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:

A single set-based query with

GENERATE_DATE_ARRAY

A staging table loaded with all dates then one join query

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: MERGE on a deterministic key (e.g., entity_id + date + model_version ) Write to a staging table scoped to the run, then swap or merge into final Append-only with a dedupe view: QUALIFY ROW_NUMBER() OVER (PARTITION BY ) = 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").