Google BigQuery Limits & Quotas: Comprehensive Guide 101

on BigQuery Limits, BigQuery Quotas, Data Warehouse, Google BigQuery, Google Cloud Platform, Tutorials • February 1st, 2022 • Write for Hevo

BigQuery Limits

Companies allocate a significant amount of budget for managing the data and other business operations for Marketing and Analytics. They know the importance of data in making data-driven business decisions and how it benefits them. Google BigQuery is a Data Warehousing solution widely used by companies to store and Analyze data. 

Google BigQuery charges its users on a use per basis, so it is essential to keep the track of usage limits and you have enough quota left with you for your projects. Google BigQuery Limits and Quotas section will provide you with all the information about the usage of resources and how much you left with.

Google BigQuery Limits and Quotas is an essential part of Project Management and developers should try to optimize the development to minimize the usage of Google Cloud resources. In this article, you will learn about Google BigBigQuery Limits and Quotas, and how to set up custom Limits and Quotas in Google Cloud Console. Also, you will read about a few important Google BigQuery Limits that are widely used while working with data.

Table of Contents


  • An active Google BigQuery Account.

Introduction to Google BigQuery 

Image Source

Google BigQuery is a fully managed Cloud Data Warehouse offered by Google and part of Google Cloud Platform (GCP). It allows users to store huge volumes of data in a single place for Analytics purposes. Companies widely use it for managing their business data and use it further to make data-driven decisions using its high query performance. Google BigQuery is capable of managing terabytes of data and analyzing data for you faster using standard SQL queries and generating rich insights from it.

Google BigQuery is built on Dremel’s technology that enables it to deliver high performance using fewer resources and comes with a Columnar Storage structure that optimizes the data storage and speeds up the query response. Users can independently scale up or down the computation and storage simultaneously as per their requirements.

Key Features of Google BigQuery 

Some of the main features of Google BigQuery are listed below:

  • BI Engine: Google BigQuery allows users to Analyze large datasets directly into the Data Warehouse with the help of its in-memory analysis service. It makes the sub-query response faster.
  • BigQuery ML: Google BigQuery comes with a pre-configured Machine Learning environment so that users can use the data and train models right there from the Data Warehouse.
  • Integrations: Google BigQuery offers integrations with Google products, and many 3rd party apps and services to sync data. 

To learn more about Google BigQuery, click here.

Simplify Data Analysis with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Get Started with Hevo for Free

Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.

Check out why Hevo is the Best:

  1. Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  2. Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  3. Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  4. Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  5. Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  6. Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, E-Mail, and support calls.
  7. Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!

Google BigQuery Limits and Quotas

Google BigQuery can restrict how much the shared Google Cloud resources your Cloud project can use including the software, hardware, and other network components. It becomes essential to keep the track of all the resources in use while your project is running and what are the essential requirements of your project because Google BigQuery Limits the resources according to your particular Google BigQuery Quotas. 

Google BigQuery Limits are unrelated to the quota system as these BigQuery Limits cannot be altered unless stated. The default configuration follows that Google BigQuery Limits and Quotas are applicable on a per-project basis. This means that every project in the Google Cloud will have its individual Google BigQuery Limits and Quotas. For example, the Google BigQuery Limits and Quotas apply on different bases such as the maximum number of concurrent API requests made per user or the maximum number of columns per table.

The Google BigQuery Limits and Quotas are responsible for the following things of your system, listed below:

  • It monitors and keeps track of the consumption of the resources of Google Cloud projects and services.
  • It can also restrict your Google Cloud project from using the resources by restricting them to ensure fairness and reduce the spikes in usage when something wrong goes with the project.
  • You can create prescribed restrictions, and the Google BigQuery Limits and Quotas feature will maintain configurations that automatically enforce restrictions.
  • Google BigQuery Limits and Quotas offers a means that allows you to make or request changes to the BigQuery Quota.

When Google BigQuery Limits and Quotas exceeds the limit, the system automatically blocks and or stops offering access to the particular Google Cloud resource. The tasks that are running in that time will fail because the resources are unaccessible to your project.

To avoid any disruptions in the services and accessing resources to the project, Google BigQuery offers intermittent refresh when the quota is exhausted. Daily quotas are replenished at regular intervals throughout the day.

Google BigQuery Limits and Quotas of Jobs

Now that you have understood about Google BigQuery Limits and Quotas. In this section, you will read about the limits and quotas of some of the widely used jobs in Google BigBigQuery. The following jobs are listed below.

1) Google BigQuery Limits: Copy Jobs

The Google BigQuery Limits are applicable on the jobs for copying tables. It includes all the jobs created by using the command-line tool, the bq command, Google Cloud Console, or copy-type jobs.insert API method. The following limits and quotas for the usage of copying tables jobs are given below.

Copy jobs per day100,000 jobs
Cross-region copy jobs per destination table per day100 jobs
Cross-region copy jobs per day2,000 jobs

Similarly, the details on limits and quotas for copying datasets are available here.

2) Google BigQuery Limits: Export Jobs

Google BigQuery can export up to 1GB of data to a single file. The limits on jobs that export data by using the bq command, command-line tools, and Cloud Console or the export-type jobs.insert API method. The following limits and quotas for the usage of export data jobs are given below.

Maximum number of exported bytes per day50 TB
Maximum number of exports per day100,000 exports
Wildcard URIs per export500 URIs

3) Google BigQuery Limits: Query Jobs

The jobs.query and query-type jobs.insert API methods are used to create query jobs by automatically running scheduled queries, interactive queries. The Google BigQuery Quotas for the following query jobs are given below.

Query usage per dayUnlimitedYour project can run an unlimited number of queries per day.
Query usage per day per userUnlimitedUsers can run an unlimited number of queries per day.
Cloud SQL federated query cross-region bytes per day1 TBYour project can run up to 1 TB in cross-region queries per day. 

4) Google BigQuery Limits: Row-level Security

The Row-level security allows users to filter data and provide access to specific rows available in the table based on the given conditions. The Google BigQuery supports access controls at the project, dataset, and table levels, as well as column-level security through policy tags. The following limits apply on Row-level access policies are given below.

Maximum number of row access policies per table100 policiesA table can have up to 100 row access policies.
Maximum number of row access policies per query100 policiesA query can access up to a total of 100 row access policies.
Maximum number of CREATE / DROP DDL statements per policy per 10 seconds5 statementsYour project can make up to five CREATE or DROP statements per row access policy resource every 10 seconds.
DROP ALL ROW ACCESS POLICIES statements per table per 10 seconds5 statementsYour project can make up to five DROP ALL ROW ACCESS POLICIES statements per table every 10 seconds.

5) Google BigQuery Limits: Streaming Inserts

When you stream data into Google BigQuery by using the legacy streaming API, then you must ensure that you don’t exceed the Google BigQuery Limits and Quotas and stay within limits to avoid any errors else you will get the quotaExceeded errors. The following information on Streaming inserts limits and quotas is given below.

Maximum bytes per second per project in the us and eu multi-regions1 GB per second
Maximum bytes per second per project in all other locations300 MB per second
Maximum row size10 MB
HTTP request size limit10 MB
Maximum rows per request50,000 rows
insertId field length128 characters

Configuring Google BigQuery Custom Limits and Quotas

Users can set custom values to the Google BigQuery Limits and Quotas for any quota displayed on the Quotas page of the Google Cloud Console. You can request lower quota or higher quota limits depending on your requirements. 

If you are lowering your Google BigQuery Limits and Quotas configuration then it will take a few minutes to make changes. But if you are requesting higher quota limits then it will go through an approval process which will take more time to take effect. 

To change your Google BigQuery Limits and Quotas, you must have permissions of serviceusage.quotas.update. The following steps are listed below:

Step 1: Filtering Quotas

  • First, go to the Google BigQuery Limits and Quotas page, here.
Google BigQuery Limits and Quotas Console page
Image Source: Self
  • Make sure you select your project.
  • Now, find the quota that you want to increase or decrease in the “Limit name” column or use the “Filter” search box to find your quota.

Step 2: Customizing the Limits

  • Then, select the quota by clicking on it or checking the check box.
  • After selecting the quota, click on the “Edit Quota” option on the top. 
Edit Quotas Button
Image Source: Self
  • The Quota changes form will appear on the right-hand side of the screen.
  •  Here, you can see the “New Limit” field, where you can set the new value to increase or decrease the quota limit.
Setting New Limits
Image Source: Self
  • Fill in any other additional fields if available and click on the “Done” button. 
  • After that click on the “SUBMIT REQUEST” button.

That’s it! In this way, you can custom values to your Google BigQuery Limits and Quotas.


In this article, you learnt about Google BigQuery Limits and Quotas, why it is an important aspect of a project, and how to configure it to set the custom limits and Quotas as per the business requirements. You also read about the Google BigQuery Limits and Quotas of a few jobs that one should note while working with data. Avoiding the Google BigQuery Limits can lead to failure of running tasks once the quota finishes and managing the right limits will keep the project budget in check.

Visit our Website to Explore Hevo

Companies store valuable data from multiple data sources into Google BigQuery. The manual process to transfer data from source to destination is a tedious task. Hevo Data is a No-code Data Pipeline that can help you transfer data from any data source to desired Google BigQuery. It fully automates the process to load and transform data from 100+ sources to a destination of your choice without writing a single line of code. 

Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of learning about Google BigQuery Limits and Quotas in the comments section below!

No-code Data Pipeline For your Google BigQuery