Data engineering · science · architecture

IngestThis

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

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  1. 01Agentic Data ArchitectureA six-layer reference architecture for agents on company data: planners, tool boundaries, identity, the semantic layer, and what breaks when a layer is missing.2026-09-10
  2. 02Running Apache Polaris in ProductionApache Polaris past the quickstart: persistence backends, realm bootstrap, replica token signing, upgrades, backups, and which failures take the lakehouse offline.2026-09-10
  3. 03What the 2026 Consolidation Means for Open FormatsTabular, Dremio, and a wave of data platform acquisitions: what changes for teams building on open formats and which guarantees survive a change of ownership.2026-09-10
  4. 04Context Engineering for Data AgentsWhy text-to-SQL accuracy collapses on enterprise schemas, the five kinds of context an agent needs, where each one hides, and how to make the semantics legible.2026-09-10
  5. 05Guardrails for AI on Company DataThe control surfaces that actually contain damage once an agent is fooled: identity, permissions, audit trails, and prompt injection at the query layer.2026-09-10
  6. 06Migrating Into Iceberg Without Moving Dataadd_files, snapshot, and migrate compared: the three in-place paths into Iceberg, the reconciliation each requires, the layout traps, and the rollback story.2026-09-10
  7. 07What Iceberg Table Maintenance Actually CostsA cost model for compaction, snapshot expiry, orphan cleanup, and manifest rewriting: what each operation spends, on which meter, and how to set a schedule.2026-09-10
  8. 08Running an Iceberg Lakehouse on KubernetesCatalog, maintenance, and compaction as Kubernetes workloads: scheduling classes, job structure, credential flow, and the failures that come from the interaction.2026-09-10

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