Cloud SQL is a database service. BigQuery, on the other hand, is a service that can be used to query massive amounts of data. Cloud SQL takes care of data for operations. It also models relationships and handles transactions.

BigQuery can come in handy when tackling analysis on flattened data. In this article, we will be discussing the differences between Cloud SQL vs BigQuery to help you choose the one that fits your needs. 

Google Cloud SQL vs BigQuery

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

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Cloud SQL vs BigQuery: Integrations

The primary difference between Cloud SQL and BigQuery lies in the 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 is also an important factor to consider when comparing Cloud SQL vs BigQuery. Pricing for Cloud SQL is dependent on your instance type:

  • SQL Server
  • MySQL and PostgreSQL

SQL Server: Cloud SQL for SQL Server consists of the following charges:

  • Storage and Networking Pricing
  • CPU and Memory Pricing
  • Licensing

Storage and Networking; and CPU and Memory pricing would depend on the region where your instance is located. You can choose your region from the dropdown on the pricing table. HA prices get applied for instances that are configured for high availability, also known as regional instances.

Apart from these, SQL Server also has a licensing component. Regional instances would only incur the cost for a single license for the active resource. SQL Server instances get charged a 10 minute minimum for licenses. After 10 minutes, SQL Server licenses are charged in 1 minute increments.

MySQL and PostgreSQL

Cloud SQL for MySQL and PostgreSQL contains the following charges:

  • Storage and Networking Pricing
  • CPU and Memory Pricing
  • Instance Pricing

The pricing requirements for Storage/Networking and CPU/Memory pricing are similar to SQL Server pricing.

Instance pricing is applied only to shared-core instances. It’s charged for every second that the instance is running. Since Cloud SQL uses seconds as the time unit multiplier for usage, each second of usage counts toward a full billable minute.

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 consists of two main components:

  • Storage Pricing: This is the cost to store the data that you load into BigQuery.
  • Compute Pricing: This refers to the cost to process queries, including user-defined functions, SQL queries, scripts, and specific Data Definition Language (DDL) and Data Manipulation Language (DML) statements.

BigQuery would also charge for other operations. This includes BigQuery ML, BigQuery Omni, BI Engine, and streaming reads and writes.

For Compute Pricing, BigQuery provides two options for running queries:

  • Capacity Pricing: Under this pricing model, you’ll be charged for the compute capacity that’s required to run queries. This will be measured in slots (virtual CPUs) over time. Here, you can use the purchase slot commitments which are dedicated capacity that’s always available for your workloads, at a lower price. Or, you can use the BigQuery autoscaler. The pricing happens across three tiers here (known as BigQuery Editions): Standard, Enterprise, and Enterprise Plus.
  • On-Demand Pricing: For on-demand pricing, you’ll be charged for the number of bytes processed by each query. The first 1 TiB of query data processed per month would be free. Post that, you’ll be incurring a cost of $6.25/TiB. A few points to keep in mind:
    • If you cancel a running query job, you could end up bearing the full cost for the query if you let the query run to completion.
    • Clustering and partitioning your tables can reduce the amount of data processed by queries.

For Storage Pricing in BigQuery, you pay for active storage and long-term storage.

  • Long-term storage would include any table or table partition that’s unchanged for 90 consecutive days. In this scenario, the price of storage for that table automatically drops by approximately 50%. There is no difference in durability, performance, or availability between long-term storage and active storage.
  • Active storage includes all table partition or table that has been modified in the past 90 days.   
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.

Cloud SQL vs BigQuery: Scalability

BigQuery Scalability: BigQuery excels in handling large-scale data analytics, offering a serverless architecture that scales compute and storage effortlessly.

Cloud SQL Scalability: Cloud SQL is a managed relational database service optimized for smaller workloads, providing horizontal autoscaling but with limited vertical scaling.

Cloud SQL vs BigQuery: Data Structure

BigQuery Data Structure: BigQuery’s columnar storage is ideal for querying both structured and semi-structured data, adept at managing complex data models with nested and repeated fields.

Cloud SQL Data Strucutre: Cloud SQL relies on traditional SQL databases like MySQL and PostgreSQL, which are suitable for structured data with set schemas.

Cloud SQL vs BigQuery: Query Language

BigQuery Query Language: BigQuery employs BigQuery SQL, an extension of standard SQL is tailored for its advanced analytics capabilities and efficient execution of complex queries. 

CloudSQL Query Language: Cloud SQL supports standard SQL, aligning with the querying needs of traditional SQL databases.

Cloud SQL vs BigQuery: Use Cases

BigQuery Query Language: BigQuery serves organizations analyzing vast datasets for business intelligence, data warehousing, and machine learning, facilitating ad hoc analytics and data exploration. CloudSQL Query Language: Cloud SQL is geared towards traditional database applications requiring structured data and transactional processing, such as web applications, content management systems, and e-commerce platforms.

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. 

In summary, BigQuery is perfect for storing and querying large volumes of data quickly. On the other hand, Google Cloud SQL is a relational database that’s more geared towards transactional purposes.

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Nicholas Samuel
Technical Content Writer, Hevo Data

Nicholas Samuel is a technical writing specialist with a passion for data, having more than 14+ years of experience in the field. With his skills in data analysis, data visualization, and business intelligence, he has delivered over 200 blogs. In his early years as a systems software developer at Airtel Kenya, he developed applications, using Java, Android platform, and web applications with PHP. He also performed Oracle database backups, recovery operations, and performance tuning. Nicholas was also involved in projects that demanded in-depth knowledge of Unix system administration, specifically with HP-UX servers. Through his writing, he intends to share the hands-on experience he gained to make the lives of data practitioners better.

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