How to find who uses BigQuery

Who uses BigQuery and how can I list those users?

Identify which user principals are running jobs in your BigQuery project by querying audit and INFORMATION_SCHEMA views.

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Description

Example H2

Example H3

Why would I want to know who uses BigQuery?

Knowing the active user list helps control costs, tighten security, and allocate training resources.

Which tables store user activity in BigQuery?

Job-level metadata lives in <region>.INFORMATION_SCHEMA.JOBS_BY_* views and Cloud Audit Logs. These views expose principal_email for every job.

How do I list query runners in the last 30 days?

Filter JOBS_BY_ORGANIZATION on creation_time and aggregate by principal_email. Limit the window to avoid scanning an excessive partition.

SELECT principal_email, COUNT(*) AS job_count, SUM(total_bytes_processed/1e12) AS tb_scanned FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_ORGANIZATION WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY) AND job_type = 'QUERY' GROUP BY principal_email ORDER BY job_count DESC;

Tip

Replace region-us with your datasets region to avoid cross-region charges.

How can I see which tables each user touches?

Join JOBS_BY_PROJECT to JOBS_TIMELINE_BY_PROJECT or parse the referenced_tables array to break down activity by table.

Can I automate notifications for new users?

Schedule a query that compares todays user list against a reference table and pushes a Pub/Sub message when a new email appears.

Best practices for tracking BigQuery users

Partition by creation_time, leverage WITH CONNECTION scheduled queries, and store snapshots in a low-cost tier for audit purposes.

Common pitfalls

Querying the wrong region or scanning entire history dramatically raises cost. Forgetting to include service accounts skews usage totals.

Why How to find who uses BigQuery is important

How to find who uses BigQuery Example Usage


-- Who queried the Products table in the last 24 hours?
SELECT principal_email,
       COUNT(*) AS queries,
       ROUND(SUM(total_bytes_processed)/1e9,2) AS gb_scanned
FROM  `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
  AND JSON_EXTRACT_SCALAR(referenced_tables, '$[0].table_name') = 'Products'
GROUP BY principal_email
ORDER BY gb_scanned DESC;

How to find who uses BigQuery Syntax


-- Basic syntax to discover users running jobs
SELECT principal_email [, agg_functions]
FROM  `&lt;region&gt;.INFORMATION_SCHEMA.JOBS_BY_<SCOPE>`
WHERE creation_time BETWEEN <start_ts> AND <end_ts>
  [AND job_type = 'QUERY']
GROUP BY principal_email
ORDER BY <metric> DESC;

-- Example (E-commerce project)
-- Which analysts updated the Orders table this week?
SELECT principal_email,
       COUNT(*) AS updates
FROM  `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
  AND referenced_tables LIKE '%Orders%'
  AND job_type = 'QUERY'
GROUP BY principal_email;

Common Mistakes

Mistake: Querying US multi-region when your datasets sit in europe-west2. This triggers cross-region reads and higher latency. Fix: Always query the region-specific INFORMATION_SCHEMA view matching your data location.
Mistake: Ignoring service accounts. Reports filtered to human emails miss scheduled dataflow users, leading to incomplete audits. Fix: Include all principals or tag accounts for filtering later.