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Alex Merced

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Alex Merced edited this page May 21, 2026 · 2 revisions

Alex Merced

Alex Merced is a prominent technologist, developer advocate, educator, and author in the data and AI ecosystem. Currently serving as the Head of Developer Relations at Dremio, he is a leading voice in modern data architectures, specializing in the Data Lakehouse, Apache Iceberg, Nessie, and Apache Polaris. He is also the recipient of Dremio's CEO Award for his outstanding contributions to developer education and advocacy.

Career & Background

Alex's professional journey spans tech, education, finance, and economics.

  • Developer Advocacy & Relations: As Head of DevRel at Dremio, Alex focuses on fostering communities around open-source data technologies. He has designed and published extensive educational resources on Dremio University and authored numerous deep-dive technical articles.
  • Developer Education: Prior to his advocacy work at Dremio, Alex served as a Software Engineering Instructor (such as at General Assembly), helping thousands of developers enter the software industry.
  • NYC Finance & Economics: For over a decade, Alex taught economics and finance in New York City, which heavily informs his writings on the economic impact of technological shifts.
  • Public Speaking: He is a frequent speaker at major tech conferences worldwide, including Data Council, Confluent Current, and DataEngBytes.

Technical Focus Areas

  • Data Lakehouse Architecture: Promoting open, decoupled analytical platforms that separate compute from storage, combining the reliability of a Data Warehouse with the scale of a Data Lake.
  • Open-Source Table Formats & Catalogs: Advocating for the adoption of Apache Iceberg and open-source Data Catalog solutions like Apache Polaris and Nessie to eliminate vendor lock-in.
  • Agentic AI & AI Engineering: Researching and designing patterns for building AI-ready data architectures, Retrieval-Augmented Generation (RAG), and agentic workflows built on lakehouse foundations.

Publications Catalog

Alex Merced is a prolific author who has written over 30 books covering data engineering, artificial intelligence, economics, and fiction.

Data Engineering & Lakehouses

  • Apache Iceberg: The Definitive Guide (O'Reilly, co-authored with Jason Hughes): The definitive guide to learning, architecting, and implementing Apache Iceberg.
  • Architecting an Apache Iceberg Lakehouse (Manning): Focused on architectural patterns and production strategies for Iceberg.
  • Apache Polaris: The Definitive Guide (Packt/O'Reilly): Comprehensive guide to using the Polaris open-source catalog.
  • The Book on Using Apache Iceberg with Python (Self-published): Practical guide to interacting with Iceberg tables using Python libraries like PyIceberg, PySpark, and PyFlink.
  • The Open Source Lakehouse: Architecting the Decoupled Analytical Foundation (Self-published): Architectural principles of building an open, decoupled analytics stack.

AI & Agentic Analytics

  • AI-Ready Data: Designing Data Platforms for LLMs, Agents, and RAG (Self-published): A handbook on preparing enterprise data pipelines for generative AI workloads.
  • The Book on Agentic Analytics (Self-published): Outlining the shift from descriptive analytics to agent-driven autonomous analytics.
  • Enabling Agentic Analytics with Apache Iceberg and Dremio (Self-published): Architectural blueprints for deploying AI agents on open data lakehouses.
  • Using AI Agents for Data Engineering and Data Analysis (Self-published): Hands-on workflows using AI agents to automate data ingestion, cleaning, and reporting.
  • The AI Engineering Handbook (Self-published): Standard reference guide for building production-grade AI applications.
  • The 2026 Guide to AI-Assisted Development (Self-published): Guidelines on prompt engineering, IDE assistants, and developer agents.
  • Apache Iceberg for Agentic AI: Connecting Structured Enterprise (Self-published): Bridging structured tabular data with semantic understanding for LLM agents.
  • The Agentic Enterprise: Deploying AI Agents Across the Modern Organization (Self-published): Strategy and implementation of enterprise-wide AI agents.
  • Constructing Context and Semantics for AI Agents (Self-published): Designing vector embeddings, contextual retrieval systems, and semantic layers for AI.
  • Evaluating AI Systems: Testing LLMs, RAG, and Agents (Self-published): Best practices for performance testing and accuracy evaluation in generative AI systems.
  • Governing AI Systems (Self-published): Frameworks for compliance, security, and ethics in deploying AI.
  • Shipping AI: From Prototype to Production Systems (Self-published): Production engineering, containerization, and scaling for AI microservices.
  • Building Knowledge Systems for AI: Graphs, RAG, Memory, and Context (Self-published): Integrating graph structures, vector stores, and persistent memory for LLM reasoning.
  • The AI Lakehouse: Architecting Data Platforms for AI (Self-published): Reimagining the lakehouse storage layer specifically to serve high-scale machine learning and AI inference.
  • AI Application Architecture: Patterns for Building Intelligent Systems (Self-published): Software engineering design patterns for AI-native applications.
  • The Economics of AI: Cost, Latency, and Infrastructure Tradeoffs (Self-published): Optimizing AI compute costs and latency across different deployment models.
  • The Economics of Labor in the AI Era (Self-published): Assessing the socio-economic impacts of automated workflows on the workforce.

Economics

  • Economic Ideas: From Beginning to Early 2026 (Self-published): A comprehensive survey of economic history leading into the modern AI-driven age.

Fiction Novels

  • The Semantic Rebellion (Self-published): A science-fiction novel following a data engineer navigating a world ruled by centralized algorithmic control.
  • The Emperors of A.I. Valley (Self-published): High-stakes power struggles and corporate intrigue inside the race for Artificial General Intelligence (AGI).
  • Embers of Claim (Self-published): The opening book of a fantasy trilogy exploring world-building, power dynamics, and ancient legacies.

Media & Community Outlets

  • 🎙️ Spotify Data Engineering Podcast: Host of deep-dive conversations with leading architects, engineers, and creators in the data space, focused on open-source platforms and lakehouse patterns.
  • 📧 AM Data Lakehouse Substack: A weekly newsletter sharing technical guides, architecture design patterns, and analysis of data industry trends.
  • Advocacy Hubs:

External Resources

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