Snowflake vs BigQuery – Comparing Two Popular Cloud Data Warehouses

As organizations lean on the massive volume of data that is being accumulated, for insightful decision-making, the apt choice of a cloud data warehouse is important. Project owners take time to decide on which one to select since it directly impacts the power to transform business through data-driven insights.Cloud-driven data storage and processing is the key to business success now and hence business owners and data architects must choose the best fit for a successful output. When it comes to cloud-based data warehouses, two popular names that suit the bill are Snowflake and Google BigQuery.Often compared with each other, Snowflake vs. BigQuery offers an interesting set of parameters against which they can be evaluated. Through this article, we offer a detailed assessment of the two tech stalwarts, to help you finalize which one is best for your organization. Before comparison, let us delve into the overview and features of each.What is Snowflake?Snowflake is a popular platform that powers the data cloud. You can execute your most critical workloads on top of Snowflake’s multi-cluster shared data architecture in a fully managed platform that capitalizes on the near-infinite resources of the cloud.Snowflake, as a SaaS platform, offers a three-layered architecture having the best of shared-nothing and shared-disk models. The three layers are the data storage layer, the query processing compute layer, and the cloud services layer. Data warehouses in Snowflake can be hosted on AWS or Azure.It makes use of an SQL database engine with a cloud-driven architecture and hence is easy to use and fast to operate. The compute and storage needs are separated and hence there is scalability and flexibility of resources. Fit for modernized data workloads, it is a fully managed technology and has a serverless architecture.

Key Features of Snowflake

Fast and elastic scalability
Security and compliance with standards
Cost-effective with a pay-per-use model
Supports other cloud service providers
Well-designed data manipulation services
Robust query profiler
Delivered as a Service
Complete ANSI SQL database
Unlimited query concurrency

‍What is BigQuery?

BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Powered by Google, BigQuery is a robust cloud data warehouse that is serverless and fully managed. It makes use of a columnar storage format for analytical workloads. It integrates seamlessly with other GCP products.Dremel is an effective query engine that is leveraged for query execution in BigQuery. This interactive query system segregates the complicated queries into nested, smaller components which can then be processed individually and reset for a unified result. BigQuery also uses Colossus for data replication and recovery, Jupiter for distributed computing and storage, and Borg for cluster management.It supports different data formats that are structured, semi-structured, or unstructured like CSV, AVRO, Parquet, JSON, etc. It executes in a multi-tenancy mode with shared resources that are given as slots representing virtual CPU executing SQL.

Key Features of BigQuery

Inbuilt Machine Learning integration
Low latency streaming
No requirement for the provisioning of servers
Scalability to manage large datasets
Easy integration with other GCP products
Geospatial analysis
Access to unsampled raw events and user-level data
No cardinality limits
Multi-cloud functionality

Comparing Snowflake vs. BigQuery – The Similarities and Differences

The Similarities

Secure, scalable, and powerful data warehouses
Columnar storage and Massively Parallel Processing (MPP)
Cost-driven query planning
Decoupled storage and compute resources
Low maintenance burden with higher usability
Supports key-pair authentication, multi-factor authentication (MFA), single sign-on (SSO), and OAuth 2
Compliant with industry-specific regulations like HIPAA
Support for materialized views
End-to-end data encryption
Supports customer-managed encryption keys (CMEK) for better control over data encryption
Compatible with varied third-party tools for transforming, visualizing, and analyzing data
Easy ingestion and replication of data

Quick Comparison: Snowflake vs. BigQuery

	Snowflake	Big Query
Technical Proficiency	Fit for teams with multiple data tools and cloud services experience	Fit for teams well known to Google Cloud Platform and other GCP services
Scalability	8 concurrent queries for each warehouse, autoscaling till 10 warehouses, independent scaling of compute resources	Restricted to 100 concurrent users by default, serverless architecture, automated resource allocation
Architecture	Multi-cluster shared data architecture, decoupled compute and storage architecture permitting ad-hoc resources.	The serverless architecture permits ad-hoc resources without having to bother about computing, simplifying resource management based on query requirements
Performance	High performance because of its architecture and automated query optimization	High performance because of its faster queries, columnar storage, caching styles, and Dremel
User Management and Access Control	Robust access control through role-based access with support for Single Sign On (SSO)	Robust access control through GCP’s identity and access management (IAM) system
Concurrency and Workload Management	Multi-cluster computing and scaling fit in concurrent queries and users	Serverless design allocates resources in an automated way, with more concurrency
Supported Cloud Technologies	Azure, AWS, Google Cloud	Only Google Cloud
Table Level Partitioning	Automatic division of data in micro partitions with cluster keys and pruning	Users with pruning define partitions at the table level at the partition level
Network Security	Restricts virtual private networking to those who have a subscription to a Virtual Private Snowflake edition	Permits all Google Cloud Platform users to use a virtual private network through GCP Virtual Private Cloud Service Controls
Pricing Model	Makes use of a time-based pricing model for computing resources	Makes use of a query-based pricing model for computing resources
Query Performance	High-performance query execution and automated query optimization	Faster query performance through columnar storage and caching methodologies
Warm Cache (SSD)	Yes	No
Data Connectors	Native data connectors with third-party integrations	Smooth integration with other GCP services

Snowflake vs. BigQuery – Which One to Choose?

Overall, both the options – Snowflake and BigQuery have carved a niche for themselves in the data warehousing arena. Both are robust solutions with their pros and cons. As you tend to choose between either of them, you must consider important factors such as organization size, budget estimates, data needs, technical expertise, current infrastructure, and technology stack. These considerations will help you make the ideal decision that suits your company’s needs.Being an official Snowflake professional service provider, we facilitate organizations in leveraging Snowflake’s speed, flexibility, extensibility, and accessibility to thrive in the data-driven world. You can reform your data infrastructure with Ridgeant’s comprehensive suite of Snowflake services.As a proud Snowflake partner, we extend a wide range of services that serve your unique business needs. Right from inception to implementation to optimization, our expert team ensures seamless integration, accessibility, and analytics, resulting in maximum value from the key features of Snowflake’s incredible technology.Whether you’re looking to migrate to the cloud, optimize your data infrastructure, or unlock the true potential of your data, our expert team is here to steer your way. Hire Snowflake engineers from Ridgeant to transform your raw data into richer insight.