into along the path to where you are today? What are some of the persistent challenges that you are facing? So tell us more about data agents. Firstly, what are data agents and why do you think they're important? How are data agents different from chatbots? Are data agents harder to build? How do you make them work in production? What other technical architectures have you had to develop to support the use of AI in Zenlytic? How have you approached the work of customer education as you introduce this functionality? What are some of the most interesting or erroneous misconceptions that you have heard about what the AI can and can't do? How have you balanced accuracy/trustworthiness with user experience and flexibility in the conversational AI, given the potential for these models to create erroneous responses? What are the most interesting, innovative, or unexpected ways that you have seen your AI agent used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI agent for business intelligence? When is an AI agent the wrong choice? What do you have planned for the future of AI in the Zenlytic product? Contact Info Ryan LinkedIn Paul LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announce AI/ML BI Data Engineering Data Lake Data Lakehouse Data Management Delta GenAI Hive Iceberg Trino	Data Engineering Podcast Listen
Paul Blankley & Ryan Janssen (Zenlytic) - How LLMs will Change Data and Analytics 2023-06-21 · 13:00 Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Joe Reis – founder @ Ternary Data Paul Blankley and Ryan Janssen are the co-founders of Zenlytic. They started a BI company with an LLM-first approach (back before LLM's were insanely cool). We talk about the future of BI, and how LLM's will change the face of data and analytics. Zenlytic: https://www.zenlytic.com/ Paul's LinkedIn: https://www.linkedin.com/in/paulblankley/ Ryan's LinkedIn: https://www.linkedin.com/in/janssenryan/ If you like this show, give it a 5-star rating on your favorite podcast platform. Purchase Fundamentals of Data Engineering at your favorite bookseller. Subscribe to my Substack: https://joereis.substack.com/ Analytics BI Data Engineering LLM	The Joe Reis Show Listen
Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic 2023-04-16 · 23:00 Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Tobias Macey – host 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. 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 [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Zenlytic OLAP Cube Large Language Model Starburst Pr AI/ML BI CDP Data Engineering Data Lake Data Management LLM Python Data Streaming	Data Engineering Podcast Listen

Zenlytic Is Building You A Better Coworker With AI Agents 2024-05-19 · 01:00

Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Tobias Macey – host

Summary

The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Ryan Janssen and Paul Blankley about their experiences building AI powered agents for interacting with your data

Interview

Introduction How did you get involved in data? In AI? Can you describe what Zenlytic is and the role that AI is playing in your platform? What have been the key stages in your AI journey?

What are some of the dead ends that you ran into along the path to where you are today? What are some of the persistent challenges that you are facing?

So tell us more about data agents. Firstly, what are data agents and why do you think they're important? How are data agents different from chatbots? Are data agents harder to build? How do you make them work in production? What other technical architectures have you had to develop to support the use of AI in Zenlytic? How have you approached the work of customer education as you introduce this functionality? What are some of the most interesting or erroneous misconceptions that you have heard about what the AI can and can't do? How have you balanced accuracy/trustworthiness with user experience and flexibility in the conversational AI, given the potential for these models to create erroneous responses? What are the most interesting, innovative, or unexpected ways that you have seen your AI agent used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI agent for business intelligence? When is an AI agent the wrong choice? What do you have planned for the future of AI in the Zenlytic product?

Contact Info

Ryan

Paul

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announce

AI/ML BI Data Engineering Data Lake Data Lakehouse Data Management Delta GenAI Hive Iceberg Trino

Data Engineering Podcast

Listen

Paul Blankley & Ryan Janssen (Zenlytic) - How LLMs will Change Data and Analytics 2023-06-21 · 13:00

Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Joe Reis – founder @ Ternary Data

Paul Blankley and Ryan Janssen are the co-founders of Zenlytic. They started a BI company with an LLM-first approach (back before LLM's were insanely cool). We talk about the future of BI, and how LLM's will change the face of data and analytics.

Zenlytic: https://www.zenlytic.com/

Paul's LinkedIn: https://www.linkedin.com/in/paulblankley/

Ryan's LinkedIn: https://www.linkedin.com/in/janssenryan/

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Analytics BI Data Engineering LLM

The Joe Reis Show

Listen

Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic 2023-04-16 · 23:00

Ryan Janssen – guest @ Zenlytic , Paul Blankley – guest @ Zenlytic , Tobias Macey – host

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. 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 [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Zenlytic OLAP Cube Large Language Model Starburst Pr

AI/ML BI CDP Data Engineering Data Lake Data Management LLM Python Data Streaming

Data Engineering Podcast

Listen

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