An independent resource for the open lakehouse
Reference material on Apache Iceberg, lakehouse catalogs, the agentic lakehouse, and modern data architecture. It covers what table formats are, how to deploy Apache Polaris, and how to connect query engines to Iceberg tables. Written by a practitioner, free to read.
Not an Apache project. This is a personal site by Alex Merced. It is not affiliated with, endorsed by, or sponsored by the Apache Software Foundation or the Apache Iceberg project, whose official site is iceberg.apache.org.
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Nine long-form pillar guides that cover the lakehouse stack from the file format up to the agents querying it.
- Pillar guide
Apache Iceberg
Covers the metadata tree, snapshots, hidden partitioning, and the catalog API from end to end.
Read the guide - Deep dive
Iceberg Architecture
How manifest lists, manifest files, and data files fit together at query time.
Read the guide - Catalogs
The REST Catalog
What the Iceberg REST Catalog spec standardizes, and how engines authenticate against it.
Read the guide - Deep dive
Snapshots & Time Travel
Atomic commits, snapshot expiration, rollback, and querying a table as of any point in time.
Read the guide - Deep dive
Schema Evolution
Add, drop, rename, and reorder columns safely, and why Iceberg's field IDs make it work.
Read the guide - Comparison
Iceberg vs Delta Lake vs Hudi
A neutral comparison of the three open table formats across design, features, and ecosystem.
Read the guide - Pillar guide
The Data Lakehouse
What a lakehouse actually is, the layers it is built from, and how it differs from a warehouse.
Read the guide - Agentic AI
The Agentic Lakehouse
Semantic layers, MCP, and the architecture AI agents need to query your data reliably.
Read the guide - Foundations
Open Table Formats
Why table formats exist at all, and the problems they solved for data lakes.
Read the guide
Recent Posts
- 31 MIN READ•Aug 19, 2026
Mastering Apache Iceberg v3 Deletion Vectors for High-Throughput Streaming Ingest
Apache Iceberg v3 deletion vectors for high-throughput streaming ingest: how bitmaps and Puffin files fix CDC write amplification and read decay.
Apache Icebergv3deletion vectors - 31 MIN READ•Aug 19, 2026
The Decoupled Data Lakehouse: Multi-Engine Freedom with Open REST Catalogs
The decoupled data lakehouse: multi-engine freedom with open REST catalogs, credential vending, and an estate that outlives its tools.
REST catalogdecoupled lakehousemulti-engine - 31 MIN READ•Aug 19, 2026
The Five Layers of an Agentic Lakehouse
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
agentic lakehousearchitectureMCP - 30 MIN READ•Aug 19, 2026
Goal-Directed Data Quality Agents: Anomaly Quarantine on Apache Iceberg
Goal-directed data quality agents that watch Apache Iceberg tables, detect anomalies, and quarantine suspect data safely with snapshot isolation and branches.
Apache Icebergdata qualityAI agents - 31 MIN READ•Aug 19, 2026
Managing the TCO of Agentic Analytics: Token Budgets, Query Throttles, and the Economics of Autonomy
Managing the total cost of agentic analytics: token budgets, query throttles, unit economics, and the FinOps discipline that keeps AI spend under control.
TCOFinOpstoken budgets - 31 MIN READ•Aug 19, 2026
Metric Contracts in 2026: Standardizing Business Logic Across Multi-Agent Frameworks
Metric contracts in 2026: versioned, testable definitions of business logic that let multi-agent frameworks compute revenue identically, with OSI interchange.
metric contractssemantic layerAI agents
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Must reads on Iceberg, agentic AI, and the lakehouse
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The Definitive Guide to the Semantic Layer
Understand what a semantic layer is, why it matters for modern data architectures, and how it creates a consistent, governed layer between raw data and business consumers.
Read article -
Apache Polaris: The Catalog Standard for Lakehouses and AI
A deep dive into Apache Polaris, the open-source catalog that is emerging as the standard for managing Iceberg tables across multi-engine Lakehouses and AI workloads.
Read article -
What Are Table Formats and Why Were They Needed?
Explore the history and motivations behind open table formats like Apache Iceberg, Delta Lake, and Apache Hudi, and why they solved critical problems in big data engineering.
Read article -
What is Dremio?
An overview of Dremio's Lakehouse platform: how it unifies data access, accelerates queries, and powers self-service analytics across cloud and on-premise sources.
Read article -
What Apache Iceberg Native Actually Means
Not all Iceberg integrations are equal. This article breaks down what it truly means for a platform to be 'Apache Iceberg native' and why the distinction matters for your architecture.
Read article -
Open Source and the Data Lakehouse
A survey of the open source ecosystem powering modern Data Lakehouses, from Apache Iceberg and Nessie to Apache Arrow and Spark, and how they work together.
Read article -
What is Agentic Analytics?
How AI agents are changing analytics pipelines by querying data, generating insights, and taking actions on their own, and what that means for the Lakehouse.
Read article
This site is an independent publication by Alex Merced. It is not affiliated with, endorsed by, or sponsored by the Apache Software Foundation or the Apache Iceberg project, whose official home is iceberg.apache.org.