As organizations grapple with fragmented data, siloed teams, and inconsistent pipelines, data products have emerged as a practical solution for delivering trusted, scalable, and reusable data assets. In Building Data Products, Jean-Georges Perrin provides a comprehensive, standards-driven playbook for designing, implementing, and scaling data products that fuel innovation and cross-functional collaboration—whether or not your organization adopts a full data mesh strategy. Drawing on extensive industry experience and practitioner interviews, Perrin shows readers how to build metadata-rich, governed data products aligned to business domains. Covering foundational concepts, real-world use cases, and emerging standards like Bitol ODPS and ODCS, this guide offers step-by-step implementation advice and practical code examples for key stages—ownership, observability, active metadata, compliance, and integration. Design data products for modular reuse, discoverability, and trust Implement standards-driven architectures with rich metadata and security Incorporate AI-driven automation, SBOMs, and data contracts Scale product-driven data strategies across teams and platforms Integrate data products into APIs, CI/CD pipelines, and DevOps practices

Bien menée, la gouvernance devient moteur : les Data Contracts (ODCS) rendent pipelines data/IA précis, fiables & conformes, sans blocages.

Bien menée, la gouvernance est un moteur de croissance. Durant cette session, Jean-Georges Perrin montrera comment les data contracts apportent précision, confiance et responsabilité à vos pipelines données et IA, sans créer de goulots d'étranglement. En utilisant l'Open Data Contract Standard (ODCS) du projet Bitol de la Fondation Linux, vous découvrirez comment les organisations peuvent réduire les défauts en aval, accélérer l'intégration des modèles IA, réduire les risques de conformité et simplifier la gestion des incidents, souvent en quelques jours seulement.

When done right, governance is a growth engine. In this talk, Jean-Georges “jgp” Perrin will show how data contracts bring precision, trust, and accountability into your data and AI pipelines—without creating bottlenecks. Using the Open Data Contract Standard (ODCS) from the Linux Foundation’s Bitol project, you’ll see how organizations can cut downstream defects, accelerate AI model onboarding, lower compliance risk, and reduce firefighting—often in just days.

https://www.bigdataldn.com/en-gb/conference/session-details.4500.251781.the-high-performance-data-and-ai-debate.html

Eric and Jean-Georges Perrin are talking about their upcoming book Implementing Data Mesh, but also about actually executing Data Mesh through an open-source project. 

As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code. Data engineers, architects, and analysts will explore a simple and intuitive process for identifying key data mesh components and data products. You'll learn a consistent set of interfaces and access methods that make data products easy to consume. This approach ensures that your data products are easily accessible and the data mesh ecosystem is easy to navigate. This book helps you: Identify, define, and build data products that interoperate within an enterprise data mesh Build a data mesh fabric that binds data products together Build and deploy data products in a data mesh Establish the organizational structure to operate data products, data platforms, and data fabric Learn an innovative architecture that brings data products and data fabric together into the data mesh About the authors: Jean-Georges "JG" Perrin is a technology leader focusing on building innovative and modern data platforms. Eric Broda is a technology executive, practitioner, and founder of a boutique consulting firm that helps global enterprises realize value from data.

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 is a serial startup founder, currently co-founder of AbeaData [https://abeadata.com/], and co-author of "Implementing Data Mesh." He is the one who championed the PayPal's data contract project, which is now part of Bitol and the Linux Foundation. In this episode, JGP speaks about building and maintaining open-source data contract solutions using open standards. He shares a lot about why and how he came to it and the challenges of maintaining it to avoid appropriation of the solution. JGP discusses how they balance the interests of different groups in developing a community around open data contract standards. More importantly, he shares how data contracts can positively change the life of every data engineer.Check out JGP's LinkedInCheck out Bitol -  Open Standards for Data Contracts and become a contributor.

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 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 Blog

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

Data Mesh

O'Reilly Book (affiliate link)

The next generation of Data Platforms is the Data Mesh PayPal Conway's Law Data Mesh For All Ages - US, Data Mesh For All Ages - UK Data Mesh Radio Data Mesh Community Data Mesh In Action Great Expectations

Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: TimeXtender: TimeXtender is a holistic, metadata-driven solution for data integration, optimized for agility. TimeXtender provides all the features you need to build a future-proof infrastructure for ingesting, transforming, modelling, and delivering clean, reliable data in the fastest, most efficient way possible.

You can't optimize for everything all at once. That's why we take a holistic approach to data integration that optimises for agility instead of fragmentation. By unifying each layer of the data stack, TimeXtender empowers you to build data solutions 10x faster while reducing costs by 70%-80%. We do this for one simple reason: because time matters.

Go to dataengineeringpodcast.com/timextender today to get started for free!Support Data Engineering Podcast

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted 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 Notes 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? Connect with the Team Producer 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 [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. About the Technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the Book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's Inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the Reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the Author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Quotes This book reveals the tools and secrets you need to drive innovation in your company or community. - Rob Thomas, IBM An indispensable, well-paced, and in-depth guide. A must-have for anyone into big data and real-time stream processing. - Anupam Sengupta, GuardHat Inc. This book will help spark a love affair with distributed processing. - Conor Redmond, InComm Product Control Currently the best book on the subject! - Markus Breuer, Materna IPS

Send us a text 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 Notes Check us out on: - YouTube - Apple Podcasts - Google Play Music - Spotify - TuneIn - Stitcher 00:10 - 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? Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary

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.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jean Georges Perrin, author of the upcoming Manning book Spark In Action 2nd Edition, about the ways that Spark is used and how it fits into the data landscape

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Spark is?

What are some of the main use cases for Spark? What are some of the problems that Spark is uniquely suited to address? Who uses Spark?

What are the tools offered to Spark users? How does it compare to some of the other streaming frameworks such as Flink, Kafka, or Storm? For someone building on top of Spark what are the main software design paradigms?

How does the design of an application change as you go from a local development environment to a production cluster?

Once your application is written, what is involved in deploying it to a production environment? What are some of the most useful strategies that you have seen for improving the efficiency and performance of a processing pipeline? What are some of the edge cases and architectural considerations that engineers should be considering as they begin to scale their deployments? What are some of the common ways that Spark is deployed, in terms of the cluster topology and the supporting technologies? What are the limitations of the Spark programming model?

What are the cases where Spark is the wrong choice?

What was your motivation for writing a book about Spark?

Who is the target audience?

What have been some of the most interesting or useful lessons that you have learned in the process of writing a book about Spark? What advice do you have for anyone who is considering or currently using Spark?

Contact Info

@jgperrin on Twitter Blog

Parting Question

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

Book Discount

Use the code poddataeng18 to get 40% off of all of Manning’s products at manning.com

Links

Apache Spark Spark In Action Book code examples in GitHub Informix International Informix Users Group MySQL Microsoft SQL Server ETL (Extract, Transform, Load) Spark SQL and Spark In Action‘s chapter 11 Spark ML and Spark In Action‘s chapter 18 Spark Streaming (structured) and Spark In Action‘s chapter 10 Spark GraphX Hadoop Jupyter

Podcast Interview

Zeppelin Databricks IBM Watson Studio Kafka Flink

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