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snowflake vs bigquery key differences to know

Snowflake Vs BigQuery Tutorial: 5 Differences Between Data Warehouses

Snowflake vs BigQuery is a long debate, as these are the leaders in data warehouse technology. These cloud-based repositories pull and consolidate data from disparate sources. They typically support a variety of functions, including data analytics, machine learning, artificial intelligence, data mining, and decision support functions.

Although these cloud data warehouses have their own features, pros, and cons, the right choice will depend on the organization’s needs and preferences. In this detailed blog post, we will reveal the main differences between Google BigQuery and Snowflake. We will dive into various aspects, including scalability, performance, pricing, integration, architecture, and more. 

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that provides a scalable and flexible solution for managing and analyzing data. 

The multi-cloud data warehouse runs on the top of Amazon Web Services, Google Cloud Platform, and Microsoft Azure. This means organizations get complete flexibility to choose the best cloud provider or even operate on multiple clouds. 

Snowflake offers numerous benefits, including:

Elasticity: Snowflake’s cloud-native architecture enables automatic scaling, allowing businesses to handle varying workloads efficiently. It dynamically allocates resources, ensuring optimal performance and cost-effectiveness.

Data Sharing: Snowflake enables seamless data sharing between organizations, allowing secure collaboration and real-time insights without data movement.

Ease of Use: Snowflake offers a user-friendly interface and SQL-based querying, making it accessible to data engineers and analysts. Its intuitive features enable faster time-to-insights.

If you want to implement a Snowflake data warehousing solution, check out our comprehensive guide about 5 best practices for Snowflake implementation

What is BigQuery?

BigQuery, developed by Google Cloud, is another powerful data warehousing solution for analyzing massive datasets. It is a fully managed, serverless cloud data warehouse solution that offers numerous benefits, including:

Scalability: BigQuery automatically scales compute resources based on the size of the query, enabling fast and efficient processing of large datasets.

Serverless Architecture: With BigQuery’s serverless model, users can focus on analyzing data without worrying about infrastructure management. It handles tasks such as provisioning, patching, and optimizing performance.

Integration with Google Ecosystem: BigQuery seamlessly integrates with other Google Cloud services, such as Dataflow and Cloud ML Engine, enhancing its capabilities for data processing and machine learning workflows.

Snowflake Vs BigQuery: Data Warehouse Comparison Table 

Before we dive deep into why two popular data warehouse solutions differ, here is a quick comparison between Snowflake and BigQuery.

Parameters  Snowflake BigQuery
Pricing Time-based pricing model Query-based pricing model 
Performance In some instances, Snowflake outperforms BigQuery Robust performance than Snowflake in certain cases
Scalability Offers basic and advanced scalability features Highly scalable with advanced features
Security Extra security features to keep data protected and confidential  Compliant with PCI DSS and HIPAA regulations
Ease of Use Easier compared to BigQuery  Extremely easy to run due to serverless nature
Data Encryption Offers end-to-end encryption services Offers end-to-end encryption services

5 Key Differences Between Snowflake and BigQuery

Both modern cloud data warehouses — Google BigQuery and Snowflake — can be used with ETL or ELT. In this comparison between Snowflake and BigQuery guide, let’s dive deep into how BigQuery and Snowflake differ based on five main aspects. 


Snowflake’s architecture consists of three main layers, including storage, computing, and cloud services. It has a multi-cloud shared data architecture that allows easy separation of storage and compute resources. The main advantage of Snowflake is that it offers a multi-cluster approach to auto-scale based on its demand. 

Google BigQuery’s architecture, on the other hand, is based on Google’s Dremel technology. It allows BigQuery to perform interactive analysis for massive datasets. The virtual data warehouse solution relies mainly on a serverless multi-cluster framework to separate storage and compute layers. 


Snowflake’s architecture excels in handling complex queries and workloads with high concurrency. BigQuery, on the other hand, offers excellent performance for ad-hoc queries and large-scale data analysis.


Snowflake offers robust data-sharing capabilities, allowing organizations to securely collaborate and share data. BigQuery integrates seamlessly with Google Cloud’s ecosystem, providing additional functionalities for data processing and machine learning tasks.


Snowflake charges for data storage and compute usage, providing flexibility but potentially leading to higher costs for specific workloads. BigQuery offers a pricing model based on data processed, which can be cost-effective for organizations with unpredictable workloads.


Snowflake provides integration options with various business intelligence (BI) tools, while BigQuery offers tight integration with Google Cloud’s suite of services, enabling end-to-end data workflows within the Google ecosystem.

Is Snowflake better than BigQuery?

The choice between Snowflake and BigQuery ultimately depends on specific business requirements. Snowflake’s data-sharing capabilities and concurrency handling make it suitable for collaborative environments with complex workloads. 

Alternatively, BigQuery’s serverless model and seamless integration with Google Cloud services make it a compelling choice for organizations already invested in the Google ecosystem.

How is BigQuery different from Snowflake?

One significant difference is their architecture — Snowflake follows a shared-nothing approach, while BigQuery adopts a shared storage model. 

Additionally, their pricing models vary, with Snowflake’s cost based on execution time and BigQuery based on how many virtual CPU slots are consumed. 

Is Snowflake or BigQuery cheaper?

Determining the cost-effectiveness of Snowflake vs BigQuery depends on the specific workload and usage patterns. However, according to PeerSpot, BigQuery is inexpensive as it costs around $20 to $22 monthly to store one terabyte of data. 

Snowflake uses a time-based pricing model that provides flexibility but may result in higher costs for specific workloads. In order to store one terabyte of data, you’ll have to pay nearly $25 per month. 

Since BigQuery’s pricing model is based on data processed, it can be cost-effective, particularly for organizations with unpredictable workloads.

Best Data Warehouse Platform: BigQuery Vs Snowflake

Snowflake and BigQuery are considered the best data warehouse solutions with unique strengths. Snowflake excels in collaborative environments with complex workloads, while BigQuery offers seamless integration within the Google Cloud ecosystem. Understanding the differences between Snowflake and BigQuery will enable you to choose the best data warehouse platform that aligns with your business requirements.

When selecting the right data warehouse platform, consider your organization’s size, workload complexity, collaboration needs, existing cloud ecosystem, and budget. Evaluating these aspects will help you decide whether Snowflake or BigQuery is the ideal choice for your business.

Ready to take your data analytics to the next level? Contact Inferenz experts and let us help you with data analytics and solution consulting services. We will analyze your business needs and help you choose the one between Snowflake vs BigQuery. 

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