The Data Products Series
Designing Data Products.
Finally, a Practical Guide
to Doing Data Right.
From broken pipelines to boardroom buy-in. Learn what Data Products really are, why they matter, and how to build them. No buzzwords.
It started on a honeymoon in Seville. The tapas were calling, but the CTO called first. A critical reporting pipeline had failed. Nobody on the team even knew it existed. Sound familiar? Designing Data Products was born from two decades of exactly these moments: the sleepless nights, the firefights, the finger-pointing between business and tech. This book cuts through the hype and gives you a clear, structured path to building Data Products that actually deliver value.
Published by Technics Publications · Edited by Steve Hoberman · Cover by Lorena Molinari
Built for Practitioners, Not Theorists
Whether you're knee-deep in pipelines or presenting to the board. This book speaks your language.
Data Engineers & Architects
Move beyond pipeline plumbing toward product thinking. Learn how technical decisions connect to business outcomes and how to make the case for better architecture.
Data Product Owners & Managers
Get the framework to prioritize, fund, and scale Data Products. A scoring model that connects technical feasibility to financial impact. Executive buy-in follows.
Business Leaders & CDOs
Gain the language to bridge the gap between tech teams and the boardroom. Understand what you're funding, why it matters, and how to measure success.
Curious Minds & Newcomers
New to Data Products? This book is your jargon-free starting point. It connects business value with technical reality without losing you in abstract theory.
"This book is for everyone who wants to understand what Data Products are, without getting lost in technical jargon."
Mario Meir-Huber, Author
Business Meets Technology: In Three Acts
Eight chapters. Three dimensions. One clear path from confusion to mastery.
Meeting Data Products
What they are. What they aren't. Why they matter.
Strip away the confusion. Get a crystal-clear definition and discover what separates a real Data Product from "just a dashboard." Two original frameworks make the complexity manageable from day one.
- The GAP Triad: Governance, Architecture & People
- The DRIVE Framework: the end-to-end lifecycle
- CIA: Continuous Improvement & Adaptation
- What's a dashboard vs. a real Data Product?
The Business Dimension
Win funding, prove value, and build teams that scale.
The part most data books skip entirely. A practical scoring framework measuring financial impact AND technical feasibility. Build roadmaps that actually get executive buy-in.
- Calculating financial & strategic value
- Prioritization: Low-Hanging Fruits vs. Challengers
- The Hub-and-Spoke organizational model
- Virtual squads, chapters & the Data Product Owner role
The Technical Dimension
The toolbox: retrieval, integration, and value extraction.
A hands-on tour through the technical backbone. Covers batch vs. streaming ingestion, the Medallion architecture, and the many ways Data Products surface value to the business.
- Batch vs. streaming data retrieval
- Medallion architecture: Bronze, Silver, Gold
- Data Contracts & testing with Great Expectations
- Value extraction: BI, APIs, AI, MCP, Data Spaces
The Toolkit
Three frameworks designed to be instantly memorable and immediately applicable. No 47-step maturity models. No abstract diagrams that dissolve on contact with reality.
The GAP Triad
The three dimensions embedded into every step of building a Data Product. Not as separate workstreams, but as a unified force. Miss any one, and the whole thing falls apart.
The DRIVE Framework
Retrieval
Extraction
The end-to-end lifecycle of every Data Product, from raw data to measurable business impact. Think of it as the assembly line for data: predictable, repeatable, and scalable.
CIA
Data Products are living entities. They must evolve with the business, the data, and the technology landscape. CIA is the operating model that keeps them relevant and valuable.
Not Another Hype Cycle Book
There are a hundred books about data. There is one that bridges the gap between business strategy and technical architecture. This is it.
Stories from the Trenches
Every chapter opens with a real story. Interrupted honeymoons, CEOs learning data science over wine, CFOs with personal grudges, 5 a.m. Hadoop deployments. This isn't theory. It's lived experience.
Two Dimensions, Not One
Most data books are either "all business" or "all tech." This one refuses to choose. Business value and technical design are treated as equal partners. That's how Data Products actually succeed.
Frameworks You'll Actually Remember
GAP. DRIVE. CIA. Practical tools designed to be easy to recall and immediately applicable. No complex diagrams that don't survive contact with reality.
Vendor-Neutral & Pragmatic
No sales pitch for a specific platform. The concepts work on AWS, Azure, GCP, or open-source stacks. Focus is on principles, not products. Your knowledge won't expire when a vendor pivots.
The Missing Bridge to Data Mesh
Data Products aren't just a Data Mesh concept. This book positions them as standalone entities combining the best of traditional data management with modern distributed thinking. No full org revolution required.
Two Decades of Real Experience
Written by a practitioner who has led 60+ person data teams, run large-scale transformation programs, and been on the other end of those 5 a.m. emergency calls.
What Readers Are Saying
Verified purchases on Amazon.
"Designing Data Products explains one of the most discussed topics in modern data management in a practical and accessible way. The presented frameworks, such as GAP and DRIVE, are particularly valuable as they turn theoretical concepts into repeatable practice. A highly recommended book for anyone who wants to understand how data products bridge the gap between technology, organization, and value creation."
"The book is easy to read and makes clear what the aspects of Data Products are and what they are not. I could read the entire book over the weekend and it was never overwhelming. Especially interesting was the business section which covers a third of the book. I am a techie but this helped me understand the business side of data."
"Each chapter begins with a personal anecdote from practice. From GAP to the DRIVE Framework to measuring financial success via ROI, the range of best practices is impressive. The author manages the balancing act between a comprehensive high-level overview and useful practical concepts. I warmly recommend this well-crafted and entertaining book."
"The book explains the topic of data products in an understandable and practical way without getting lost in technical details. Each chapter starts with personal experiences from practice and complex concepts are always explained using the 'car' example. It gives a good overview of how companies can meaningfully use data and create real value — including in the context of AI."
"This book offers an insightful exploration of data products, engaging storytelling with practical takeaways."
"The only thing not clear to me after reading the book was why the car is a Skoda. Great read :)"
Stop Treating Data as a Second-Class Citizen
Data Products aren't a buzzword. They're a pragmatic way to finally do things right. Get the foundation you need to build Data Products that deliver real value. For your business, your teams, and your career.
Published by Technics Publications · Edited by Steve Hoberman · Available in eBook and print
Building Data Products.
From Theory to Practice
with Open-Source Tools.
Volume I answered why Data Products matter and what they are. Volume II answers how to actually build them.
This book goes deep into Data Retrieval. That's the critical first phase of the DRIVE framework. It applies the GAP triad to every decision along the way and is built around a running automotive quality intelligence scenario: a European car manufacturer connecting supplier quality data, dealer service records, and manufacturing telemetry to detect quality patterns before they escalate into recalls. No Databricks. No Snowflake. Just clean, open-source infrastructure you own and understand.
By Mario Meir-Huber · Published by Technics Publications
What Readers Can Expect
Everything Volume I set up, now implemented end to end in working code.
A Technical Definition of Data Products
Where a Data Product actually lives across Git repos, catalogs, orchestration tools, and API gateways. The Open Data Product Standard (ODPS) ties it all together as a machine-readable source of truth.
Real Processes, Not Just Concepts
Three concrete workflows: creating a Data Product from scratch, updating it through the catalog, and deploying changes through pipelines. Including the author's hard-won lesson about wallet sizing and service portals.
Governance That Actually Works
Source system profiling before you connect, data classification at ingestion, quality gates that accept/flag/reject, automated metadata capture, lineage from source to landing zone, and retention and audit logging.
Architecture You Can Build Yourself
Pipeline anatomy from connectors to landing zone, orchestration design, storage architecture, schema validation, and monitoring. Grounded in an open-source stack: PostgreSQL, MinIO, Apache Airflow, and DataHub.
The People Dimension Nobody Else Covers
Working with source system teams without turning profiling into an audit, RACI matrices, training source system users (the cheapest quality investment you'll ever make), and a clear escalation and incident response framework.
A Hands-On Capstone Project
Everything comes together in one final chapter. Readers build a working Data Product infrastructure they can run on their own machine, using the same open-source stack described throughout the book.
The Open-Source Stack
No vendor lock-in. No license fees. Tools that real teams actually use.
Can't Wait? Start with Volume I.
Get the complete foundation now. Volume II picks up exactly where Volume I leaves off.
Scaling Data Products.
Integration, Value Extraction
& Data Contracts.
The final volume in the definitive Data Products series. Details to be announced.
Volume III completes the DRIVE framework. It goes deep into Integration, Value Extraction, and the Open Data Contract Standards (ODCS). The full curriculum from concept to enterprise-scale platform, now complete. More details coming soon.
By Mario Meir-Huber · Published by Technics Publications
Details to Be Announced
Volume III will complete the DRIVE framework, covering Integration, Value Extraction, and Open Data Contract Standards (ODCS). Stay tuned as the series progresses.
Start the Journey with Volume I
The series builds on itself. Volume I is where it all begins. Available right now.
Learn these frameworks in a live workshop
The GAP Triad, DRIVE Framework, and CIA model are taught hands-on in Mario's practitioner-led workshops. Small cohorts, real enterprise cases, immediate application.
Browse Data Products workshops →