How to Apply BigQuery Best Practices

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What are the most effective BigQuery best practices?

Concrete guidelines to cut cost and speed up queries in Google BigQuery.

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Description

Example H2

Example H3

What problems do BigQuery best practices solve?

They reduce query cost, improve performance, and keep datasets maintainable by enforcing smart table design, query patterns, and resource governance.

How should I structure tables for performance?

Partition large fact tables by a frequently-filtered DATE/TIMESTAMP column such as order_date. Add clustering on high-cardinality columns like customer_id or product_id to prune blocks during scans.

Why combine partitioning and clustering?

Partitioning narrows scanned partitions; clustering orders data within each partition, allowing BigQuery to skip more blocks and lower slot usage.

When is SELECT * acceptable?

Only in ad-hoc exploration on small tables. In production queries, list required columns to avoid reading unneeded column blocks.

How do I avoid cross-region data shuffles?

Store datasets and run queries in the same location. Specify location="US" or location="EU" in client libraries to prevent costly cross-region copies.

What query patterns save money?

Filter early, use subqueries or CTEs to restrict rows before joins, and join on partition and clustering keys to preserve pruning. Always qualify tables with project.dataset.table to avoid accidental wildcard scans.

Should I use temporary or materialized views?

Materialized views cache results and auto-refresh, cutting cost on repeated reporting queries. Temporary CTEs are free but evaluate on every run. Use materialized views for dashboards refreshed many times a day.

How do I monitor query cost?

Query PLAN and STAGE details in the UI reveal bytes scanned. Set custom cost controls with maximum_bytes_billed in a job configuration to fail runaway queries automatically.

What governance settings help teams?

Create separate projects for dev, staging, and prod. Apply IAM roles minimally and use authorized views or column-level security to expose only necessary data to each group.

Why How to Apply BigQuery Best Practices is important

How to Apply BigQuery Best Practices Example Usage


-- Join partitioned Orders with clustered Customers efficiently
SELECT c.id, c.name, o.order_date, o.total_amount
FROM `shop.analytics.Customers`   c
JOIN `shop.analytics.Orders`      o
  ON o.customer_id = c.id
WHERE o.order_date BETWEEN '2024-01-01' AND '2024-01-31'
  AND c.created_at < '2024-01-01';

How to Apply BigQuery Best Practices Syntax


-- Create a partitioned, clustered table for Orders
CREATE TABLE `shop.analytics.Orders`
PARTITION BY DATE(order_date)
CLUSTER BY customer_id AS
SELECT id, customer_id, order_date, total_amount
FROM `shop.raw.Orders`;

-- Cost guardrail when querying
DECLARE max_cost INT64 DEFAULT 1e9; -- 1 GB
EXECUTE IMMEDIATE "SELECT * FROM `shop.analytics.Orders` WHERE order_date > '2024-01-01'"
OPTIONS (maximum_bytes_billed = max_cost);

-- Materialized view for daily revenue
CREATE MATERIALIZED VIEW `shop.analytics.daily_revenue` AS
SELECT DATE(order_date) AS order_day,
       SUM(total_amount) AS revenue
FROM `shop.analytics.Orders`
GROUP BY order_day;

Common Mistakes

Running SELECT * on large fact tables. This scans all column blocks and inflates cost. Fix by explicitly selecting only needed columns.
Creating partitioned tables without ingestion-time filters. Queries that skip WHERE on the partition key scan every partition. Always filter by the partition column.