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Google BigQuery vs Snowflake: What are the differences?

## Key Differences between Google BigQuery and Snowflake

Google BigQuery and Snowflake are two popular cloud-based data warehouse platforms known for their scalability and performance. While both offer powerful analytical capabilities, there are key differences that users should consider when choosing between the two solutions.

1. **Architecture and Scalability**: Google BigQuery is fully managed by Google Cloud Platform and uses a shared architecture, allowing for automatic scalability and no need for manual tuning. On the other hand, Snowflake follows a multi-cluster, shared data architecture that provides more control over resource allocation and isolation for workloads.

2. **Concurrency and Performance**: Snowflake offers better support for concurrent workloads with its multi-cluster architecture, allowing for higher performance for complex queries and large datasets. In comparison, Google BigQuery may face limitations in handling high concurrency queries due to its shared architecture.

3. **Pricing Model**: Google BigQuery charges users based on the amount of data processed, which can be a cost-effective solution for organizations with sporadic query needs. Snowflake, on the other hand, follows a more traditional pricing model based on compute resources used, which can be advantageous for users with predictable workloads.

4. **Data Storage and Compression**: Google BigQuery uses columnar storage and automatic data compression techniques to reduce storage costs and improve query performance. In contrast, Snowflake utilizes a hybrid columnar data warehousing approach that combines the benefits of both row and columnar storage for optimized storage and query execution.

5. **Ecosystem and Integration**: Both Google BigQuery and Snowflake offer robust ecosystems and integrations with popular BI tools, data sources, and languages. However, Snowflake provides more native connectors and support for third-party tools, making it easier to integrate with existing workflows and applications.

6. **Security and Compliance**: Snowflake offers advanced security features such as end-to-end encryption, granular access controls, and data governance capabilities. While Google BigQuery also provides strong security measures, Snowflake is often preferred by organizations with strict compliance requirements due to its focus on data protection and governance.

In Summary, Google BigQuery and Snowflake differ in their architecture, scalability, pricing model, performance, ecosystem, and security features, making it essential for organizations to evaluate their specific needs and priorities when choosing a cloud data warehouse solution.
Decisions about Google BigQuery and Snowflake
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Pros of Google BigQuery
Pros of Snowflake
  • 28
    High Performance
  • 25
    Easy to use
  • 21
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 11
    Full table scans in seconds, no indexes needed
  • 11
    Big Data
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 7
    Public and Private Data Sharing
  • 4
  • 4
    Good Performance
  • 4
    User Friendly
  • 3
    Great Documentation
  • 2
  • 1
  • 1
    Usage based billing
  • 1

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Cons of Google BigQuery
Cons of Snowflake
  • 1
    You can't unit test changes in BQ data
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    What is Google BigQuery?

    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

    What is Snowflake?

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

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    Blog Posts

    Aug 28 2019 at 3:10AM


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    What are some alternatives to Google BigQuery and Snowflake?
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Google Analytics
    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives