BigQuery vs Snowflake: Key Differences and When to Use Each

Both BigQuery and Snowflake are cloud data warehouses designed for large-scale analytics, but BigQuery is a fully managed serverless service on Google Cloud, while Snowflake runs on multiple clouds with a unique architecture separating storage and compute. BigQuery charges mainly for data scanned, and Snowflake charges separately for storage and compute usage.

Quick Comparison

This table summarizes the main differences between BigQuery and Snowflake across key factors.

Factor	BigQuery	Snowflake
Cloud Platform	Google Cloud Platform only	Multi-cloud: AWS, Azure, GCP
Architecture	Serverless, decoupled storage and compute	Decoupled storage and compute with virtual warehouses
Pricing Model	Pay per data scanned (query), storage separate	Pay separately for storage and compute (per second)
Scaling	Automatic scaling with no user management	User controls compute clusters, can scale up/down manually or auto-scale
Data Sharing	Supports authorized views and external tables	Built-in secure data sharing across accounts
SQL Compatibility	Standard SQL with some Google extensions	ANSI SQL compliant with extensions

Key Differences

BigQuery is a fully managed, serverless data warehouse that abstracts infrastructure management. It automatically handles scaling and resource allocation, so users focus only on querying data. It charges mainly based on the amount of data scanned by queries, which can be cost-effective for infrequent or exploratory queries.

Snowflake uses a unique multi-cluster shared data architecture that separates storage from compute. This allows users to create multiple virtual warehouses that can scale independently, providing more control over performance and concurrency. Snowflake charges separately for storage and compute, with compute billed per second, which can be more predictable for steady workloads.

BigQuery is tightly integrated with Google Cloud services and supports serverless machine learning and geospatial analytics. Snowflake supports multiple cloud providers, making it flexible for multi-cloud strategies, and offers advanced data sharing features that simplify collaboration across organizations.

Code Comparison

Here is an example of a simple SQL query to count rows in a table in BigQuery.

sql

SELECT COUNT(*) AS total_rows FROM `project.dataset.table`;

Output

total_rows
123456

Snowflake Equivalent

The equivalent SQL query in Snowflake to count rows in a table looks very similar.

sql

SELECT COUNT(*) AS total_rows FROM database.schema.table;

Output

TOTAL_ROWS
123456

When to Use Which

Choose BigQuery when you want a fully managed, serverless experience tightly integrated with Google Cloud, especially if you have variable workloads or want to avoid managing clusters. It is ideal for fast, large-scale analytics with pay-per-query pricing.

Choose Snowflake if you need multi-cloud flexibility, want fine control over compute resources, or require advanced data sharing capabilities. It suits organizations with steady workloads that benefit from separate storage and compute billing and want to optimize concurrency and performance.

Key Takeaways

BigQuery is serverless and auto-scales, charging mainly for data scanned in queries.

Snowflake separates storage and compute, allowing flexible scaling and multi-cloud use.

SQL syntax is similar, but pricing and architecture differ significantly.

Use BigQuery for Google Cloud-centric, variable workloads and pay-per-query pricing.

Use Snowflake for multi-cloud needs, steady workloads, and advanced data sharing.