Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the. . .
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here. Support the show!16 April 2023Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic - E371Rewind 10 seconds1XSkip 30 seconds ahead0:00/0:00Share on social media:DescriptionTranscriptChaptersSummaryBusiness 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. AnnouncementsHello and welcome to the Data Engineering Podcast, the show about modern data managementRudderStack 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 atdataengineeringpodcast. com/rudderstackYour 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 brandsInterviewIntroductionHow 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 InfoPaul Blankley (LinkedIn)Parting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?Closing AnnouncementsThank 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. TheMachine Learning Podcasthelps you go from idea to production with machine learning. Visit thesiteto 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! Emailhosts@dataengineeringpodcast. com) with your story. To help other people find the show please leave a review onApple Podcastsand tell your friends and co-workersLinksZenlyticOLAP CubeLarge Language ModelStarburstPrompt EngineeringChatGPTThe intro and outro music is fromThe HugbyThe Freak Fandango Orchestra/CC BY-SASponsored By:Rudderstack: RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit[dataengineeringpodcast. com/rudderstack](https://www. dataengineeringpodcast. com/rudderstack)to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener. Support Data Engineering PodcastShare on social media:Listen in your favorite app:PodversePodcast GuruApple PodcastsPocket CastsOvercastSpotifyPodlinkMore optionsHere are shows you might likeSee show recommendationsAI Engineering PodcastTobias MaceyThe Python Podcast. __init__Tobias Macey