Variant Shredding Explained: How Iceberg Gets Columnar Performance From Messy JSON
Variant shredding turns messy JSON into Parquet columns with statistics. How the layout works, how readers reassemble values, and why some queries prune.
The archive
652 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
Variant shredding turns messy JSON into Parquet columns with statistics. How the layout works, how readers reassemble values, and why some queries prune.
Managed and external Iceberg tables mean different things on every platform. Five ownership dimensions and a translation method for vendor vocabulary.
Apache Iceberg v3 deletion vectors for high-throughput streaming ingest: how bitmaps and Puffin files fix CDC write amplification and read decay.
The decoupled data lakehouse: multi-engine freedom with open REST catalogs, credential vending, and an estate that outlives its tools.
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
Goal-directed data quality agents that watch Apache Iceberg tables, detect anomalies, and quarantine suspect data safely with snapshot isolation and branches.
Managing the total cost of agentic analytics: token budgets, query throttles, unit economics, and the FinOps discipline that keeps AI spend under control.
Metric contracts in 2026: versioned, testable definitions of business logic that let multi-agent frameworks compute revenue identically, with OSI interchange.
Policy-aware lakehouse telemetry for the EU AI Act era: build auditable, immutable, governed AI decision records on Apache Iceberg.
Query routing at machine scale for the agentic lakehouse: engine selection, acceleration substitution, admission control, and placement.