Posts
All the articles I've posted.
- 31 MIN READ•Jul 28, 2026
Building Agent Telemetry Tables in Iceberg That Survive an Audit
A practical guide to building agent decision traces in Apache Iceberg that support audit reconstruction, governance review, and cost attribution across sessions.
Apache IcebergAI AgentsData Governance - 31 MIN READ•Jul 28, 2026
What Agentic Analytics Actually Costs, and How to Keep It Bounded
Agent analytics generates two cost streams that scale on different variables. Here's the arithmetic, the levers that actually move the number, and how to build attribution before you need it.
AI AgentsAnalyticsCost Optimization - 31 MIN READ•Jul 28, 2026
Running an Apache Iceberg Lakehouse With No Internet Connection
A practical guide to deploying an Iceberg lakehouse in air-gapped environments: component choices, artifact pipelines, identity without a cloud, and the operational realities that surprise teams.
Apache IcebergAir-GappedData Engineering - 31 MIN READ•Jul 28, 2026
When the Query Optimizer Starts Managing Its Own Materializations
Autonomous materialized view management replaces quarterly review meetings with workload-driven scoring, and it's essential when AI agents generate unpredictable query patterns.
Apache IcebergQuery OptimizationAI Agents - 31 MIN READ•Jul 28, 2026
Why AI Agents Fail on Raw Data, and What to Give Them Instead
Agents fail on raw lake data because business rules live in people's heads. Data products with semantic contracts fix this at the source.
AI AgentsApache IcebergSemantic Layer - 31 MIN READ•Jul 28, 2026
Why Iceberg V4 Wants to Retire Equality Deletes, and What Streaming Teams Should Do About It
Equality deletes made streaming upserts into Iceberg practical at the cost of read performance. V4 proposes retiring them in favor of deletion vectors with an async conversion path.
Apache IcebergStreamingData Engineering - 31 MIN READ•Jul 28, 2026
The Five Layers Between Your Lakehouse and a Trustworthy Agent
Agent reliability is a property of the stack the model sits on. Five layers with distinct owners and failure modes turn the agent is unreliable into a specific diagnosis.
AI AgentsApache IcebergData Architecture - 31 MIN READ•Jul 28, 2026
Apache Fluss and Kafka Solve Different Problems in an Iceberg Pipeline
Fluss puts a columnar, indexed hot tier between Kafka and Iceberg. Here's what it changes structurally, what Kafka still does better, and how to benchmark the comparison yourself.
Apache IcebergApache FlussKafka - 31 MIN READ•Jul 28, 2026
Serving Sub-Second Queries Over an Iceberg Lakehouse With a Hot Tier
A lakehouse cannot serve sub-second queries over seconds-old data. A hot tier in front solves it, with consequences for consistency, governance, and operational surface.
Apache IcebergStreamingData Serving - 31 MIN READ•Jul 28, 2026
Surviving Commit Conflicts When Dozens of Writers Hit the Same Iceberg Table
Commit conflicts multiply with writer count, and AI agents introduce unpredictable write patterns. Here's how to diagnose, tune, and architect around Iceberg's optimistic concurrency.
Apache IcebergConcurrencyData Engineering - 31 MIN READ•Jul 28, 2026
The Jackson 3 Problem in Apache Iceberg, and What It Means for Your Code
Jackson 3 changes everything: package names, unchecked exceptions, flipped defaults. Here's what breaks, why the engines are fine and your service isn't, and how to migrate safely.
Apache IcebergJacksonJava - 31 MIN READ•Jul 28, 2026
Wiring an AI Agent to Apache Polaris with the Model Context Protocol
The catalog is the right attachment point for AI agents working against a lakehouse. Here's how to wire the official Polaris MCP Server and add the read path it deliberately leaves out.
Apache IcebergMCPAI Agents