Google Cloud SQL vs BigQuery: Key Differences

Nicholas Samuel • Last Modified: August 23rd, 2023

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Every organization is now putting much value on its data. This comes after organizations realized that data has hidden information and insights that facilitate evidence-based decision-making. Due to this, organizations are looking for data storage options that can help them store their data in huge volumes and analyze it to extract insights. 

Google Cloud SQL and BigQuery are some of the options used by users for data storage and analytics. The two products come from Google. Although the two can be used for data storage and analytics, there are significant differences between the two. You may also get stuck in deciding which one to choose for your personal or business use.

In this article, we will be discussing the differences between Cloud SQL vs BigQuery to help you choose the one that fits your needs. 

Table of Contents

  1. What is Cloud SQL?
  2. What is Google BigQuery?
  3. Google Cloud SQL vs BigQuery
  4. Conclusion

What is Cloud SQL?

Cloud SQL is a Google Cloud Platform service. It is a database-as-a-service (DBaaS) or a cloud database service. The data in the database is stored in the cloud, using the cloud service provider’s infrastructure, and access is provided by the Google Cloud Platform Console or via the command line. 

This gives the application owner the permission to use the computing power of the Google Cloud without having to spend their time maintaining their own infrastructure. Cloud SQL also makes it easy for its users to integrate other GCP services with the cloud database service, for example, Kubernetes container management service and virtual machines. Some of the databases supported by Cloud SQL include MySQL 5.6, 5.7, and 8.0, PostgreSQL 9.6, 10, 11, 12, and 13, and SQL Server in the 2017 version.

What is Google BigQuery?

Google BigQuery is a highly scalable, serverless data warehouse with a built-in query engine. It was developed by Google, hence, it uses the processing power of Google’s infrastructure. The query engine can run SQL queries on terabytes of data within seconds, and petabytes within minutes. BigQuery gives you this performance without the need to maintain the infrastructure or rebuild or create indexes. 

BigQuery’s speed and scalability make it suitable for use in processing huge datasets. It also comes with built-in machine learning capabilities that can help you to understand your data better. 

With BigQuery, you can achieve the following:

  • Democratize insights with a scalable and secure platform that comes with machine learning features. 
  • Improve business decision-making from data using a multi-cloud and flexible analytics solution. 
  • Adapt to data of any size, from bytes to petabytes, with no operational overhead. 
  • Run large-scale analytics.  

BigQuery also allows you to create dashboards and reports that you can use to analyze your data and gain meaningful insights from it. 

It is also a powerful tool for real-time data analytics. 

Next, we will be discussing Cloud SQL vs BigQuery. 

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Google Cloud SQL vs BigQuery

Let us discuss Cloud SQL vs BigQuery to know how the two compare in different areas. 

Cloud SQL vs BigQuery: Integrations

Cloud SQL Integrations: When using Cloud SQL, you will definitely need to transfer data to and from other platforms. You will also need to move data from Cloud SQL into the BI tool for analytics. The good news is that Cloud SQL supports integration with a wide variety of tools. Examples of such tools include Google App Maker, Google Cloud Deployment Manager, and dbForge Studio for MySQL. 

BigQuery Integrations: Bigquery also supports integration with different tools to allow you to move data to and from Bigquery. It supports integration with Looker, Data Studio, Chartio, Google Cloud Data Fusion, and others. 

Cloud SQL vs BigQuery: Availability of Applications

Cloud SQL: Cloud SQL doesn’t have any application within itself. It’s up to you to know how to move data from Cloud SQL to the application that you need to use. 

BigQuery: BigQuery comes with individual applications within itself. The applications are made available within the data warehouse and can be accessed from the dropdown menu within the portal. BigQuery also makes it possible for you to access any Google application that you need to use. 

Cloud SQL vs BigQuery: Database Security Options

Cloud SQL Database Security Options: The security of data should be considered when comparing Cloud SQL vs BigQuery. Cloud SQL has more database security options than BigQuery. It also has query editors integrated within itself. The security of data is of great importance and Cloud SQL has done that part very well. 

BigQuery Database Security Options: Although Bigquery comes with database security options, they are not as robust as those offered in Cloud SQL. The reason is that BigQuery was not created based only on database services. 

Cloud SQL vs BigQuery: Monitoring and Metrics 

Cloud SQL Monitoring and Metrics: There are many operations that run in data warehouses, thus, it is good to monitor all the data warehouse activities. It’s not easy to monitor all queries that run in Cloud SQL. Cloud SQL does not have strong monitoring and metrics logging like BigQuery. 

BigQuery Monitoring and Metrics: BigQuery is very strong in monitoring and metrics compared to Cloud SQL. This will let the users know the progress of all the activities. 

Cloud SQL vs BigQuery: Storage Space

Cloud SQL Storage Space: The available storage space in Cloud SQL depends on the data warehouse that is being used since Cloud SQL doesn’t have its own storage. 

BigQuery Storage Space: BigQuery’s storage is as big as that of Google storage. You can use it to store and analyze data of any size. It’s also easy to store and access data in BigQuery as most of our devices are connected to Google. 

Cloud SQL vs BigQuery: Pricing

Cloud SQL Pricing: Pricing is also an important factor to consider when comparing Cloud SQL vs BigQuery. 

Google has two pricing plans for Cloud SQL, Packages and Per Use. The best plan for you will depend on how your database will be used. The packages plan is good if your instance will be used for over 450 hours monthly. It offers the following packages:

Cloud SQL vs BigQuery: Cloud SQL Pricing

The following resources are charged per use:

Cloud SQL vs BigQuery: resources charged per use

BigQuery Pricing: BigQuery pricing is based on usage. Data storage is charged at $0.02 per GB, per month. Streaming inserts attract a cost of $0.01 per 200MB. The following figure shows BigQuery charges for different components:

Cloud SQL vs BigQuery: BigQuery charges for different components

Cloud SQL vs BigQuery: ROI

Cloud SQL ROI: Cloud SQL has a free trial version and doesn’t charge any fee for entry-level. It imposes less burden on users since they can use standard SQL to query for data. However, it requires more effort to set up and get started than BigQuery.  

BigQuery ROI: BigQuery also has a free trial version and doesn’t charge any fee for the entry-level. Bigquery is easier to set up and get started than Cloud SQL. 

Cloud SQL vs BigQuery: Replication Methods

Cloud SQL Replication Methods: When using cloud data storage, you will need to scale the use of data in the database without affecting the performance.

That is what replication helps you to achieve. Cloud SQL supports different types of replication including reading replicas, cross-region read replicas, external read replicas, and Cloud SQL replicas during replication from an external server. However, Cloud SQL doesn’t support replication between two external servers. 

BigQuery Replication Methods: In BigQuery, data replication can be done using Datastream, a serverless data replication service. It synchronizes data across heterogeneous databases, storage systems, and applications with minimal latency. It can be used for data replication for a wide number of use cases such as real-time analytics. 

Cloud SQL vs BigQuery: Programming Language Support

Cloud SQL Programming Language Support: Cloud SQL can be used with applications written in Java, C#, Go, Ruby, Python, Node.js, PHP, and Ruby. 

BigQuery Programming Language Support: BigQuery can be accessed via standard SQL. BigQuery also has client libraries that support programming languages such as Python, C#, Java, Go, PHP, Node.js, and Ruby.

Those are the major differences between Cloud SQL vs BigQuery.


This is what you’ve learned in this article:

  • Cloud SQL is a Cloud Platform Service provided by Google. It allows its users to take advantage of the computing power of the Google Cloud Platform instead of setting up their own infrastructure. Cloud SQL supports specific versions of MySQL, PostgreSQL, and SQL Server.
  • BigQuery is a cloud data warehouse solution provided by Google. It also comes with a built-in query engine. Bigquery has tools for data analytics and creating dashboards and generating reports. 
  • There are a number of differences between Cloud SQL vs BigQuery. Whereas BigQuery comes with applications within itself, Cloud SQL doesn’t come with any applications. 
  • Cloud SQL also has more database security options than BigQuery. The storage space in Cloud SQL depends on the data warehouse being used, while that of Bigquery is equivalent to that of Google cloud storage. 

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