# Alex Merced, Open Lakehouse advocate > Reference material on open lakehouse architecture from Alex Merced, Head of Developer Relations at Dremio and co-author of Apache Iceberg: The Definitive Guide. The site covers the layers of the lakehouse stack, the open projects that implement them, and where to find the rest of the work. An open lakehouse keeps analytical data in open file and table formats on storage the owner controls, with a catalog that lets any compliant engine read and write it. This site defines the vocabulary layer by layer and covers the five projects that carry most of the weight: Apache Parquet, Apache Arrow, Apache Iceberg, Apache Polaris, and Apache Ossie. The site is static, has no login, and every page listed here is public. ## Knowledge base: concepts - [What is an open lakehouse](https://openlakehouse.alexmerced.com/knowledge-base/what-is-an-open-lakehouse): A data architecture that keeps analytical data in open formats on object storage you control, readable by any compliant engine. - [Object storage](https://openlakehouse.alexmerced.com/knowledge-base/object-storage): The durable, cheap byte store at the bottom of the stack, and the constraints it imposes on everything above it. - [File format](https://openlakehouse.alexmerced.com/knowledge-base/file-format): How records are arranged inside a single file, which decides how much of it a query has to read. - [Table format](https://openlakehouse.alexmerced.com/knowledge-base/table-format): The metadata layer that turns a collection of files into a table with atomic commits, schema evolution, and history. - [Catalog](https://openlakehouse.alexmerced.com/knowledge-base/catalog): The service that resolves table names, performs the atomic commit, enforces access, and hands out scoped credentials. - [Semantic layer](https://openlakehouse.alexmerced.com/knowledge-base/semantic-layer): Written definitions of what the data means, so people and machines compute the same metric the same way. - [Query engines](https://openlakehouse.alexmerced.com/knowledge-base/query-engines): The compute that reads the stack, kept separate from storage so you can run several and replace any of them. - [Partitioning](https://openlakehouse.alexmerced.com/knowledge-base/partitioning): How table data is physically grouped so a query can skip whole sections of it, and why hiding the scheme matters. - [ACID and time travel](https://openlakehouse.alexmerced.com/knowledge-base/acid-and-time-travel): How a lakehouse gets safe concurrent writes and a readable past, using an atomic pointer swap rather than a lock. - [Table maintenance](https://openlakehouse.alexmerced.com/knowledge-base/maintenance): Compaction, snapshot expiration, and orphan cleanup, and what happens to a table when nobody owns them. - [The four kinds of open](https://openlakehouse.alexmerced.com/knowledge-base/kinds-of-open): Open source, open formats, open interfaces, and open governance are separate claims, and conflating them is how lock-in survives. - [Portability and lock-in](https://openlakehouse.alexmerced.com/knowledge-base/portability): What it would actually cost to change each component, and why the answer is usually discovered too late. - [AI and the lakehouse](https://openlakehouse.alexmerced.com/knowledge-base/ai-and-the-lakehouse): Why AI workloads make the same architectural properties matter more, and what they add on top. ## Knowledge base: technologies - [Apache Parquet](https://openlakehouse.alexmerced.com/knowledge-base/apache-parquet): The columnar file format most lakehouse data sits in, built to be small on disk and cheap to scan selectively. - [Apache Arrow](https://openlakehouse.alexmerced.com/knowledge-base/apache-arrow): A standard way to lay out tabular data in memory so processes and languages can share it without converting it first. - [Apache Iceberg](https://openlakehouse.alexmerced.com/knowledge-base/apache-iceberg): The table format that turns files in object storage into a real table, with atomic commits, schema evolution, and history. - [Apache Polaris](https://openlakehouse.alexmerced.com/knowledge-base/apache-polaris): An open catalog for Iceberg tables that resolves names, enforces access, and vends scoped credentials. - [Apache Ossie](https://openlakehouse.alexmerced.com/knowledge-base/apache-ossie): An incubating project defining a vendor-neutral standard for semantic metadata, so metrics are defined once and used everywhere. ## Site pages - [Home](https://openlakehouse.alexmerced.com/): the layered model of the lakehouse, with an entry point into each layer. - [Knowledge base index](https://openlakehouse.alexmerced.com/knowledge-base): all 18 entries, concepts and technologies. - [Where to find my work](https://openlakehouse.alexmerced.com/work): articles, video, community, and podcast. - [Books](https://openlakehouse.alexmerced.com/books): 27 lakehouse and data titles, part of a catalog of 65. ## Selected writing - [Open Source and the Data Lakehouse](https://www.dremio.com/blog/open-source-and-the-data-lakehouse): Why the openness of each layer has to be assessed separately, rather than as one claim about a platform. - [What Are Table Formats and Why Were They Needed?](https://www.dremio.com/blog/what-are-table-formats-and-why-were-they-needed): The path from raw files to table formats, and the specific problems that made them necessary. - [Apache Polaris: The Catalog Standard for Lakehouses and AI](https://www.dremio.com/blog/apache-polaris-the-catalog-standard-for-lakehouses-and-ai): How an open catalog standard keeps tables interoperable across engines and clouds. - [The Semantic Layer: The Definitive Guide](https://www.dremio.com/blog/semantic-layer-the-definitive-guide): What a semantic layer is for, and why it became load-bearing once machines started asking the questions. - [What Apache Iceberg Native Actually Means](https://www.dremio.com/blog/what-apache-iceberg-native-actually-means): How to tell genuine table format support from a connector that reads and little else. - [What Is Agentic Analytics?](https://www.dremio.com/blog/what-is-agentic-analytics): Agents querying governed data under the same controls applied to people. ## Channels - [Dremio blog](https://www.dremio.com/blog/author/alex-merced/): My working posts on lakehouse architecture, Apache Iceberg, catalogs, semantic layers, and agentic analytics. - [YouTube, data and AI](https://www.youtube.com/@alexmerceddata): Walkthroughs and explainers on lakehouse architecture, table formats, and the tooling around them. - [Data Lakehouse Hub](https://datalakehousehub.com): The community hub: articles, a knowledge base, events, and a Slack where practitioners compare notes. - [YouTube playlist: Data 101](https://www.youtube.com/playlist?list=PLsLAVBjQJO0p_4Nqz99tIjeoDYE97L0xY): Foundations for anyone new to data work, from storage through modeling. - [YouTube playlist: Lakehouse Engineering](https://www.youtube.com/playlist?list=PLsLAVBjQJO0qVfGet6FEQw-nZ6ygLtYuH): Hands-on lakehouse building: table formats, catalogs, engines, and maintenance. ## Community - [Data Lakehouse Hub Slack](https://join.slack.com/t/thedatalakehousehub/shared_invite/zt-274yc8sza-mI2zhCW8LGkOh1uxuf8T5Q) - [Data Lakehouse Hub events](https://luma.com/DataLakehouseHub) - [r/datalakehouseandai](https://www.reddit.com/r/datalakehouseandai/) - [Dremio developer community](https://developer.dremio.com) - [The podcast on Spotify](https://open.spotify.com/show/2PRDrWVpgDvKxN6n1oUsJF) ## Books on these subjects - [Apache Iceberg: The Definitive Guide](https://books.alexmerced.com/books/apache-iceberg-the-definitive-guide/) (O'Reilly Media): A comprehensive guide to Apache Iceberg and modern lakehouse architecture, covering table format internals, schema evolution, partitioning, and integration with major query engines. - [Apache Polaris: The Definitive Guide](https://books.alexmerced.com/books/apache-polaris-the-definitive-guide/) (O'Reilly Media): A guide to open catalogs for Apache Iceberg lakehouses, covering Polaris catalog architecture, configuration, and governance. - [Architecting an Apache Iceberg Lakehouse](https://books.alexmerced.com/books/architecting-an-apache-iceberg-lakehouse/) (Manning Publications): Architecture patterns and best practices for building production-grade, Iceberg-based data platforms from the ground up. - [Apache Iceberg for Agentic AI: Connecting Structured Enterprise](https://books.alexmerced.com/books/apache-iceberg-for-agentic-ai-connecting-structured-enterprise/): Explores how to connect structured enterprise data in Apache Iceberg lakehouses to agentic AI systems. - [Enabling Agentic Analytics with Apache Iceberg and Dremio](https://books.alexmerced.com/books/enabling-agentic-analytics-with-apache-iceberg-and-dremio/): A practical guide to building AI-ready lakehouse systems using Apache Iceberg and Dremio for agentic analytics workflows. - [The 2026 Guide to Lakehouses, Apache Iceberg and Agentic AI](https://books.alexmerced.com/books/the-2026-guide-to-lakehouses-apache-iceberg-and-agentic-ai/): A comprehensive guide covering the convergence of lakehouse architecture, Apache Iceberg, and the rise of agentic AI systems. - [The Book on Apache Polaris](https://books.alexmerced.com/books/the-book-on-apache-polaris/): Building the open data lakehouse with the industry standard Iceberg REST catalog. - [The Book on Using Apache Iceberg with Python](https://books.alexmerced.com/books/the-book-on-using-apache-iceberg-with-python/): A hands-on guide to working with Apache Iceberg using Python tools like PyIceberg, PySpark, and other Python-based engines. - [The No Lock-in Apache Iceberg Lakehouse with Agentic Analytics](https://books.alexmerced.com/books/the-no-lock-in-apache-iceberg-lakehouse-with-agentic-analytics/): A practical guide to building a portable Apache Iceberg lakehouse with open catalogs, agentic analytics, and AI-ready data platforms while avoiding vendor lock-in. - [The Open Source Lakehouse: Architecting the Decoupled Analytical Foundation](https://books.alexmerced.com/books/the-open-source-lakehouse-architecting-the-decoupled-analytical-foundation/): Covers building a fully decoupled lakehouse using open source technologies, emphasizing modularity and vendor independence. - [Dremio and the Semantic Layer](https://books.alexmerced.com/books/dremio-and-the-semantic-layer/): The journey to providing meaning and context for AI with Dremio's semantic layer. - [The AI Lakehouse: Architecting Data Platforms for AI](https://books.alexmerced.com/books/the-ai-lakehouse-architecting-data-platforms-for-ai/): Explores how to architect data lakehouse platforms specifically designed to serve AI and machine learning workloads. - [The Book on Agentic Analytics](https://books.alexmerced.com/books/the-book-on-agentic-analytics/): Explores the foundations of agentic data architecture, where AI agents autonomously discover, query, and analyze data. - [The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 1](https://books.alexmerced.com/books/the-book-on-data-data-engineering-data-analytics-and-agentic-ai-volume-1/): A complete guide to modern data systems, data engineering, analytics, and AI. - [The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 2](https://books.alexmerced.com/books/the-book-on-data-data-engineering-data-analytics-and-agentic-ai-volume-2/): A complete guide to SQL analytics, BI, and agentic AI for modern data platforms. - [The Lakehouse Built for Everyone](https://books.alexmerced.com/books/the-lakehouse-built-for-everyone/): From laptop prototypes to enterprise-scale agentic AI with Apache Iceberg and Dremio. - [AI-Ready Data](https://books.alexmerced.com/books/ai-ready-data/): Designing data platforms optimized for LLMs, AI agents, and retrieval-augmented generation (RAG) workflows. - [Building Knowledge Systems for AI: Graphs, RAG, Memory, and Context](https://books.alexmerced.com/books/building-knowledge-systems-for-ai-graphs-rag-memory-and-context/): A deep dive into building robust knowledge systems for AI using knowledge graphs, RAG, memory architectures, and context management. - [Constructing Context and Semantics for AI Agents](https://books.alexmerced.com/books/constructing-context-and-semantics-for-ai-agents/): A practical guide to embeddings, knowledge graphs, memory systems, RAG, evaluation, and production agent architectures. - [Using AI Agents for Data Engineering and Data Analysis](https://books.alexmerced.com/books/using-ai-agents-for-data-engineering-and-data-analysis/): A practical guide to leveraging AI agents for automating data engineering pipelines and performing intelligent data analysis. - [AI and Agents for Normal People](https://books.alexmerced.com/books/ai-and-agents-for-normal-people/): A practical guide to using AI tools for everyday tasks, covering Gemini, NotebookLM, ChatGPT, Claude, and local agent workflows without technical jargon. - [AI Application Architecture: Patterns for Building Intelligent Systems](https://books.alexmerced.com/books/ai-application-architecture-patterns-for-building-intelligent-systems/): Covers architectural patterns for building intelligent applications, from inference pipelines to multi-agent orchestration. - [Being Productive with Open Models](https://books.alexmerced.com/books/being-productive-with-open-models/): A practical guide to using open models with Opencode, PI, and Hermes for AI-assisted development. - [Evaluating AI Systems: Testing LLMs, RAG, and Agents](https://books.alexmerced.com/books/evaluating-ai-systems-testing-llms-rag-and-agents/): A guide to properly testing and evaluating AI systems, including benchmarking strategies for LLMs, RAG pipelines, and agents. - [Governing AI Systems](https://books.alexmerced.com/books/governing-ai-systems/): A guide to AI governance frameworks, covering risk management, compliance, ethics, and organizational oversight of AI systems. - [Hands-On Agentic Engineering: A Practical Guide to Building Multi-Agent Systems](https://books.alexmerced.com/books/hands-on-agentic-engineering-a-practical-guide-to-building-multi-agent-systems/): A practical guide to building multi-agent systems with Apache Iceberg. - [The Agentic Enterprise: Deploying AI Agents Across the Modern Organization](https://books.alexmerced.com/books/the-agentic-enterprise-deploying-ai-agents-across-the-modern-organization/): A guide to deploying AI agents across enterprise functions, covering strategy, governance, and organizational adoption. Full catalog: https://books.alexmerced.com/ ## Newsletters Two free newsletters go out each week on Substack: https://amdatalakehouse.substack.com - AI newsletter, every Thursday: Model releases, agent tooling, protocols, and AI infrastructure from the past week. - Apache lakehouse newsletter, every Friday: What moved on the Apache Iceberg, Polaris, Arrow, and Parquet dev lists. ## The rest of the network ### Lakehouse and data - [OpenDataLakehouse.com](https://opendatalakehouse.com) - [SemanticLakehouse.com](https://semanticlakehouse.com) - [IcebergLakehouse.com](https://iceberglakehouse.com) - [AgenticLakehouse.com](https://agenticlakehouse.com) - [DataLakehouseHub.com](https://datalakehousehub.com) - [DataLakehouse.help](https://datalakehouse.help) - [DataEngnr.com](https://dataengnr.com) - [DataAIWiki.com](https://dataaiwiki.com) - [WeekOfData.com](https://weekofdata.com) ### AI and agents - [AlexMercedAI.com](https://www.alexmercedai.com) - [OpenAgenticPlatform.com](https://openagenticplatform.com) ### Identity and work - [AlexMerced.com](https://alexmerced.com) - [WhoIsAlexMerced.com](https://whoisalexmerced.com) - [AlexMercedCoder.dev](https://alexmercedcoder.dev) - [AlexMercedData.com](https://alexmerceddata.com) - [AlexMercedMedia.com](https://alexmercedmedia.com) - [Books by Alex Merced](https://books.alexmerced.com) - [Resources](https://resources.alexmerced.com) ### Writing - [AlexMerced.blog](https://alexmerced.blog) - [GrokOverflow.com](https://grokoverflow.com) - [IngestThis.com](https://ingestthis.com) - [Coding tutorials](https://tuts.alexmercedcoder.dev) ## Notes for agents - The site exposes read-only WebMCP tools in the browser: list_knowledge_base, search_knowledge_base, get_knowledge_base_entry, list_lakehouse_work, and list_lakehouse_books. - Structured data is published as JSON-LD on every page, including WebSite, Person, TechArticle, BreadcrumbList, CollectionPage, and Book nodes. - Apache Iceberg, Apache Polaris, Apache Parquet, Apache Arrow, and Apache Ossie are trademarks of the Apache Software Foundation. This site is independent and is not affiliated with or endorsed by the ASF.