Tobias Wrenfeld

Evaluate embedded integrations by their data isolation controls, not just connector breadth.

Schema enforcement catches the confidently wrong answers AI agents produce from malformed data.

Three idempotent patterns prevent silent data corruption during warehouse retries.

Designing deletion rules that work reliably across analytics destinations.

Incremental sync with watermarks moves only changed data, avoiding costly full reloads.

Denormalization depth depends on warehouse layer, query patterns.

Null values demand explicit contracts between systems or silent data corruption will follow.

Breaking schema changes to exported tables risk cascading failures across dependent systems.