How to Apply Data Modeling Best Practices in BigQuery

Galaxy Glossary

How do I model data efficiently in BigQuery?

BigQuery data modeling organizes tables, partitions, and relationships to minimize cost and maximize query speed.

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Description

Example H2

Example H3

What problems do BigQuery data modeling best practices solve?

Sound modeling cuts scan costs, accelerates queries, and simplifies maintenance. Poorly-organized tables force full scans and hinder BI adoption.

Which table structures does BigQuery favor?

Denormalized, partitioned, and clustered tables reduce shuffles and slot usage. Store semirelational data (orders with items) in nested, repeated columns when joins are minimal.

How do I decide between wide tables and star schemas?

Choose wide tables for write-once analytical data (event logs). Use star schemas—fact plus dimension tables—when dimensions change frequently or are reused across facts.

Why partition and cluster together?

Partition prunes large date ranges; clustering orders rows, enabling micro-pruning inside each partition. The duo slashes scanned bytes without manual indexes.

When should I partition a table?

Partition on a column queried with range filters ≥90% of the time—typically DATE(order_date) or TIMESTAMP(created_at).

What makes a good clustering key?

Use low-cardinality columns frequently filtered or grouped (customer_id, product_id). Limit keys to ≤4 to avoid diminishing returns.

How do nested and repeated fields help?

BigQuery stores arrays and structs column-wise, so nested OrderItems inside Orders keep data co-located and remove joins—ideal for daily exports from OLTP.

How to enforce schema evolution safely?

Add nullable columns instead of dropping or renaming. Use views to abstract old names. Schedule data quality tests to catch breaking changes early.

What governance tactics matter most?

Adopt descriptive naming (stg_, dim_, fct_), document column meaning in INFORMATION_SCHEMA, and lock production datasets with IAM roles (dataViewer, dataOwner).

Why How to Apply Data Modeling Best Practices in BigQuery is important

How to Apply Data Modeling Best Practices in BigQuery Example Usage


-- Daily revenue by product for last 7 days, reading only needed partitions
SELECT
  p.id            AS product_id,
  p.name,
  SUM(i.quantity * i.price) AS revenue
FROM ecommerce.fct_orders o,
     UNNEST(o.items) i
JOIN ecommerce.dim_products p ON p.id = i.product_id
WHERE o.order_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY) AND CURRENT_DATE()
GROUP BY product_id, p.name
ORDER BY revenue DESC;

How to Apply Data Modeling Best Practices in BigQuery Syntax


-- Create a partitioned & clustered fact table
CREATE TABLE ecommerce.fct_orders (
  id            INT64,
  customer_id   INT64,
  order_date    DATE,
  total_amount  NUMERIC,
  items         ARRAY<STRUCT<product_id INT64, quantity INT64, price NUMERIC>>
)
PARTITION BY DATE(order_date)
CLUSTER BY customer_id;

-- Create dimension tables
CREATE TABLE ecommerce.dim_customers (
  id          INT64,
  name        STRING,
  email       STRING,
  created_at  TIMESTAMP
);

CREATE TABLE ecommerce.dim_products (
  id     INT64,
  name   STRING,
  price  NUMERIC,
  stock  INT64
);

Common Mistakes

Mistake: Partitioning on high-cardinality columns like customer_id. Why wrong: Creates thousands of tiny partitions, slowing metadata operations. Fix: Partition on DATE/TIMESTAMP, then cluster by customer_id.
Mistake: Relying on SELECT * in scheduled queries. Why wrong: Adds unused columns, inflates scanned bytes. Fix: List only required columns or create views with trimmed schemas.