In this short guide, we’ll walk you through the basics of Google BigQuery and Cloud SQL, the reasons to choose each solution over the other and provide a sample architecture for each. Whether you’re new to learning cloud technologies or trying to understand which data storage or database solution is the right fit for your next cloud project, by the end of this guide, you should at least have a high-level understanding of the optimal choice for your next application or data project.
We’ll quickly introduce the basic concepts of BigQuery and Cloud SQL to ensure a common understanding of each offering. Each solution would require extensive coverage to fully understand the full potential of capabilities and use cases. But this guide will give you the confidence to understand where one solution is a better fit based on the use case or requirements.
If you’d like to skip to a specific subtopic, click on any linked sections in our Table of Contents.
Table of contents
What is Google BigQuery?
BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery’s serverless architecture lets you use SQL queries to answer your organization’s biggest questions with zero infrastructure management. BigQuery’s scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes.
Google has invested billions of dollars in its global cloud infrastructure. BigQuery is an example of a solution that directly benefits from the scale, speed, and elasticity of Google’s investments in Google Cloud.
One of BigQuery’s greatest strengths is its native machine learning capabilities, BigQuery ML. BigQuery ML lets you use SQL in BigQuery to perform machine learning and predictive analytics. Whether you’re collecting data on customers or weather patterns, BigQuery ML can perform trend, forecast, and affinity analysis in the same solution and interface.
Suppose you use other Google first-party solutions, such as Google Analytics 4, Google Ads, Google Workspace, or even YouTube. In that case, you can export data directly to BigQuery for further analysis and building deep correlations across customers or users of your applications.
Example Use Cases for BigQuery
- Migrating data warehouses to BigQuery
- Visualizing BigQuery data in a Jupyter notebook
- Incorporating Google Earth Engine geospatial data into BigQuery
- Performing AI tasks in BigQuery ML using large language models (LLMs) and Cloud AI APIs
- Querying data stored in BigQuery using data analytics and business intelligence platforms such as Looker, Power BI, or Tableau
- If you want to use Google Cloud Vertex AI for Generative AI solutions, you’ll want to use BigQuery for your MLOps.
What is Cloud SQL?
Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server. This frees you from database administration tasks so that you have more time to manage your data.
Each Cloud SQL instance is powered by a virtual machine (VM) running on a host Google Cloud server. Each VM operates the database program, such as MySQL Server, PostgreSQL, or SQL Server, and service agents that provide supporting services, such as logging and monitoring. The high availability option also provides a standby VM in another zone with a configuration identical to the primary VM.
When customers want to migrate on-premises MySQL databases to the cloud, most will start with Cloud SQL as it is a familiar, low-effort migration path versus BigQuery.
Reasons to Choose BigQuery vs. Cloud SQL
- Analyzing Petabytes of Data: BigQuery is designed to handle petabytes of data in scale. If you’re working with datasets that are too large or complex for traditional relational databases, BigQuery provides the power and scalability you need.
- Supports Unstructured, Semi-structured, and Structured Data: BigQuery can store and query data in JSON, Avro, and Parquet formats. This makes it ideal for handling data from IoT devices, logs, social media feeds, and other sources that don’t fit neatly into traditional tables.
- Ad-Hoc Analysis and Exploration: BigQuery excels at complex analytical queries over large datasets. Data analysts and scientists can explore massive volumes of information using SQL-like syntax without predefining schemas or indexes. If you know SQL, you know how to use BigQuery to query data.
- Speed: BigQuery is lightning-fast. It can process billions of rows in seconds, enabling you to gain insights from your data in near real-time. Queries that can take hours or even days can happen in a fraction of the time on BigQuery due to its scale and elasticity.
- Serverless & Cost-Effective: BigQuery is entirely serverless – you don’t need to worry about managing infrastructure or provisioning instances. Its pay-per-query pricing model makes it cost-effective for analyzing large datasets, especially if query patterns are infrequent.
- Integration with Other Google Cloud Services: BigQuery seamlessly integrates with Google Cloud’s machine learning (ML), data visualization, and data processing tools, allowing you to build robust end-to-end data pipelines.
Reasons to choose Cloud SQL vs. BigQuery
- Transactional Workloads: Cloud SQL is built on familiar relational database engines (MySQL, PostgreSQL, SQL Server). Cloud SQL is the more natural choice if your application needs to perform frequent updates, inserts, and deletes on structured data. It excels at tasks like online transaction processing (OLTP).
- Strict Data Consistency: Cloud SQL inherently enforces ACID (Atomicity, Consistency, Isolation, Durability) properties. This is vital in applications where immediate data consistency across transactions is a hard requirement (e.g., financial systems, order processing).
- Relational Modeling: The traditional relational model and SQL syntax in Cloud SQL are advantageous if your data has well-defined relationships and requires complex joins across tables.
- Simpler Workloads: Cloud SQL often provides a more straightforward and manageable solution for applications that don’t deal with massive data volumes or excessively complex queries.
- Existing Relational Tools/Skills: If your team already has experience working with relational database systems, leveraging those skills with Cloud SQL offers a smoother transition.
Key Differences between Google Cloud SQL and BigQuery
| Characteristic | Cloud SQL | BigQuery |
|---|---|---|
| Type | Managed Relational Database (MySQL, PostgreSQL, SQL Server) | Serverless Columnar Data Warehouse |
| Ideal Use Cases | OLTP, structured data, frequent transactions, apps needing strong consistency | OLAP, analysis of large datasets, complex ad-hoc queries |
| Data Structure | Tables with rows and columns; Supports structured data | Column-oriented tables. Supports structured, semi-structured, and unstructured data |
| Scaling | Horizontal and limited vertical scaling | Serverless, scales seamlessly to handle massive datasets |
| Query Language | Standard SQL | SQL-like, with some extensions |
| Cost | Can be more expensive for large-scale analytics | Pay-per-query model, cost-effective for large volumes |
The Bottom Line
Choose Google Cloud SQL if your priorities are strong transactional capabilities, familiarity with relational databases, and clearly structured data. Choose BigQuery if you need to quickly analyze massive datasets (large terabytes or petabytes), handle complex analytical queries, and desire a serverless, highly scalable solution.
