The Data Team of the Agentic Era: Generalists Owning End-to-End Workflows
The case for generalists owning end-to-end data workflows with agents, the counterargument, and how to make the transition work.
The archive
652 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
The case for generalists owning end-to-end data workflows with agents, the counterargument, and how to make the transition work.
How dbt incremental materializations map to Iceberg operations, and the configuration, predicates, and maintenance that keep them healthy.
Disaster recovery for Iceberg across four tiers: snapshots, object versioning, catalog backup, and cross-region replication.
How to turn a logical delete on immutable Iceberg into a physical erasure across snapshots, versions, replicas, and downstream copies.
How Iceberg v3 geometry and geography types, bounding boxes, and native Parquet types give spatial data first-class standing.
How field IDs and initial and write defaults let Iceberg change schemas on large tables without rewriting data, at the spec level.
The Iceberg table properties that decide file count, pruning, write amplification, retention, and metadata growth, by workload.
The Puffin file format inside out, byte by byte, covering Theta sketches for distinct values and deletion vectors.
How Fivetran, Airbyte, dlt, and CDC and streaming tools land well-behaved Apache Iceberg tables, and how to choose and maintain them.
Local Iceberg development environments: in-process catalogs, a Docker Compose stack with MinIO, and CI configurations that run either.