Metric Contracts as the Interface AI Agents Actually Need
Metric contracts as the interface AI agents need: calculation, inclusion rules, grain, temporal semantics, ownership, semantic versioning, and testing.
The archive
652 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
Metric contracts as the interface AI agents need: calculation, inclusion rules, grain, temporal semantics, ownership, semantic versioning, and testing.
How Apache Polaris vends short-lived, prefix-scoped storage credentials across AWS, Azure, and GCP, and how to retire permanent storage keys for good.
Designing policy-aware AI telemetry tables in Apache Iceberg: what to log, tamper evidence, retention against conflicting deletion requirements.
Defending the lakehouse gateway against prompt injection and data exfiltration: per-user identity, no-SQL tool surfaces, volume bounds, and detection.
How the Iceberg REST catalog became the lakehouse control plane: multi-table atomic commits, credential vending, capability negotiation, and what still breaks.
A dependency-first playbook for migrating legacy warehouses onto Apache Iceberg: snapshot vs migrate vs add_files, four-level parity validation.
What zero-copy data sharing actually does across Salesforce, Snowflake, and Databricks: query federation, file federation, catalog federation, and when.
Apache Polaris 1.7.0 deep dive: idempotent writes, semantic models, stricter credential vending, orphan cleanup, and what the upgrade asks of you.
How to design batch pipelines that write well into Apache Iceberg: commit strategy, partitioning, sort order, write-audit-publish, and maintenance done right.
How AWS, Google Cloud, and Microsoft Azure actually support Apache Iceberg: storage, catalogs, maintenance, governance, and interoperability, layer by layer.