How to Denormalize Data in BigQuery

How do I denormalize multiple tables into one BigQuery table?

Denormalizing data in BigQuery flattens related tables into one wide table to improve query speed and simplify analysis.

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Description

Example H2

Example H3

Why denormalize data in BigQuery?

BigQuery charges by bytes processed. Joining multiple tables on every query inflates scanned data and latency. A denormalized table stores the final shape your dashboards need, eliminating repeated joins and cutting cost.

Which ecommerce tables should be merged?

Start with high-traffic fact tables.For ecommerce, join Orders with Customers, OrderItems, and Products so analysts can filter by customer traits and product details without extra JOINs.

How to build a denormalized SELECT?

Use LEFT JOINs to pull one-to-one attributes (e.g., customer name) and ARRAY_AGG for one-to-many relationships (e.g., order items). Wrap ARRAY_AGG inside STRUCT to keep nested columns organized.

Example pattern

ARRAY_AGG(STRUCT(oi.product_id, p.name AS product_name, oi.quantity)) AS items

How to write the CREATE TABLE AS SELECT?

Use CREATE OR REPLACE TABLE project.dataset.orders_denorm AS followed by your SELECT statement.Schedule it with Cloud Scheduler and Cloud Functions or an ELT tool so it refreshes hourly.

How to materialize and auto-refresh?

Materialized views auto-refresh when source tables change. Wrap your denormalized query in CREATE MATERIALIZED VIEW. Ensure all joins use deterministic functions and no non-deterministic UDFs.

What maintenance strategies work best?

Partition denormalized tables by order_date and cluster by customer_id.Use incremental loads with WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY) to refresh only new data.

How to validate row counts?

After each refresh, compare COUNT(DISTINCT order_id) in the denormalized table against the Orders table. Alert on mismatches via Cloud Monitoring.

Best practices checklist

• Document column lineage.
• Keep raw normalized data for flexibility.
• Limit ARRAY sizes with HAVING COUNT(<1000).
• Recreate table on schema change to avoid legacy rows.

Why How to Denormalize Data in BigQuery is important

How to Denormalize Data in BigQuery Example Usage


-- Get last week’s orders already denormalized
SELECT order_id,
       customer_name,
       order_date,
       item.product_name,
       item.quantity
FROM   project.dataset.orders_denorm,
       UNNEST(items) AS item
WHERE  order_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY) AND CURRENT_DATE();

How to Denormalize Data in BigQuery Syntax


CREATE OR REPLACE TABLE project.dataset.orders_denorm AS
SELECT
  o.id AS order_id,
  o.order_date,
  o.total_amount,
  c.id AS customer_id,
  c.name   AS customer_name,
  c.email  AS customer_email,
  ARRAY_AGG(STRUCT(
      oi.product_id,
      p.name     AS product_name,
      p.price,
      oi.quantity
  )) AS items
FROM `project.dataset.Orders`   o
JOIN `project.dataset.Customers`  c ON c.id = o.customer_id
JOIN `project.dataset.OrderItems` oi ON oi.order_id = o.id
JOIN `project.dataset.Products`   p ON p.id = oi.product_id
GROUP BY o.id, o.order_date, o.total_amount, c.id, c.name, c.email;

Common Mistakes

Ignoring ARRAY explosion. Selecting ARRAY_AGG without GROUP BY causes one giant row. Always GROUP BY the unique parent key (order_id) to keep row counts correct.
Rebuilding the whole table daily. Full refreshes waste slots and money. Use date partitions and incremental WHERE clauses to load only new or changed rows.