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 25, 2026
Agent-Driven Storage Tiering for Apache Iceberg: Moving Cold Data Without Breaking Queries
A background agent can move cold Iceberg partitions to cheaper tiers without breaking live queries. Heatmaps, path-safe moves, and restore paths.
Apache Icebergstorage tieringcost optimization - 31 MIN READ•Aug 25, 2026
Securing the Agentic Lakehouse Gateway: Preventing Prompt Injection and Data Exfiltration
Agentic lakehouse gateways face prompt injection and exfiltration through query results. A threat model and defenses for the layer in front of data.
AI agentssecurityMCP - 31 MIN READ•Aug 25, 2026
Apache Ossie and Apache Polaris: Putting Semantic Models in the Open Catalog
Apache Ossie and Polaris put metric definitions in the open catalog. What the spec covers, what Polaris stores, and what is still unfinished.
Apache OssieApache Polarissemantic layer - 31 MIN READ•Aug 25, 2026
Arrow Flight SQL and ADBC: Why the Database Driver Is the Slowest Part of Your Query
JDBC and ODBC often dominate large-result time. Flight SQL and ADBC keep data columnar from server to client, with Python, Go, and Rust examples.
Apache ArrowFlight SQLADBC - 31 MIN READ•Aug 25, 2026
DataFusion Comet 1.0 and What Native Rust Scans Change for Spark on Iceberg
DataFusion Comet 1.0 replaces Spark Iceberg scans with native Rust. What speeds up, what still falls back to the JVM, and how to deploy it.
Apache IcebergApache SparkDataFusion Comet - 31 MIN READ•Aug 25, 2026
FSST and ALP: The Two Encodings Fixing Parquet's Weakest Compression Cases
ALP and FSST target Parquet's worst cases: floats and high-cardinality strings. How they work and what they change for Iceberg tables.
Apache ParquetencodingsALP
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A Slack workspace for lakehouse practitioners, plus a shared calendar of meetups, webinars, and Lakehouse Linkups.
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.