Podcast appearances and mentions of jean georges perrin

Tim and Juan meet in person with Jean-Georges Perrin for a longer-than-usual episode. Together, they unpack the real meaning behind data contracts, explaining how these agreements serve as the backbone for successful data products and the practical framework for data mesh implementation. If you want to cut through all the jargon and buzzwords and get to an Honest, No-BS discussion, this is the episode for you!

Tim and Juan meet in person with Jean-Georges Perrin for a longer-than-usual episode. Together, they unpack the real meaning behind data contracts, explaining how these agreements serve as the backbone for successful data products and the practical framework for data mesh implementation. If you want to cut through all the jargon and buzzwords and get to an Honest, No-BS discussion, this is the episode for you!

Nick Tune and Jean-Georges Perrin join host Giovanni Asproni to talk about their proposed approach to modernizing legacy systems. The episode starts with some high-level perspective to set context for the approach described in their book, Architecture Modernization (Manning, 2024). From there, the discussion turns to important details, including criteria for deciding which aspects to revisit; some of the activities, processes, and tools; and the importance of data engineering in modernization efforts. Nick and Jean-Georges describe how to successfully implement an architecture-modernization effort, and how to fit that work with the teams' other priorities. The episode finishes with some warnings about the typical risks associated with modernizing a legacy system, and suggestions on how to mitigate them. This episode is sponsored by QA Wolf.

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. We've released a special edition series of minisodes of our podcast. Recorded live at Data Connect 2024, our host Michael Toland engages in short, sweet, informative, and delightful conversations with five prevelant practitioners who are forging their way forward in data and technology. About our host Michael Toland: Michael is a Product Management Coach and Consultant with Pathfinder Product, a Test Double Operation. Since 2016, Michael has worked on large-scale system modernizations and migration initiatives at Verizon. Outside his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors with Venture for America, sings with the Columbus Symphony, and writes satire for his blog Dignified Product. He is excited to discuss data product management with the podcast audience. Connect with Michael on LinkedIn About our guest Jean-Georges Perrin: Jean-Georges “jgp” Perrin is the Chief Innovation Officer at AbeaData, where he focuses on developing cutting-edge data tooling. He chairs the Open Data Contract Standard (ODCS) at the Linux Foundation's Bitol project, co-founded the AIDA User Group, and has authored several influential books, including Implementing Data Mesh (O'Reilly) and Spark in Action, 2nd Edition (Manning). With over 25 years in IT, Jean-Georges is recognized as a Lifetime IBM Champion, a PayPal Champion, and a Data Mesh MVP. His expertise spans data engineering, governance, and the industrialization of data science. Outside of tech, he enjoys exploring Upstate New York and New England with his family. Connect with J-GP on LinkedIn.  All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. Join the conversation on LinkedIn. Apply to be a guest or nominate a practitioner.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!

Jean-Georges Perrin joins the show to chat about data mesh, data contracts, modern data engineering standards, Bitol (is open standard project with the Linux Foundation), data architecture, and much more. This is a wide-reaching discussion. Enjoy!

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/Amy's LinkedIn: https://www.linkedin.com/in/amy-raygada/Andrew's LinkedIn: https://www.linkedin.com/in/andrewrhysjones/Andrew's website: https://andrew-jones.com/daily/Andrew's book: https://data-contracts.com/Data contract standard project Bitol: https://lfaidata.foundation/projects/bitol/JGP's blog: https://jgp.ai/In this episode, guest host Jean-Georges Perrin, Data Innovation Consultant at ProfitOptics (guest of episode #130 and panelist in episode #227), facilitated a discussion with Amy Raygada, Senior Data Product Manager at Swiss Marketplace Group (guest of episode #165), and Andrew Jones, Principal Engineer and Author of the book on Data Contracts (guest of episode #29). As per usual, all guests were only reflecting their own views.The topic for this panel was all about data contracts and how do we go about getting them in place. Much of it was about the general concept but some of it was specifically about how do we think about data contracts applying to data mesh. This was the first topic I really did a deep dive into in early 2022 and it has evolved but is definitely still evolving.Scott note: As per usual, I share my takeaways rather than trying to reflect the nuance of the panelists' views individually.Scott's Top Takeaways:Data contracts are about trust and understanding. Trust that there is an owner and there are rules, there is a minder that knows this data matters. Trust that things...

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode #130 with my partner in our weekly data mesh roundtables Jean-Georges Perrin. There are a lot of interesting things to take away from this. A biggie is to have an early thesis about what to drive towards - what will drive value early? Doing data mesh doesn't simply create value. And you need to build momentum. There's a lot here to learn about how to apply good software engineering practices to data with data mesh.Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Data Mesh at PayPal blog post: https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522JGP's All Things Open talk (free virtual registration): https://2022.allthingsopen.org/sessions/building-a-data-mesh-with-open-source-technologies/JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/JGP's Twitter: @jgperrin / https://twitter.com/jgperrinJGP's YouTube: https://www.youtube.com/c/JeanGeorgesPerrinJGP's Website: https://jgp.ai/In this episode, Scott interviewed Jean-Georges Perrin AKA JGP, Intelligence Platform Lead at PayPal. JGP is probably the first guest to lean

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Paolo's LinkedIn: https://www.linkedin.com/in/paoloplatter/Paolo's Medium (multiple data mesh articles): https://medium.com/@p-platterAgile Lab's website: https://www.agilelab.it/Manisha's LinkedIn: https://www.linkedin.com/in/evermanisha/'A streamlined developer experience in Data Mesh' blog post by Manisha: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-platform'A streamlined developer experience in Data Mesh (Pt. two)' blog post by Manisha: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-product'Data Mesh Accelerate Workshop' blog post by Thoughtworks: https://martinfowler.com/articles/data-mesh-accelerate-workshop.htmlMax's LinkedIn: https://www.linkedin.com/in/max-schultze/Max's Data Mesh Learning meetup presentation:

Summary There has been a lot of discussion about the practical application of data mesh and how to implement it in an organization. Jean-Georges Perrin was tasked with designing a new data platform implementation at PayPal and wound up building a data mesh. In this episode he shares that journey and the combination of technical and organizational challenges that he encountered in the process. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it's all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender (https://www.dataengineeringpodcast.com/timextender) where you can do two things: watch us build a data estate in 15 minutes and start for free today. Your host is Tobias Macey and today I'm interviewing Jean-Georges Perrin about his work at PayPal to implement a data mesh and the role of data contracts in making it work Interview Introduction How did you get involved in the area of data management? Can you start by describing the goals and scope of your work at PayPal to implement a data mesh? What are the core problems that you were addressing with this project? Is a data mesh ever "done"? What was your experience engaging at the organizational level to identify the granularity and ownership of the data products that were needed in the initial iteration? What was the impact of leading multiple teams on the design of how to implement communication/contracts throughout the mesh? What are the technical systems that you are relying on to power the different data domains? What is your philosophy on enforcing uniformity in technical systems vs. relying on interface definitions as the unit of consistency? What are the biggest challenges (technical and procedural) that you have encountered during your implementation? How are you managing visibility/auditability across the different data domains? (e.g. observability, data quality, etc.) What are the most interesting, innovative, or unexpected ways that you have seen PayPal's data mesh used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh? When is a data mesh the wrong choice? What do you have planned for the future of your data mesh at PayPal? Contact Info LinkedIn (https://www.linkedin.com/in/jgperrin/) Blog (https://jgp.ai/) 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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Data Mesh (https://www.thoughtworks.com/en-us/what-we-do/data-and-ai/data-mesh) O'Reilly Book (https://amzn.to/3Z5nC8T) (affiliate link) The next generation of Data Platforms is the Data Mesh (https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522) PayPal (https://about.pypl.com/about-us/default.aspx) Conway's Law (https://en.wikipedia.org/wiki/Conway%27s_law) Data Mesh For All Ages - US (https://amzn.to/3YzVRop), Data Mesh For All Ages - UK (https://amzn.to/3YzVRop) Data Mesh Radio (https://daappod.com/data-mesh-radio/) Data Mesh Community (https://datameshlearning.com/) Data Mesh In Action (http://jgp.ai/dmia) Great Expectations (https://greatexpectations.io/) Podcast Episode (https://www.dataengineeringpodcast.com/great-expectations-technical-debt-data-pipeline-episode-117/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) Transcript for this episode (https://docs.google.com/document/d/1uQ-o4BewpWADsUWTUXz6vayygnIiqAnp-q0X_PS6We4/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodmcenter&utm_term= (here). You can download their Data Mesh for Dummies e-book (info gated) https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here). Data Mesh at PayPal blog post: https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522 (https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522) JGP's All Things Open talk (free virtual registration): https://2022.allthingsopen.org/sessions/building-a-data-mesh-with-open-source-technologies/ (https://2022.allthingsopen.org/sessions/building-a-data-mesh-with-open-source-technologies/) JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/ (https://www.linkedin.com/in/jgperrin/) JGP's Twitter: @jgperrin / https://twitter.com/jgperrin (https://twitter.com/jgperrin) JGP's YouTube: https://www.youtube.com/c/JeanGeorgesPerrin (https://www.youtube.com/c/JeanGeorgesPerrin) JGP's Website: https://jgp.ai/ (https://jgp.ai/) In this episode, Scott interviewed Jean-Georges Perrin AKA JGP, Intelligence Platform Lead at PayPal. JGP is probably the first guest to lean into using "data quantum" instead of "data product". JGP did want to emphasize that as of now, he was only discussing the implementation for his team the GCSC IA (Global Credit Risk, Seller Risk, Collections Intelligence Automation) within PayPal. Some key takeaways/thoughts from JGP's point of view: Data mesh as it's been laid out by Zhamak obviously leaves a lot of room for innovation. For some, that's great. For others, they want the blueprint. And it's okay to wait for the blueprint. But JGP and team are excited to innovate! PayPal's 3 main initial target outcomes from data mesh: A) faster and easier data discovery, B) easier to use the data in a governed way, and C) increase data consumer trust in data. PayPal's initial data consumers are data scientists so their platform and data quanta are built to serve that audience first. Really consider what you want to prove out in your MVP. Is that minimum viable A) data quantum, B) data platform, C) data mesh, or D) something else? Only doing a data quantum probably sets you up for trouble and a platform only won't be tested until it has data quanta on it. Data contracts are crucial to making trustability actually measurable and agreed upon. Otherwise, it's far too easy to have miscommunication between data producers and consumers, which leads to lack/loss of trust. Producers, don't set your data contract terms too strictly when first launching a data quantum. There's no need to over-engineer - despite how interesting that can sometimes be... For too long, we have tried to keep software...

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Jean-Georges Perrin, Director of Engineering at weexperience. Together, they discuss — and compare — Apache Spark and Hadoop, and explain what it means to hold the title of IBM Champion.Show Notes02:07 - Connect with Jean-Georges Perrin on LinkedIn and Twitter, and check out his website.13:14 - Check out Jean-Georges' book on Apache Spark.24:38 - What does it mean to be an IBM Champion?Connect with the TeamProducer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter. 

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Jean-Georges Perrin, Director of Engineering at weexperience. Together, they discuss — and compare — Apache Spark and Hadoop, and explain what it means to hold the title of IBM Champion.Show Notes02:07 - Connect with Jean-Georges Perrin on LinkedIn and Twitter, and check out his website.13:14 - Check out Jean-Georges' book on Apache Spark.24:38 - What does it mean to be an IBM Champion?Connect with the TeamProducer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter. 

Jean-Georges Perrin compares Apache Spark to an operating system for data management. He explains how it can be used to pull data from disparate data sources, process the data, feed it into Machine Learning algorithms and stream the data out to other data streaming services. Coupon code: podmladventure20 Panel Charles Wood Guest Jean-Georges Perrin Sponsors Next Level Mastermind Picks Charles- Talk to people you don’t agree with and try to understand their point of view. Charles- Eureka on Amazon Prime Charles- The Expanse books and series

As we stretch the technology stack in this episode, I rely on my trusty co-hosts to get us through. Kevin and Jean-Georges Perrin do a great job giving us their thoughts on Spark. I hope you will enjoy the episode. The show notes for today's episode can be found at https://sqldatapartners.com/2020/08/06/episode-203-spark-in-action. Have fun on the SQL Trail!

Our guest for this episode is Jean-Georges Perrin, the author of Spark in Action, 2nd edition. We talk about his career path (he's been doing "big data" since before the term existed), what inspired him to write Spark in Action, and where Spark fits in your company's data efforts. He also shares his thoughts on data quality.

In this episode, Jean Georges Perrin, Software Architect and IBM Champion talks to us about the benefits of Spark for data analysis. We also go into the motivation for writing his new book Spark in Action which allows developers to get the benefits of Apache Spark in Java and without having to learn the intricacies of Scala. --- Send in a voice message: https://anchor.fm/databasement/message

In this episode I speak with Jean Georges Perrin about his forthcoming book "Spark in Action", where we cover Apache Spark, writing books, and why many are wrong about IBM. I also cover Twine, Open Source and Alexa and the UK's health service. https://www.manning.com/books/spark-in-action-second-edition Discount code for any Manning books for you all: podchinc19 And 5 free eBook codes for Jean's book if it caught your interest! spchc-3424 spchc-552D spchc-445B spchc-3087 spchc-F63D They are already activated and will last for two months. --- Send in a voice message: https://anchor.fm/theweeklysqueak/message

And now for something completely different: a book review! Not something we have done before, but when Jean-Georges Perrin contacted us with the suggestion of taking a deeper look at the "Spark in Action" book he is currently writing, we certainly did not say no! However, in al honesty, we talked about much, much more... Free eBook raffle Manning Publication has been kind enough to give us a couple of download codes for a free eBook version of "Spark in Action". As always, our Patreons get a first chance to get their hands on one of the codes. If you are a Roaring V.I.P. (or higher), you can head over to our Patreon Page now where you will find a  posts containing all the information required. If you become a Patreon now, you immediately get access tot that post! ;) After one week, if there are any codes left, there will be a tweet about what you can do to get a free code, even if you are not a Patreon. A book review on Spark in Action, second edition with author Jean-Georges Perrin In the second part we go deeper into the book, going over the available chapters and appendices. We cover a number of topics and concepts like the layout of a typical data lake, the four pillars of Apache Spark and more. We end the interview with a discussion on what it's like to write a technical book like Spark in Action. Our thanks to Jean-Georges for spending quite a bit of time with us talking about Apache Spark and to Manning Publication for the free eBook codes! Find out more about Jean-Georges at his blog: https://jgp.net/ Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

And now for something completely different: a book review! Not something we have done before, but when Jean-Georges Perrin contacted us with the suggestion of taking a deeper look at the "Spark in Action" book he is currently writing, we certainly did not say no! However, in al honesty, we talked about much, much more... Free eBook raffle Manning Publication has been kind enough to give us a couple of download codes for a free eBook version of "Spark in Action". As always, our Patreons get a first chance to get their hands on one of the codes. If you are a Roaring V.I.P. (or higher), you can head over to our Patreon Page now where you will find a  posts containing all the information required. If you become a Patreon now, you immediately get access tot that post! ;) After one week, if there are any codes left, there will be a tweet about what you can do to get a free code, even if you are not a Patreon. A book review on Spark in Action, second edition with author Jean-Georges Perrin In this first part of the interview, we meet the author and talk about Apache Spark and Open Source in general. We also cover the MEAP system used by Manning Publication to get books like these in the hands of the readers as soon as possible while allowing early readers to help shape the book. Our thanks to Jean-Georges for spending quite a bit of time with us talking about Apache Spark and to Manning Publication for the free eBook codes! Find out more about Jean-Georges at his blog: https://jgp.net/ Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Al Martin is joined this week by guest Jean-Georges Perrin, Director of Engineering at weexperience. Together, they discuss — and compare — Apache Spark and Hadoop, and explain what it means to hold the title of IBM Champion.Show NotesCheck us out on:- YouTube- Apple Podcasts- Google Play Music- Spotify- TuneIn- Stitcher00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter. 00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter. 00:20 - Connect with Producer Rachit Sharma on LinkedIn. 00:25 - Connect with Host Al Martin on LinkedIn and Twitter. 02:07 - Connect with Jean-Georges Perrin on LinkedIn and Twitter, and check out his website.13:14 - Check out Jean-Georges' book on Apache Spark.24:38 - What does it mean to be an IBM Champion?

Al Martin is joined this week by guest Jean-Georges Perrin, Director of Engineering at weexperience. Together, they discuss — and compare — Apache Spark and Hadoop, and explain what it means to hold the title of IBM Champion.Show NotesCheck us out on:- YouTube- Apple Podcasts- Google Play Music- Spotify- TuneIn- Stitcher00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter. 00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter. 00:20 - Connect with Producer Rachit Sharma on LinkedIn. 00:25 - Connect with Host Al Martin on LinkedIn and Twitter. 02:07 - Connect with Jean-Georges Perrin on LinkedIn and Twitter, and check out his website.13:14 - Check out Jean-Georges' book on Apache Spark.24:38 - What does it mean to be an IBM Champion?

Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem.