Posts - Page 10

All the articles I've posted, page 10.

• 31 MIN READ•Aug 2, 2026

  Designing Batch Pipelines That Write Well Into Apache Iceberg

  How to design batch pipelines that write well into Apache Iceberg: commit strategy, partitioning, sort order, write-audit-publish, and maintenance done right.

  Apache IcebergData EngineeringBatch Pipelines
• 31 MIN READ•Aug 2, 2026

  Apache Iceberg Support Across the Major Hyperscalers

  How AWS, Google Cloud, and Microsoft Azure actually support Apache Iceberg: storage, catalogs, maintenance, governance, and interoperability, layer by layer.

  Apache IcebergAWSGoogle Cloud
• 31 MIN READ•Jul 28, 2026

  Guardrails for Analytics Agents That Do More Than Answer Questions

  The risk isn't agents going rogue, it's agents acting correctly on bad input at machine speed.

  AI AgentsGuardrailsData Governance
• 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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  Apache IcebergStreamingData Serving