BigQuery Querying Tips & Best Practices for Faster, Cost-Efficient Analysis

Discover expert BigQuery querying tips to optimize performance, reduce costs, and improve efficiency. Learn best practices for filtering data, optimizing joins, using partitions, and writing scalable queries. Maximize your BigQuery potential today!

Shreya Banker

Data Analyst enthusiast. More than 7 years of exposure in Data Analysis and Software programming. I am a highly motivated, versatile IT professional with experience in Data Analysis, Visualization and Database Management. I look for the hardest problem to solve and where I can learn and develop the most. I love a challenge and never run from a difficult task. I'm determined to succeed, and I look forward to what life has to offer.

Google BigQuery is a powerful, scalable platform designed for fast and efficient analysis of large datasets. By applying best practices, you can optimize query performance, reduce costs, and improve maintainability. Here’s a step-by-step guide to help you get the most out of BigQuery.

1. Plan Before You Query

Before jumping into writing a query, take a moment to:

Understand the schema: Know your table structure, including nested and repeated fields.
Define your goal: Be clear about what you want to achieve.

2 . Filter Data Early

One of the simplest ways to speed up queries is to reduce the amount of data BigQuery processes:

Add WHERE clauses as early as possible.
Use partition filters like _PARTITIONTIME or _TABLE_SUFFIX to query specific time ranges.
Avoid full table scans unless necessary.

3 . Avoid SELECT *

Using SELECT * to query all columns is tempting. It is quick and includes all columns without extra effort. However, it is not efficient because it retrieves extra data. This can lead to higher costs, slower performance, and risks if the schema changes. To ensure better query performance and maintainability,
Selecting only the columns you need is best, reducing data processing overhead. Additional columns should be included only when required to avoid unnecessary complexity and costs. This approach prioritizes efficiency and scalability in the long term.

4. Handle Nested Data Carefully

BigQuery often deals with nested and repeated fields, like in Google Analytics 4 datasets. To avoid inefficiency:

Use UNNEST() only when necessary.
Filter data while unnesting to minimize processing.

SELECT user_pseudo_id, event_name FROM `project.dataset.events_*`,

UNNEST(event_params) AS params WHERE params.key = 'page_location';

5. Optimize Aggregations

When working with metrics or counts:

Aggregate data as early as possible.
Use efficient functions like COUNTIF() and SAFE_DIVIDE().

6. Use Modular Querying

Break complex queries into smaller, reusable parts using Common Table Expressions (CTEs):

WITH recent_events AS (

SELECT user_pseudo_id, event_date

FROM `project.dataset.events_*`

WHERE _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'

)

SELECT event_date, COUNT(DISTINCT user_pseudo_id) AS daily_active_users

FROM recent_events

GROUP BY event_date;

7. Be Smart with Joins and Unions

Join only the data you need. Filter datasets before performing joins.
Avoid unnecessary UNION DISTINCT, as it adds computational overhead.

8. Partitioning and Clustering for Performance

Partitioning and clustering can greatly improve query speed:

Partition tables by date for time-based queries.
Cluster by commonly filtered fields, like country or event_name.

9. Use Built-In functions

BigQuery has many powerful built-in functions:

Use SAFE_DIVIDE() to avoid division errors.
Take advantage of array and string functions for transformations.

10. Test and Debug Queries

Use LIMIT to test your queries with smaller datasets.
Check execution details to identify bottlenecks and optimize.

11. Monitor Performance

Regularly review query statistics and logs in the BigQuery interface.
Use caching for repeated queries to save time and costs.

12. Minimize Costs

BigQuery charges based on the amount of data processed:

Preview data with TABLESAMPLE or the table preview option.
Process only the necessary partitions and columns.

13. Document and Collaborate

Add comments to your queries to explain their purpose and logic.
Use clear, consistent naming conventions for tables, columns, and aliases.
Share intermediate results as temporary tables or materialized views.

Here’s an example of a BigQuery SQL query that incorporates all the best practices mentioned:

Query: Analyzing Daily Active Users by Traffic Source

-- Purpose: Analyze daily active users by traffic source for the past 30 days.

-- Inputs: Google Analytics 4 event data from `project.dataset.events_*`.

-- Assumptions:

-- - Data is partitioned by event date using _TABLE_SUFFIX.

-- - Only page views and app sessions are considered.

-- Author: Shreya

-- Version: 1.0

-- Last Updated: 2025-01-15

-- Optimization: Query is scoped to the past 30 days to reduce cost.

WITH filtered_data AS (

-- Step 1: Filter data early by date and relevant events

SELECT

user_pseudo_id,

DATE(TIMESTAMP_MICROS(event_timestamp)) AS event_date,

(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'source') AS traffic_source,

event_name

FROM

`project.dataset.events_*`

WHERE

_TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))

AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())

AND event_name IN ('page_view', 'session_start') -- Only relevant events

),

aggregated_data AS (

-- Step 2: Aggregate daily active users and group by traffic source

SELECT

event_date,

traffic_source,

COUNT(DISTINCT user_pseudo_id) AS daily_active_users

FROM

filtered_data

WHERE

traffic_source IS NOT NULL -- Exclude null traffic sources

GROUP BY

event_date, traffic_source

),

ranked_data AS (

-- Step 3: Add rankings for the top traffic sources per day

SELECT

event_date,

traffic_source,

daily_active_users,

RANK() OVER (PARTITION BY event_date ORDER BY daily_active_users DESC) AS rank

FROM

aggregated_data

)

-- Step 4: Final output with top 5 traffic sources per day

SELECT

event_date,

traffic_source,

daily_active_users,

rank

FROM

ranked_data

WHERE

rank <= 5 -- Include only the top 5 traffic sources per day

ORDER BY

event_date, rank;

Conclusion

Google BigQuery transforms data analytics, but its true power depends on how well you optimize queries. By filtering data early, using partitioning and clustering, and managing costs, you can boost performance while staying within budget. so query wisely!

Need expert guidance to maximize your BigQuery efficiency? Napkyn specializes in advanced analytics, implementation, and optimization strategies to help you unlock the full potential of your data. Contact us today to streamline your analytics and drive smarter business decisions!

More Insights

AI: Friend, Foe, or Fancy Buzzword?

Jasmine Libert

Senior Vice President, Data Solutions

Mar 5, 2025

Google Consent Mode’s Impact on Marketing Platforms: What Advertisers Need to Know

Monika Boldak

Associate Director, Marketing

Feb 26, 2025

Consent Mode: Privacy-First Data Collection in Google Analytics

Jasmine Libert

Senior Vice President, Data Solutions

Feb 19, 2025

More Insights

Sign Up For Our Newsletter

Napkyn Inc.
204-78 George Street, Ottawa, Ontario, K1N 5W1, Canada

Napkyn US
6 East 32nd Street, 9th Floor, New York, NY 10016, USA

212-247-0800 | info@napkyn.com

About

Services

Our Work

Insights

Webinars

Careers

Kepler

Kyu

Get in Touch

BigQuery Querying Tips & Best Practices for Faster, Cost-Efficient Analysis

Shreya Banker

1. Plan Before You Query

Before jumping into writing a query, take a moment to:

Understand the schema: Know your table structure, including nested and repeated fields.
Define your goal: Be clear about what you want to achieve.

2 . Filter Data Early

One of the simplest ways to speed up queries is to reduce the amount of data BigQuery processes:

Add WHERE clauses as early as possible.
Use partition filters like _PARTITIONTIME or _TABLE_SUFFIX to query specific time ranges.
Avoid full table scans unless necessary.

3 . Avoid SELECT *

Using SELECT * to query all columns is tempting. It is quick and includes all columns without extra effort. However, it is not efficient because it retrieves extra data. This can lead to higher costs, slower performance, and risks if the schema changes. To ensure better query performance and maintainability,
Selecting only the columns you need is best, reducing data processing overhead. Additional columns should be included only when required to avoid unnecessary complexity and costs. This approach prioritizes efficiency and scalability in the long term.

4. Handle Nested Data Carefully

BigQuery often deals with nested and repeated fields, like in Google Analytics 4 datasets. To avoid inefficiency:

Use UNNEST() only when necessary.
Filter data while unnesting to minimize processing.

SELECT user_pseudo_id, event_name FROM `project.dataset.events_*`,

UNNEST(event_params) AS params WHERE params.key = 'page_location';

5. Optimize Aggregations

When working with metrics or counts:

Aggregate data as early as possible.
Use efficient functions like COUNTIF() and SAFE_DIVIDE().

6. Use Modular Querying

Break complex queries into smaller, reusable parts using Common Table Expressions (CTEs):

WITH recent_events AS (

SELECT user_pseudo_id, event_date

FROM `project.dataset.events_*`

WHERE _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'

)

SELECT event_date, COUNT(DISTINCT user_pseudo_id) AS daily_active_users

FROM recent_events

GROUP BY event_date;

7. Be Smart with Joins and Unions

Join only the data you need. Filter datasets before performing joins.
Avoid unnecessary UNION DISTINCT, as it adds computational overhead.

8. Partitioning and Clustering for Performance

Partitioning and clustering can greatly improve query speed:

Partition tables by date for time-based queries.
Cluster by commonly filtered fields, like country or event_name.

9. Use Built-In functions

BigQuery has many powerful built-in functions:

Use SAFE_DIVIDE() to avoid division errors.
Take advantage of array and string functions for transformations.

10. Test and Debug Queries

Use LIMIT to test your queries with smaller datasets.
Check execution details to identify bottlenecks and optimize.

11. Monitor Performance

Regularly review query statistics and logs in the BigQuery interface.
Use caching for repeated queries to save time and costs.

12. Minimize Costs

BigQuery charges based on the amount of data processed:

Preview data with TABLESAMPLE or the table preview option.
Process only the necessary partitions and columns.

13. Document and Collaborate

Add comments to your queries to explain their purpose and logic.
Use clear, consistent naming conventions for tables, columns, and aliases.
Share intermediate results as temporary tables or materialized views.

Here’s an example of a BigQuery SQL query that incorporates all the best practices mentioned:

Query: Analyzing Daily Active Users by Traffic Source

-- Purpose: Analyze daily active users by traffic source for the past 30 days.

-- Inputs: Google Analytics 4 event data from `project.dataset.events_*`.

-- Assumptions:

-- - Data is partitioned by event date using _TABLE_SUFFIX.

-- - Only page views and app sessions are considered.

-- Author: Shreya

-- Version: 1.0

-- Last Updated: 2025-01-15

-- Optimization: Query is scoped to the past 30 days to reduce cost.

WITH filtered_data AS (

-- Step 1: Filter data early by date and relevant events

SELECT

user_pseudo_id,

DATE(TIMESTAMP_MICROS(event_timestamp)) AS event_date,

(SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'source') AS traffic_source,

event_name

FROM

`project.dataset.events_*`

WHERE

_TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))

AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())

AND event_name IN ('page_view', 'session_start') -- Only relevant events

),

aggregated_data AS (

-- Step 2: Aggregate daily active users and group by traffic source

SELECT

event_date,

traffic_source,

COUNT(DISTINCT user_pseudo_id) AS daily_active_users

FROM

filtered_data

WHERE

traffic_source IS NOT NULL -- Exclude null traffic sources

GROUP BY

event_date, traffic_source

),

ranked_data AS (

-- Step 3: Add rankings for the top traffic sources per day

SELECT

event_date,

traffic_source,

daily_active_users,

RANK() OVER (PARTITION BY event_date ORDER BY daily_active_users DESC) AS rank

FROM

aggregated_data

)

-- Step 4: Final output with top 5 traffic sources per day

SELECT

event_date,

traffic_source,

daily_active_users,

rank

FROM

ranked_data

WHERE

rank <= 5 -- Include only the top 5 traffic sources per day

ORDER BY

event_date, rank;

Conclusion

More Insights