Choosing the right cloud data warehouse is essential for businesses today. With so many options available, it often comes down to two popular choices: Google BigQuery and Snowflake. To help you decide which platform might be best for your business needs, you need to know the difference between BigQuery and Snowflake.
Overview of Google BigQuery and Snowflake
Google BigQuery is a serverless, fully managed data warehouse that operates within Google Cloud. With BigQuery, businesses can run SQL queries on large datasets quickly and efficiently. Its serverless design lets users focus on querying data without worrying about managing servers or scaling.
Snowflake is a cloud-based data warehouse known for its unique architecture, which separates storage and computing. This separation allows businesses to scale each part independently, adjusting resources easily based on current data needs. Snowflake is also cloud-agnostic, meaning it works across AWS, Azure, and Google Cloud.
Difference Between Snowflake and BigQuery
Below is a breakdown of some of the most important features of Google BigQuery and Snowflake, from architecture and performance to data-sharing capabilities and pricing.
1. Architecture and Scalability
Google BigQuery:
BigQuery is fully serverless, so users don’t need to handle server management. It uses a columnar storage format and Dremel technology to process large datasets at high speed. BigQuery automatically scales based on the workload, which is helpful for businesses with unpredictable data needs.
As a serverless platform, BigQuery adjusts to handle increased data without manual intervention, making it a reliable option for fast-growing companies.
Snowflake:
Snowflake’s unique architecture separates storage from computing, allowing more control over resources. This separation provides flexibility and ensures that businesses can scale resources according to each specific task.
Snowflake lets businesses scale storage and compute separately, which allows for more precise resource management and cost control. For instance, a large query can be run without affecting storage costs, and virtual warehouses can scale as needed.
2. Performance and Speed
BigQuery:
BigQuery is optimized for quick, interactive queries with Dremel technology, which makes it a great choice for real-time analytics. Additionally, BigQuery’s BI Engine speeds up query performance further.
BigQuery’s performance improves by keeping queries concise and reducing data scanned, making it a cost-efficient option for fast results.
Snowflake:
Snowflake uses Micro-Partitioning to store data, which enables fast access even with massive datasets. Its built-in caching system also speeds up repeated queries, often reducing processing time in what’s called a Snowflake time difference—the time saved due to query
caching.
Snowflake manages query optimization in the background, making it easy to get fast results without extensive database knowledge.
3. Data Sharing:
BigQuery Data Sharing:
BigQuery provides multiple ways to share data securely. Users can set up authorized views to control access, enabling secure sharing both within and outside the organization. This is valuable for team collaboration or sharing insights with external partners.
BigQuery’s integration with Google Sheets also simplifies data access for teams who may not use SQL, enhancing accessibility.
Snowflake:
Snowflake offers Secure Data Sharing, allowing businesses to share live data without copying it. This feature is particularly helpful for businesses with multiple external partners or departments that need access to up-to-date information.
Snowflake’s Data Marketplace allows organizations to access shared data securely and collaborate in real-time.
4. Integrations and Compatibility
Google BigQuery:
BigQuery integrates seamlessly with other Google Cloud services like Google Analytics, Google Ads, and Looker, making it a great fit for companies already using the Google Cloud ecosystem. It also supports standard SQL, making it easier for analysts familiar with SQL.
Its compatibility with Google’s data tools allows businesses to centralize data from across Google platforms, simplifying analysis and reporting.
Snowflake:
Snowflake works across multiple cloud providers, supporting AWS, Azure, and Google Cloud. This cloud-agnostic design is ideal for companies with a multi-cloud strategy.
Snowflake also connects well with popular business intelligence tools like Tableau and Power BI, allowing flexible reporting options.
5. Machine Learning Capabilities
Google BigQuery:
BigQuery has BigQuery ML, which lets users build and run machine learning models directly in BigQuery using SQL. This feature is handy for teams who know SQL and want to add machine learning without extra tools.
Snowflake:
Snowflake uses Snowpark to support machine learning, allowing users to build models with programming languages like Python, Java, and Scala. This is ideal for companies needing more control over their machine-learning models.
6. Cost and Pricing Model
BigQuery:
BigQuery uses a pay-as-you-go model, charging based on data processed and stored. This flexible approach is particularly cost-effective for businesses with varying query workloads, as it allows payment only for what’s needed.
Users pay for the volume of data scanned per query, making BigQuery an affordable option for occasional or light workloads.
Snowflake:
Snowflake separates charges for storage and computing, allowing companies to manage each cost based on usage. Snowflake’s compute resources can also be paused, saving costs during off-peak hours.
Snowflake’s separate pricing for computing and storage helps control costs more precisely, which is beneficial for consistent or high-frequency workloads.
Pros and Cons of BigQuery vs Snowflake
BigQuery Pros
- Serverless, with automatic scaling and management
- Budget-friendly, especially for variable query workloads
- Integrates with Google Cloud, ideal for businesses using Google services
- Real-time data analytics capabilities
BigQuery Cons
- Limited flexibility compared to Snowflake’s separation of computing and storage
- Best suited for organizations already within the Google Cloud ecosystem
Snowflake Pros
- Separation of computing and storage for better resource control
- Works across AWS, Azure, and Google Cloud, enabling multi-cloud flexibility
- Secure Data Sharing for real-time collaboration without data duplication
- Caching leads to significant Snowflake time difference for faster-repeated queries
Snowflake Cons
- Higher costs for businesses with frequent query loads
- May require familiarity with managing virtual warehouses for best performance
Which Should You Choose: BigQuery or Snowflake?
Ultimately, the choice between BigQuery and Snowflake depends on the specific needs of your business.
Choose Google BigQuery if:
- Your organization uses other Google Cloud services.
- You need a fully managed, cost-effective data warehouse with serverless scaling.
- You prioritize real-time analytics and fast query speeds for large datasets.
Choose Snowflake if:
- You need a flexible, multi-cloud solution for your data warehousing.
- Your business requires scalable data sharing and real-time collaboration with partners.
- You prefer to manage computing and storage separately to better control costs.
Find the Best Fit for Your Data Needs
Choosing between Google BigQuery and Snowflake depends on your business needs, budget, and cloud setup. BigQuery is a good fit for businesses already using Google Cloud, with easy scaling and cost-effective options, especially for real-time analytics. Snowflake, with its flexible, multi-cloud compatibility and ability to manage computing and storage separately, is ideal for businesses needing more control and collaboration options.