/bigquery-pipeline-audit

name: bigquery-pipeline-audit
description: 'Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.'

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).

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

---

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.

---

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.

---

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.

---

E) SAFE WRITES AND IDEMPOTENCY

Identify every write operation. Flag plain `INSERT`/append with no dedup logic.

Each write should use one of: 1. `MERGE` on a deterministic key (e.g., `entity_id + date + model_version`) 2. Write to a staging table scoped to the run, then swap or merge into final 3. Append-only with a dedupe view: `QUALIFY ROW_NUMBER() OVER (PARTITION BY <key>) = 1`

Also check:

• Will a re-run create duplicate rows?
• Is the write disposition (`WRITE_TRUNCATE` vs `WRITE_APPEND`) intentional

and documented?

• Is `run_id` being used as part of the merge or dedupe key? If so, flag it.

`run_id` should be stored as a metadata column, not as part of the uniqueness key, unless you explicitly want multi-run history.

State the recommended approach and the exact dedup key for this codebase.

---

F) OBSERVABILITY: Can you debug a failure?

Verify:

• Failures raise exceptions and abort with no silent `except: pass` or warn-only
• Each BQ job logs: job ID, bytes processed or billed when available,

slot milliseconds, and duration

• A run summary is logged or written at the end containing:

`run_id, env, mode, date_range, tables written, total BQ jobs, total bytes`

• `run_id` is present and consistent across all log lines

If `run_id` is missing, propose a one-line fix: `run_id = run_id or datetime.utcnow().strftime('%Y%m%dT%H%M%S')`

---

Final

**1. PASS / FAIL** with specific reasons per section (A to F). **2. Patch list** ordered by risk, referencing exact functions to change. **3. If FAIL: Top 3 cost risks** with a rough worst-case estimate (e.g., "loop over 90 dates x 3 retries = 270 BQ jobs").