How to Denormalize Data in Redshift

How do I denormalize tables in Amazon Redshift?

Denormalizing data in Redshift flattens relational tables into wider, query-ready tables or materialized views to speed up analytics.

Welcome to the Galaxy, Guardian!
You'll be receiving a confirmation email

Follow us on twitter :)

Oops! Something went wrong while submitting the form.

Description

Example H2

Example H3

Why denormalize data in Redshift?

Denormalization reduces joins, cutting cross-node data movement and CPU usage. Fewer joins mean faster dashboards and cheaper clusters.

What is the quickest denormalization method?

CREATE TABLE AS (CTAS) writes the result of a SELECT into a new table, letting you set distribution style and sort keys in one step.

How does CTAS work step by step?

1. Draft a SELECT that joins normalized tables.
2. Choose a DISTKEY matching frequent filters.
3.Add SORTKEYs that support range scans.
4. Run CTAS to create the flattened table.
5. Schedule refresh via orchestration.

Should I use materialized views instead?

Materialized views keep data fresh automatically. Use them when your workload tolerates their refresh lag and immutable key/sort design.

When do I refresh denormalized tables?

Refresh after every ETL batch or on a cron aligned with SLA.Large tables benefit from dropping and re-running CTAS with ALTER TABLE APPEND to avoid vacuum overhead.

Best practices for Redshift denormalization

• Align DISTKEY with join columns.
• Keep SORTKEYs narrow.
• Vacuum after big inserts.
• Use ENCODE AUTO for compression.
• Tag tables for automated housekeeping.

Common mistakes and fixes

Skipping distribution keys causes random distribution and full scans—always set a DISTKEY. Relying on row-by-row INSERT bloats storage—prefer CTAS or COPY + ALTER TABLE APPEND.

Why How to Denormalize Data in Redshift is important

How to Denormalize Data in Redshift Example Usage


-- Query top customers after denormalization
SELECT customer_id,
       customer_name,
       SUM(line_total) AS lifetime_value
FROM   denormalized_orders
GROUP  BY customer_id, customer_name
ORDER  BY lifetime_value DESC
LIMIT  10;

How to Denormalize Data in Redshift Syntax


-- Denormalize Orders with CTAS
CREATE TABLE denormalized_orders
DISTSTYLE KEY
DISTKEY (customer_id)
SORTKEY  (order_date)
AS
SELECT  o.id              AS order_id,
        c.id              AS customer_id,
        c.name            AS customer_name,
        c.email           AS customer_email,
        o.order_date,
        o.total_amount,
        p.id              AS product_id,
        p.name            AS product_name,
        p.price,
        oi.quantity,
        (oi.quantity * p.price) AS line_total
FROM    Orders      o
JOIN    Customers   c  ON c.id = o.customer_id
JOIN    OrderItems  oi ON oi.order_id = o.id
JOIN    Products    p  ON p.id = oi.product_id;

-- Materialized view alternative
CREATE MATERIALIZED VIEW mv_denormalized_orders
DISTKEY (customer_id)
SORTKEY  (order_date)
AS
SELECT /* same SELECT as above */;

Common Mistakes

Mistake: Using random distribution. Why wrong: Scatters related rows, forcing full table scans. Fix: Set a meaningful DISTKEY like customer_id.
Mistake: Refreshing with DELETE → INSERT. Why wrong: Creates table bloat and slows queries. Fix: Rebuild with CTAS then ALTER TABLE APPEND or use materialized views.