Bigquery Pipeline Audit

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.

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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).

For each, answer:

• Is this inside a loop, retry block, or async gather?
• What is the realistic worst-case call count?
• For each client.query, is QueryJobConfig.maximum_bytes_billed set? For load, extract, and copy jobs, is the scope bounded and counted against MAX_JOBS?
• Is the same SQL and params being executed more than once in a single run? Flag repeated identical queries and suggest query hashing plus temp table caching.