Alex Merced Tutorialshandbook

749 tutorials · free to read

A working handbook for data and software engineering.

Hands-on tutorials by Alex Merced on Apache Iceberg, the data lakehouse, pipelines, agentic AI, and the languages and tools that hold it all together.

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  1. 01Why AI Agents Fail on Raw Data, and What to Give Them InsteadAgents fail on raw lake data because business rules live in people's heads. Data products with semantic contracts fix this at the source.Data EngineeringJul 28, 2026
  2. 02Why Iceberg V4 Wants to Retire Equality Deletes, and What Streaming Teams Should Do About ItEquality 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 IcebergJul 28, 2026
  3. 03The Five Layers Between Your Lakehouse and a Trustworthy AgentAgent 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 & AgentsJul 28, 2026
  4. 04Apache Fluss and Kafka Solve Different Problems in an Iceberg PipelineFluss 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 IcebergJul 28, 2026
  5. 05Serving Sub-Second Queries Over an Iceberg Lakehouse With a Hot TierA 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 IcebergJul 28, 2026
  6. 06Surviving Commit Conflicts When Dozens of Writers Hit the Same Iceberg TableCommit 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 IcebergJul 28, 2026