Snowflake vs. BigQuery: What are the differences & which should you choose? 

As more companies trade their on-prem infrastructures for cloud data warehouses, two of the top contenders are Snowflake vs. BigQuery. Both platforms are cost-effective and scalable, but differ significantly in their architecture, pricing models, and overall performance.

If you’re looking to see which cloud data platform is best for your needs, this comprehensive guide is a must-have for making an informed, strategic choice. 

How is Snowflake’s architecture structured?

Snowflake separates its architecture into three distinct layers: storage, compute, and services. Keeping these layers separate but interconnected, gives the platform a degree of scalability and flexibility that its competitors lack. 

Storage layer

Snowflake’s storage layer stores all data in a centralized repository. You have the option of using any of the “Big Three” cloud object storage platforms: Amazon S3, Azure Blob Storage, or Google Cloud Storage. 

With Snowflake cloud storage, data is divided into immutable micropartitions and compressed in a columnar format. Because the player automatically compresses data, there’s little to no need for manual maintenance. 

Compute layer

Snowflake’s compute layer consists of its virtual warehouses, which are independent clusters of compute resources that execute queries and data manipulation tasks. These warehouses are stateless, meaning they can be started, stopped, resized, and cloned without impacting data. 

Additionally, because each warehouse operates in isolation, concurrent workloads can run without contending for resources. Snowflake also offers auto-scaling and auto-suspension features to dynamically adjust resources based on demand and improve cost efficiency. 

Services layer

Finally, Snowflake’s service layer orchestrates operations across Snowflake. These operations include metadata management, security enforcement, authentication, infrastructure management, query parsing and optimization, and access control. 

Other highlights of Snowflake’s architecture

• Hybrid design that combines centralized storage with distributed compute architectures
• Storage and compute layers scale independently to meet varying workload demands
• Multi-cluster compute for workload isolation
• Time Travel to access historical data
• Intelligent caching and partition pruning that enable queries to reduce query latency

How is BigQuery’s architecture structured?

Google BigQuery’s architecture consists of multiple components: distributed storage, compute engine, and high-speed network. This approach to cloud infrastructure enables BigQuery to handle large datasets while also remaining flexible for diverse workloads. 

Distributed storage

BigQuery uses Google’s Colossus file system, a highly fault-tolerant, available, and durable system. Colossus stores data in a columnar format, enabling fast analytical queries. 

In terms of data organization, Colossus breaks data into datasets that hold individual tables. Tables then have a structured schema that defines columns and data types, and support nested and repeated fields. This enables a straightforward and logical approach to organizing complex data structures.

Additionally, BigQuery supports partitioning and clustering to reduce data scanned and, as a result, optimize query performance. 

Compute engine

BigQuery processes SQL queries using Dremel, a massively parallel query engine that uses a tree architecture: 

• A root server that receives the query from the client and routes it to mixers
• Mixers aggregate intermediate results from leaf nodes
• Leaf nodes read the data, perform initial computations, and send the result to mixers
• Slots are the smallest computational unit, combining CPU, memory, and networking resources to execute tasks

Networking and orchestration

BigQuery’s compute engine runs on the Jupiter Network, a high-speed internal network that connects the compute and storage layers. Additionally, the Borg Schedule orchestration layer manages Dremel’s query execution. 

Both of these components are critical to ensuring efficient resource allocation and communication among components. They also function to keep latency as low as possible when carrying out these functions. 

Other highlights of BigQuery’s architecture

• Columnar storage that scans only the columns necessary to carry out the workload
• Serverless model that doesn’t require users to provision or manage resources
• Seamless integration with the Google ecosystem, including Cloud Storage and Bigtable

Snowflake pricing

Snowflake’s pricing model is based on usage and consists of three components: 

• Storage costs for files staged for bulk loading/unloading, database tables, historical data, fail-safe, clones, and more
• Compute costs incurred when you consume Snowflake credits by performing queries, loading data, or conducting other DML operations. For example, running a query on a virtual warehouse consumes Snowflake credits. The larger the warehouse, the more credits the warehouse consumes.
• Data transfer costs are incurred anytime you transfer data across regions cloud providers

There are other factors that impact how much Snowflake bills you, including your Snowflake Edition (Standard, Enterprise, Business Critical, or Virtual Private Snowflake) or whether you pre-pay for credits (Capacity) or do a true pay-as-you-go (On-Demand).

Snowflake also has a fully managed container offering, Snowpark Container Services (SPCS), which runs containerized workloads directly within Snowflake. Additionally, Snowflake’s AI services—including document processing, text completion, language translation, extract answer, sentiment analysis, and more—are priced and billed separately from the typical services listed above. 

BigQuery pricing

BigQuery offers two main pricing models for compute resources: 

• On-Demand pricing, which charges approximately $5 per TB of data processed (the first 1 TB is free)
• Flat-rate pricing, where you purchase slots (i.e. virtual CPUs) for a fixed fee. These slots are available in three editions: Standard, Enterprise, and Enterprise Plus, each with their own distinct features and benefits. You’re charged regardless of the number of bytes your queries scan. 
• Flex Slots enable you to buy BigQuery slots for a short amount of time, starting at 60-second intervals. Flex Slots are great to help scale up and down while keeping some degree of predictability over your costs. 

BigQuery also charges for data storage. Active storage costs approximately $0.02 per GB per month, while long-term storage is half that amount. If you use BigQuery ML operations or BigQuery Omni multi-cloud analytics, each of those tools has its own pricing structure. 

If you want more info on how BigQuery’s pricing approach works, check out this comprehensive article.

Key factors when choosing Snowflake vs. BigQuery

So when it comes to choosing between the two platforms, it all depends on your data architecture needs, priorities, and the resources you have available to manage the cloud platform. 

We’ve already touched on architecture and pricing. Here are some other core factors that can help you choose between the two. 

1. Performance

In an often quoted TPC-H benchmark report, Snowflake outperforms BigQuery by about 10%. While that can be a helpful topline figure, it doesn’t take into account variance in workloads and that, in some situations BigQuery may come out ahead.

For example, Snowflake tends to perform better when executing simpler queries, while BigQuery is faster with more complex analyses. 

2. Scalability

Snowflake offers fine-grained control when it comes to scaling storage and compute resources, with each layer functioning independently of the other. On the other hand, BigQuery scales automatically with its serverless model. 

If control over scale and cost efficiency is a top priority for you, Snowflake is a better choice. If, on the other hand, you want to hand off scalability to a machine, BigQuery has better native functionality.

There’s also a third option that gives you the best of both worlds: use Keebo to autonomously scale Snowflake while maintaining full control over those operations. Set up a demo to see our platform in action. 

3. Ease of use

Both Snowflake and BigQuery are easy-to-use, fully managed platforms that require no access to the underlying cloud layer. However, according to some users, Snowflake is a little bit easier to onboard than BigQuery.

The biggest advantage Snowflake has over BigQuery, however, is that the former integrates seamlessly with all of the Big Three cloud providers, while the latter works best with Google Cloud tools. 

4. Data sharing

Of the two platforms reviewed here, only Snowflake offers live data sharing across accounts without data duplication. BigQuery, unfortunately, works only with Google Cloud. 

Snowflake vs. BigQuery: which platform should you choose? 

Snowflake vs. BigQuery: as we’ve just laid out, there are pros and cons to each platform. However, in our view, Snowflake comes out slightly ahead—the platform’s easier to use, it has a slight performance edge, and its data sharing capabilities are far more expansive. 

At the same time, it’s easier to optimize Snowflake costs and performance, at least when you have the right tools. Keebo autonomously adjusts warehouse sizes, auto-suspends, query routing, and other key activities that help to keep costs low without compromising performance. 

To get a tour of Keebo’s unified, AI-powered Snowflake optimization platform, set up a demo with our team today.