Data engineering · science · architecture

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Working notes for people who move data for a living. Pipelines, lakehouses, table formats, and the architecture underneath them.

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  1. 01Data Quality Tooling Compared: Great Expectations, Soda, dbt Tests, and Anomaly DetectionA comparison of Great Expectations, Soda, dbt tests, and anomaly detection, and a layered design that uses each where it fits.2026-09-02
  2. 02The Data Team of the Agentic Era: Generalists Owning End-to-End WorkflowsThe case for generalists owning end-to-end data workflows with agents, the counterargument, and how to make the transition work.2026-09-02
  3. 03dbt on Iceberg: Incremental Models on Open TablesHow dbt incremental materializations map to Iceberg operations, and the configuration, predicates, and maintenance that keep them healthy.2026-09-02
  4. 04Disaster Recovery for Iceberg Tables: Replication, Backup, and RestoreDisaster recovery for Iceberg across four tiers: snapshots, object versioning, catalog backup, and cross-region replication.2026-09-02
  5. 05Deleting User Data From an Immutable Lakehouse: GDPR Hard Deletes on IcebergHow to turn a logical delete on immutable Iceberg into a physical erasure across snapshots, versions, replicas, and downstream copies.2026-09-02
  6. 06Geospatial Data in Apache Iceberg: Geometry, Geography, and GeoParquetHow Iceberg v3 geometry and geography types, bounding boxes, and native Parquet types give spatial data first-class standing.2026-09-02
  7. 07Default Column Values and Field IDs: How Iceberg Schema Evolution Works at the Spec LevelHow field IDs and initial and write defaults let Iceberg change schemas on large tables without rewriting data, at the spec level.2026-09-02
  8. 08The Iceberg Table Properties That Actually MatterThe Iceberg table properties that decide file count, pruning, write amplification, retention, and metadata growth, by workload.2026-09-02

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