How to Version Control Redshift Queries in PostgreSQL

How can I version control Amazon Redshift SQL queries effectively?

Version-controlling Redshift SQL means storing every query as code, tracking changes with Git, and deploying updates with repeatable scripts.

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Description

Why put Redshift queries under version control?

Version control guarantees that every change to production SQL is reviewable, reversible, and documented. Git keeps a full history, while pull requests add peer review and CI checks.

Which objects should I track?

Track all CREATE VIEW, CREATE TABLE AS, stored procedures, and ad-hoc analysis queries that drive dashboards or applications. Ignore scratch queries that aren’t reused.

How do I structure the repository?

Organize by object type and business domain: /views/reporting/, /procedures/finance/, /analysis/ad-hoc/. Place one object per .sql file and include a semantic version in the file path or header.

File naming pattern

views/reporting/daily_sales/v1.0/daily_sales.sql keeps each release isolated. Git tags mirror the folder name.

What goes inside each .sql file?

Start with a YAML-style header: name, version, description, owner, and dependencies. Follow with CREATE OR REPLACE so deploy scripts stay idempotent.

Example header

-- name: daily_sales -- version: 1.0.0 -- owner: analytics@acme.com -- description: Daily sales by customer

How do I deploy changes safely?

Use a CI pipeline that lints SQL, runs unit tests in a staging Redshift cluster, and executes the file list inside a transaction: BEGIN; \i daily_sales.sql; COMMIT;.

How do I roll back?

Check out the previous Git tag, rerun the deploy script, and Redshift will recreate the prior objects. Git’s diff shows exactly what changed.

Best practices

1) Prefer CREATE OR REPLACE for idempotency. 2) Use semantic versioning (MAJOR.MINOR.PATCH). 3) Require code reviews before merging. 4) Tag each production deploy.

Common mistakes

Avoid editing SQL directly in the Redshift console—changes bypass Git. Also avoid storing multiple objects in one file; partial failures are hard to debug.

Why How to Version Control Redshift Queries in PostgreSQL is important

How to Version Control Redshift Queries in PostgreSQL Example Usage


-- Deploy latest tagged views to Redshift
BEGIN;
\i views/reporting/daily_sales/v1.0/daily_sales.sql
COMMIT;

-- Query the new view
SELECT *
FROM reporting.daily_sales
WHERE order_date = CURRENT_DATE;

How to Version Control Redshift Queries in PostgreSQL Syntax


-- Save versioned view
git checkout -b feature/daily_sales_v1_0

/* File: views/reporting/daily_sales/v1.0/daily_sales.sql */
-- name: daily_sales
-- version: 1.0.0
-- description: Daily sales by customer

CREATE OR REPLACE VIEW reporting.daily_sales AS
SELECT
    c.id  AS customer_id,
    c.name AS customer_name,
    o.order_date AS order_date,
    SUM(oi.quantity * p.price) AS total_spent
FROM Customers   c
JOIN Orders      o  ON o.customer_id = c.id
JOIN OrderItems  oi ON oi.order_id   = o.id
JOIN Products    p  ON p.id          = oi.product_id
GROUP BY c.id, c.name, o.order_date;

-- Commit and tag
git add .
git commit -m "Add daily_sales view v1.0.0"
git tag daily_sales/v1.0.0

Common Mistakes

Editing queries directly in the Redshift console bypasses Git. Fix by making all changes in the .sql files inside your repository and redeploying through CI.
Storing multiple objects in one .sql file causes partial failures on deploy. Fix by keeping one object per file and using a predictable folder hierarchy.

Frequently Asked Questions (FAQs)

Do I need separate repos for data models and analytics queries?

One repo is fine if your team is small. Use sub-folders or mono-repo style with clear ownership labels. Larger orgs may split repos to isolate permissions.

Can I use Liquibase or Flyway with Redshift?

Yes. Both tools support Redshift JDBC. Write migration scripts just like Postgres and let the tool handle ordering and history tracking.

How do I test a query before merging?

Spin up a temporary Redshift cluster or use a dedicated dev schema. Your CI pipeline should run the script, execute sample assertions, and tear down resources.