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 <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

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").

bigquery-pipeline-audit

NPX Install

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