Snowflake vs. BigQuery: 2023 Comparison Guide (Showdown)

Snowflake and BigQuery are cloud-based data warehouse solutions that remove the burdens of maintaining and scaling on-premise data warehouses. They offer you the advantages of the cloud-based model and a fully managed, cost-effective solution.

But you cannot simply substitute one for another. Starting from architecture to pricing models to security measures, and performance considerations, these two popular SaaS data warehouse solutions differ by a wide margin.

You will have to evaluate their specific capabilities, match them against your requirements, and see which fits your needs the best. Let us help you make that decision.

Criteria	BigQuery	Snowflake
Data warehousing	Serverless architecture, fully managed cloud data warehouse solution	Cloud-based, multi-cluster, shared data architecture
Query Performance	Uses distributed query processing, highly scalable	Employs advanced high-performance techniques, such as micro partition storage, decoupled data storage, and compute architecture
Integration Ecosystem	Native integration with Google Cloud Platform	Compatible with the most popular cloud-based and on-premise solutions. Works with all leading data integration platforms such as Talend, Fivetran, Matillion, and more.
Data ingestion	Supports both batch and streaming types of data ingestion	Supports both batch and real-time data ingestion
Data formats	Supports a wide range of data formats, including semi-structured and unstructured formats such as CSV, JSON, AVRO, parquet, and data store backups	Snowflake can work with structured and semi-structured data formats such as JSON, Avro, ORC, Parquet, or XML.
Flexibility	Auto scalability depending on the workload	Decoupled cloud storage and compute functions allow for flexible data operations and resource management
Machine learning capabilities	Easy to work with BigQuery ML to build and deploy ML models	Provides tight integration with Spark, R, Qubole, and Python, allowing you to build and deploy ML and AI-driven applications
Security and Compliance	Advanced security features and compliance with data standards are provided	Compliant with SOC 2 Type, PCI IDSS and HIPAA. Also provides robust security features.
Support and Documentation	Well setup documentation and support options are available	Provides good documentation and technical support.
Community and User base	A good number of growing user base and community support is available	You can use the snowflake data heroes community to get access to a global community network with various chapters.
Pricing	Pay-as-you-go model, where you pay for only as many resources as you use	Usage-based pricing is where you only pay for the storage and computing power you use, independently of each other.

BigQuery Overview & Features

BigQuery is Google's own answer to the growing need for cloud-based data warehousing solutions. It is built on top of the GCP ecosystem and can thus be readily used in tandem with other GCP applications.

Serverless architecture

With its serverless architecture, Google BigQuery can scale up and down as per the workload.

BigQuery BI engine

This is an in-memory analytical service that BigQuery provides. BI engine allows you to run complex data analyses on a large amount of data with a quick response time and high concurrency.

Real-time analytics

BigQuery allows for high-speed streaming inserting via APIs allowing you to run efficient real-time analytics.

High availability

You get more control over partitioning your data and are not charged extra for the replicated storage provided.

BigQuery Pros

The major benefit of using BigQuery for ELT is that Google manages it. Thus, you get fully managed infrastructure support that is quite literally scalable to any limit you want. Other pros would be:

Low cost for storage that is comparatively the most cost-efficient in the market
With the BigQuery Omni feature, you can query data from other platforms as well, including Microsoft Azure, AWS, and, of course, GCP
Great performance, even when working with huge data sets
Support for AI and ML-based analytics

BigQuery Cons

The cons with BigQuery are mostly technical such that if you follow the best practices and guidelines, most of the issues faced can be minimized.

Unoptimized queries can return redundant data, which can be costly
Best suitable for flat tables and may need extra effort when working with enterprise data models.
While the GCP ecosystem is comprehensive, it lacks proper support for integrating tools outside of this environment.

Snowflake Overview & Features

Snowflake is a cloud-based data platform specifically designed for use in cloud environments only. It provides a fast, easy-to-use, and flexible data warehouse solution that can be integrated into all major cloud platforms, such as AWS, Azure, and Google Cloud.

Independent storage and compute

One of the flagship features of Snowflake is its decoupling of storage and computing functions. This allows users to have more flexibility in the resources they use.

Snowpipe

This data ingestion service from Snowflake allows for automatic data ingestion as soon as big data is made available into virtual warehouses and data lakes, thus allowing uninterrupted data loading into the tables.

Business intelligence (BI) and analytics

Snowflake is compatible with popular BI tools like Power BI, tableau, and more and can easily integrate with several analytical applications.

Support for ML

Snowflake is built to support ML programming and integrates directly with ML libraries such as TensorFlow and PyTorch.

Snowflake Pros

Snowflake ELT provides excellent performance and scalability, with several benchmark tests showing it to be of superior performance. Snowflake can deliver high performance with the help of advanced techniques of data caching and micro partitions.
Snowflake has a low learning curve and many user-friendly options like a web UI, a Python-based command line interface called SnowSQL, and more. It is easy to use and can be used without having any coding experience as well.
It requires zero management from the user as it handles all the management and maintenance tasks.
Provides a rich set of connectors to integrate a wide range of applications and platforms.

Snowflake Cons

Built solely for the cloud, Snowflake has very limited options when it comes to handling on-premise storage.
While the pricing makes sense as a pay-as-you-go model, it may go up a lot higher based on your usage when compared to other similar services.
The community for Snowflake is still nascent and growing. It may not have the same level of maturity as you could find in other data warehouse solutions.

BigQuery vs. Snowflake: Capabilities Comparison

Architecture

While both BigQuery and Snowflake are scalable architectures, their implementation differs.

Snowflake provides a combination of shared disk and shared nothing architecture. This three-layered schema consists of a:

Centralized storage layer,
A multi-cluster compute layer and
Cloud services layer

The design is a lot simpler compared to the serverless architecture used by BigQuery.

BigQuery has a serverless architecture running on top of a query engine called Dremel.

This supports:

Colossus- distributed file system
Jupiter - networking infrastructure
Borg-cluster management system

Snowflake Pricing

While the pay-as-you-go model based on compute costs might sound similar at the outset, how your payment is calculated differs greatly between these two platforms. There is no flat-rate pricing, though.

Snowflake calculates you compute resource usage per hour, where each credit can be billed anywhere between $2.5 and $5. You can learn more about the Snowflake pricing here.

BigQuery calculates your payment based on the CPU slots that you use. It offers both on-demand and flat pricing. Flat pricing rates start at $2,000 per month for 100 slots, and on-demand pricing can be priced at $5 per TB of data. You can learn more about the BigQuery pricing here.

Query Performance

As mentioned earlier, Snowflake has often emerged as the victor regarding query performance. Based on several benchmark tests and the results from the research firm GIGAOM, it has been observed that Snowflake has better performance than BigQuery. But the difference is minimal and will not have any substantial difference for regular business requirements.

Scalability

The main differentiating factor between these two platforms regarding scalability is the ability to scale for larger volumes and higher concurrency. Snowflake allows for better optimization and scaling in both cases with its decoupled storage and compute functions.

BigQuery is also good at scaling up large data volumes (terabytes and petabytes), but BigQuery completely handles the slot allocation. It provides support for up to 100 concurrent users by default.

Security

BigQuery and Snowflake provide good security features from advanced authentication such as MFA, SSO, and access control mechanisms. Both platforms also provide virtual private networks for enforcing network security.

Cloud platform support

BigQuery is built for the GCP ecosystem and only supports the Google Cloud platform. Snowflake has a wider range, and it is basically cloud agnostic. It can support all popular cloud platforms like Amazon AWS, Azure, and GCP.

ETL Tools for BigQuery and Snowflake

1. Portable

Portable is a complete data integration tool that works well with both BigQuery and Snowflake.
It comes with more than 500+ fully managed data connectors.
It is known for providing custom connector development with fast turnaround times.
Portable also provides quick tech support and hands-on guidance with setting up your data pipelines for ELT and ETL processes.
You can get support for all popular data warehouse solutions and test the waters with its free version before moving to its fixed flat-rate payment models.

2. Fivetran

Fivetran is another that works well with both BigQuery and Snowflake.
It has fast data replication capabilities and requires zero maintenance.
Using Fivetran is as easy as it can get. It is a low-code platform with several automation features that make it almost a plug-and-play tool that anyone can easily adapt to their data engineering and data analytics team.
It is available in free and paid versions, with the enterprise plan giving you custom feature options.

3. Talend

Talend has a long track record of being one of the market's most efficient data integration tools.
It supports ETL data pipelines, profiling, faster deployment, and more.
It has a good set of features that will come in handy for managing all your data operations, including monitoring, real-time analytics, auditing, reporting, and more.
One big advantage of working with Talend is the huge community it has.
The Talend Open Studio is open source and has a wide range of data tools. It is widely preferred by companies looking for a cost-effective option for their batch-based data migration efforts.
It is also available under a pay-by-usage pricing model.

4. Informatica

Informatica is a well-known ETL tool offering an end-to-end data integration solution.
It is easily scalable and supports all data operations like transformation, processing different formats, data migration, visualization, and modeling.
It supports all data lifecycle workflows - extraction, migration, and validation.
This tool perfectly suits the need for enterprises of any size to work with legacy data sources or on-premise systems.
Informatica follows an IPU pricing model where you pay for as much IPU consumption or capacity as you make use of.

BigQuery vs. Snowflake: The Bottom Line

Snowflake and BigQuery go beyond simple data analysis tasks and can support complex data operations on huge volumes of data. But each has limitations that can make them a lesser choice for a particular use case.

For instance, Snowflake does not support streaming or low latency ingestion and thus may not be suitable for consumer-facing apps with low-level performance.
While BigQuery supports low latency streaming, it loses out on the performance required to support ad hoc queries at a high level.
Besides performance considerations, there are other factors to consider, like the existing infrastructure support, budget constraints, visualization requirements, learning curve, adapting to third-party apps used, and more.

You should be able to evaluate your particular business requirements and see if the data warehouse you have chosen will be able to satisfy those performance and functional requirements.