How to Version Control BigQuery Queries in PostgreSQL

How do I version control BigQuery queries?

Version control stores every change to your BigQuery SQL so teams can track history, collaborate safely, and roll back quickly.

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Description

Example H2

Example H3

Why should I version-control BigQuery queries?

Version control guarantees a clear history of every SQL change, enabling safe rollbacks, peer reviews, and reproducible analytics. It removes confusion over which query drives a dashboard or model.

Which tools work best?

GitHub, GitLab, Bitbucket, or Cloud Source Repositories hold .sql files. dbt, Terraform, or simple bash scripts deploy them to BigQuery.Galaxy’s desktop editor integrates Git and AI refactors, speeding reviews.

How do I organize my repo?

Use one folder per business domain. Inside each, add views/, models/, and udfs/. Name files with incremental prefixes like 001_create_customers_view.sql so Git diffs stay concise.

How do I track schema versions?

Create migration-style scripts that convert tables—for example, adding address to Customers.Each script lives in Git and runs through CI/CD.

How do I store versions inside BigQuery?

Spin up a metadata dataset and a QueryVersions table that logs every production run. Insert the SQL hash, author, and timestamp after each deployment so auditors can query lineage.

What does a CI/CD pipeline look like?

1) Developer pushes a branch. 2) GitHub Action runs bq query --dry_run for syntax checks. 3) Reviewer approves.4) Main branch merges and a deploy job executes scripts in numeric order, inserting a record into metadata.QueryVersions.

Best practices for BigQuery version control?

Keep queries idempotent using CREATE OR REPLACE. Tag releases with Git tags matching dataset versions. Bundle related SQL in a single PR. Use feature branches to isolate experiments.

How does Galaxy help?

Galaxy’s AI copilot auto-generates commit messages, suggests migration scripts, and shows inline Git diffs next to the editor.Teams endorse validated queries so only trusted SQL reaches production.

Quick checklist

✔️ Store every query as code.
✔️ Lint and dry-run in CI.
✔️ Log versions in BigQuery.
✔️ Review through pull requests.
✔️ Use Galaxy for faster edits and sharing.

Why How to Version Control BigQuery Queries in PostgreSQL is important

How to Version Control BigQuery Queries in PostgreSQL Example Usage


-- Example: version 3 calculates total spend per customer
CREATE OR REPLACE VIEW analytics.customer_ltv AS
SELECT c.id, c.name,
       SUM(o.total_amount) AS lifetime_value
FROM   Customers c
JOIN   Orders o ON o.customer_id = c.id
GROUP  BY c.id, c.name;

-- Log version 3
DECLARE sql_text STRING DEFAULT '''CREATE OR REPLACE VIEW ...''';
INSERT INTO metadata.QueryVersions
(version_id, query_name, query_text, author, created_at, deployed_by)
VALUES
(3, 'customer_ltv', sql_text, 'bob@shop.com', CURRENT_TIMESTAMP(), 'manual');

How to Version Control BigQuery Queries in PostgreSQL Syntax


-- Create a metadata table to track versions
CREATE TABLE IF NOT EXISTS metadata.QueryVersions (
  version_id    INT64,
  query_name    STRING,
  query_text    STRING,
  author        STRING,
  created_at    TIMESTAMP,
  deployed_by   STRING
);

-- Example deployment pattern
-- 1. Add new query file 002_total_sales.sql
-- 2. CI job executes:
--    bq query --replace --use_legacy_sql=false < 002_total_sales.sql
-- 3. Insert a new record in QueryVersions
INSERT INTO metadata.QueryVersions
(version_id, query_name, query_text, author, created_at, deployed_by)
VALUES
(2, 'total_sales', @query_text, 'alice@shop.com', CURRENT_TIMESTAMP(), 'github-actions');

Common Mistakes

Only storing queries as BigQuery views without source .sql files. This hides history. Fix by committing every view definition to Git and redeploying with CI.
Hard-coding dataset names in SQL. Environment changes break scripts. Fix by parameterizing dataset/project IDs and passing them through CI variables.