Posts - Page 22
All the articles I've posted, page 22.
- 16 MIN READ•Jul 6, 2026
Enforcing Fine-Grained Security at Machine Speed: Dynamic Access Control for High-Frequency AI Agents
How to secure high-frequency AI agents with dynamic access control: row, column, and tool policies, short-lived credentials, token exchange, and audit trails.
fine-grained securityai agentsaccess control - 16 MIN READ•Jul 6, 2026
Implementing Positional Deletes in Iceberg v3: Streamlining Merge-on-Read for Fast-Inbound Event Lakes
How positional deletes and deletion vectors support merge-on-read in Iceberg for late, duplicate, and corrected event data, plus compaction and engine checks.
iceberg v3positional deletesevent lakes - 16 MIN READ•Jul 6, 2026
Mapping the Variant Type in Iceberg v3: Standardizing Semi-Structured AI JSON Payloads
How Iceberg v3 variant-style typing stores evolving AI payloads like prompts and tool calls, and when to promote JSON fields into governed, typed columns.
iceberg v3variant typejson - 15 MIN READ•Jul 6, 2026
Designing Idempotent Pipelines in the Agentic Lakehouse: Eliminating Double-Write Anomalies
How to stop agent retries and reruns from writing duplicates in an Iceberg lakehouse, using operation IDs, intent logs, deterministic writes, and validation.
idempotent pipelinesagentic lakehousedouble-write - 27 MIN READ•Jul 6, 2026
File Encryption for the Lakehouse: The Terminology, the Machinery, and the Hard Problem of Interoperable Encrypted Tables
Encryption terms, Parquet modular encryption, and Iceberg 1.11 table encryption explained, plus the problems of sharing encrypted tables across engines.
encryptiondata lakehousesecurity - 15 MIN READ•Jul 6, 2026
The Metric Contract Mandate: Standardizing Semantic Layers Before AI Agent Access
What a metric contract contains (definition, grain, owner, freshness, tests, access) and why a semantic layer needs one before AI agents are allowed to query.
semantic layersmetric contractsai agents - 15 MIN READ•Jul 6, 2026
Multi-Engine Catalog Federation with Apache Polaris: Syncing Google Cloud, AWS, and Azure Metadata
How Apache Polaris federates Iceberg catalogs across AWS, Azure, and Google Cloud, covering identity mapping, scoped credentials, namespaces, and pitfalls.
apache polariscatalog federationmulticloud - 26 MIN READ•Jul 6, 2026
Operational vs. Analytical Systems: Why the Oldest Divide in Data Exists, What Physics Enforces It, and the Honest Truth About Hybrid Systems
Why OLTP and OLAP systems split, the storage physics that keep them apart, how HTAP has fared, and a 2026 framework for composing both on an open substrate.
oltpolaphtap - 30 MIN READ•Jul 6, 2026
Personal Context vs. Shared Context: A Deep Dive Into How Humans and Organizations Should Feed Their AI Agents
How personal context for one user and governed shared context for an organization feed AI agents, with optimization techniques, failure cases, and frameworks.
personal contextshared contextai agents - 30 MIN READ•Jul 6, 2026
Designing Private, Air-Gapped Data Lakehouses: Scaling Iceberg in Highly Secure, On-Premises Clouds
How to run Iceberg lakehouses in air-gapped and on-prem clouds: storage, catalogs, engine placement, agent access, supply chain upgrades, audit, and recovery.
air-gappeddata lakehouseiceberg - 15 MIN READ•Jul 6, 2026
Migrating Proprietary Warehouses to Open Lakehouses: The 2026 Playbook for Zero-Copy Metadata Translation
A staged playbook for moving proprietary warehouses to Iceberg: what zero-copy really means, federation during transition, validation, and a 90-day runbook.
migrationlakehousemetadata translation - 28 MIN READ•Jul 6, 2026
Lakehouse Table Formats in 2026: Iceberg, Delta Lake, Hudi, Paimon, and DuckLake, How They Work, Where They Stand, and Where They're Going
How Iceberg, Delta Lake, Hudi, Paimon, and DuckLake work, how each one handles row-level change, and a practical framework for choosing a table format in 2026.
table formatsicebergdelta lake