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Where Exactly Data Becomes Product: Illustrated Guide to Data Products in Action
 • Animesh Kumar and Travis Thompson
How the Ontology Pipeline Powers Semantic Knowledge Systems
 • Jessica Talisman
Data Products: A Case Against Medallion Architecture
 • Animesh Kumar, Shubhanshu Jain, and Samadrita Ghosh
Introducing Lakehouse 2.0: What Changes?
 • Animesh Kumar and Travis Thompson
AI-Ready Data: A Technical Assessment. The Fuel and the Friction.
 • Sagar Paul
So I Have A Data Product... Now What?
 • Ryan Duffy
Rethinking Data Movement: A First Principles Approach
Why batch-heavy ingestion breaks under modern scale, and what incremental-first, observable pipelines replace it with
 • Animesh Kumar and Darpan Vyas
Building Robust Data Products: 5 Pillars Every Data Engineer Should Apply
The Onion Model, Coherent Approach to Data Management, and Balancing Different Evolutionary Timelines
 • Najate BOUAD
AI and Data are Business Strategy Experiments Now. How Far Are You Willing to Push the Curve?
Why Small Experiments Bend the Curve and Why Most Organizations Never Reach Zone C
 • Travis Thompson
Reflecting the Language Instinct in Machines
Feasibility, Atomic structures of language, Template-matching algorithms, and Designing precision of responses for business deployments
 • Animesh Kumar
The 20-Year Failure: How AI Closes the Gap between Data Strategy and Business Strategy
AI as the declarative bridge that strives to solve for the ONE common language
 • Markus Schmidberger
Predicting the Map of Requirements for Long-Term Data Platform Relevance
Rubric for Architectural Choices Driven by Consumption Patterns, Charting Map of Future Data Needs, and Enabling Polymorphic Data Platforms for…
 • Sagar Paul
Closing the Architecture Gap Between FAANG and Enterprises | Case: Meta | Part 2.2
Not how Meta built it, but how Meta thinks: the missing link in enterprise data architecture, deploying data developer platforms, and mapping outcomes.
 • Travis Thompson and Animesh Kumar
Modeling Semantics: How Data Models and Ontologies Connect to Build Your Semantic Foundations
Ontology vs. Conceptual Data Modeling, Implementation of Semantic Foundations, and Using Agents in the Right Places for Enrichment
 • Juha Korpela
The Network is the Product: Data Network Flywheel, Compound Through Connection
A First-Principles Strategy for Compounding Value Across Data Products with Well-Designed Loops, Self-Service Data Infrastructures, and Network-Level…
 • Animesh Kumar
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