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.

Flag immediately if:

• Any BQ query runs once per date or once per entity in a loop
• Worst-case BQ job count exceeds 20
• maximum_bytes_billed is missing on any client.query call

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B) DRY RUN AND EXECUTION MODES

Verify a --mode flag exists with at least dry_run and execute options.

• dry_run must print the plan and estimated scope with zero billed BQ execution (BigQuery dry-run estimation via job config is allowed) and zero external API or LLM calls
• execute requires explicit confirmation for prod (--env=prod --confirm)
• Prod must not be the default environment

If missing, propose a minimal argparse patch with safe defaults.

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C) BACKFILL AND LOOP DESIGN

Hard fail if: the script runs one BQ query per date or per entity in a loop.

Check that date-range backfills use one of:

1. A single set-based query with GENERATE_DATE_ARRAY
2. A staging table loaded with all dates then one join query
3. Explicit chunks with a hard MAX_CHUNKS cap

Also check:

• Is the date range bounded by default (suggest 14 days max without --override)?
• If the script crashes mid-run, is it safe to re-run without double-writing?
• For backdated simulations, verify data is read from time-consistent snapshots (FOR SYSTEM_TIME AS OF, partitioned as-of tables, or dated snapshot tables). Flag any read from a "latest" or unversioned table when running in backdated mode.

Suggest a concrete rewrite if the current approach is row-by-row.

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D) QUERY SAFETY AND SCAN SIZE

For each query, check:

• Partition filter is on the raw column, not DATE(ts), CAST(...), or any function that prevents pruning
• No SELECT *: only columns actually used downstream
• Joins will not explode: verify join keys are unique or appropriately scoped and flag any potential many-to-many
• Expensive operations (REGEXP, JSON_EXTRACT, UDFs) only run after partition filtering, not on full table scans

Provide a specific SQL fix for any query that fails these checks.

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