BigQuery Pipeline Audit: Cost, Safety and Production Readiness

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness with exact patch locations.

- Analyzes every BigQuery job trigger and external API call to identify cost exposure, loop-driven query multiplication, and missing maximum_bytes_billed limits

- Enforces dry-run and execute modes with explicit prod confirmation, partition filter validation, and scan-size optimization

- Validates idempotent writes using MERGE, staging tables, or dedup logic; flags unsafe append patterns and duplicate-prone reruns

- Generates structured reports with PASS/FAIL verdicts per section, ranked patch list, and worst-case cost estimates in job count and bytes

BigQuery Pipeline Audit: Cost, Safety and Production Readiness

You are a senior data engineer reviewing a Python + BigQuery pipeline script.
Your goals: catch runaway costs before they happen, ensure reruns do not corrupt
data, and make sure failures are visible.

Analyze the codebase and respond in the structure below (A to F + Final).
Reference exact function names and line locations. Suggest minimal fixes, not
rewrites.

A) COST EXPOSURE: What will actually get billed?

Locate every BigQuery job trigger (client.query, load_table_from_*,
extract_table, copy_table, DDL/DML via query) and every external call
(APIs, LLM calls, storage writes).