Data warehouses have served as valuable tools for organizations for more than three decades. These repositories – now cloud-based – help organizations pull together and consolidate data from disparate sources. They typically support a variety of functions, including artificial intelligence, data mining, data analytics, machine learning and decision support functions.
Data warehouses are fast, flexible and powerful – particularly as organizations look to expand digital transformation and incorporate robotics, IoT, deep integration and API support and other functions.
There are crucial differences between Google BigQuery and Snowflake. This article offers an in-depth comparison of these two leading data warehouse platforms: how they match up, along with some of their key differences.
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BigQuery vs. Snowflake: Feature Comparison
BigQuery: Google’s reputation for providing powerful data frameworks and tools extends to BigQuery. It delivers a fast, highly flexible and scalable data warehousing solution that deftly handles both structured and unstructured data.
This serverless multi-cloud environment is designed to “democratize insights with a secure and scalable platform with built-in machine learning,” according to Google. BigQuery is a multicloud analytics solution that can accommodate a data warehouse ranging from only a few bytes to petabytes. The platform supports predictive modeling and machine learning, multicloud data analysis, interactive data analysis and geospatial analysis, along with numerous other data capabilities.
Snowflake: What makes Snowflake appealing is its focus on flexibility and scalability for huge quantities of data. The platform, which is delivered as a service, can automatically scale up and down without any impact on performance. The multi-cloud shared data architecture handles a vast array of workloads and tasks that revolve around data engineering, data warehousing, data lakes, data science and more.
Snowflake delivers ultra-high resiliency, and it delivers an architecture that supports modern standards, including security and data governance. Organizations can run the platform on AWS, Azure and Google Cloud—or any combination. Snowflake also delivers strong collaboration and data sharing features. It is ideal for modern integrated data applications, and it has strategic alliances and partnerships with Salesforce, Alation, Cognizant, Collibra, Dataiku, Informatica, Qlik, Talend and many others.
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BigQuery vs. Snowflake: Architecture Comparison
BigQuery: The platform relies on a serverless multi-cluster framework that keeps compute and storage layers separate. Google handles all resource provisioning behind the scenes and supports clustering on both partitioned and non-partitioned tables. These tables are durable, persistent, optimized and compressed for power and speed.
This massively parallel environment relies on thousands of CPUs to read data from storage. It supports almost all major data ingestion methods, including Avro, CSV, JSON and Parquet/ORC. One of the big advantages to BigQuery is its auto-replication across global data centers. This greatly minimizes the risk of service interruptions and downtime.
Snowflake: The platform offers a hybrid system that combines traits from traditional shared-disk and shared-noting architectures. It delivers a multi-cluster approach to auto-scale based on demand.
Because Snowflake has a built-in separation layer between storage and compute, it’s extremely fast and flexible. For instance, micro-partitioning accommodates structured, semi-structured and unstructured data, and the platform delivers an extensive set of connectors and drivers, including Spark, Python, .NET and Node.js. It supports most SQL commands, including DDL and DML. It’s possible to isolate data and groups, and even run different applications from a single source of data.
BigQuery vs. Snowflake: Comparing Key Tools
BigQuery: The data platform delivers a wealth of features and integrates with other Google data tools, including Vertex AI and Data Studio. BigQuery ML helps data scientists and data analysts build and use machine learning models through structured and semi-structured data, with SQL. It imports and ingests most major file types using connectors and plugins, including data from SAP, Informatica and Confluent.
BigQuery Omni delivers multicloud analytics and connects seamlessly to AWS and Azure. BigQuery BI Engine delivers analytics on complex databases with sub-second response times. And BigQuery GIS supports geospatial data analysis, with support for most mapping and charting formats. In addition, the platform provides AutoML Tables, a codeless GUI that automates tasks and guides users to the best model, and ML features that support various approaches, including Logistic Regression, K-means and Naïve Bayes. It is ANSI SQL compliant.
Snowflake: The platform handles just about every data science challenge an organization can throw at it. Common workloads include application building, collaboration, cybersecurity, data engineering, data lakes, data science and data warehousing. It is equipped to handle requirements across a wide swath of industries, offering a rich set of tools to handle every aspect of data ingestion, transformation and analytics, including unstructured data. A schema-on-read feature allows data scientists to build pipelines without the need to define a schema ahead of time.
Snowflake supports BI, analytics and machine learning at scale. The ML solution allows users to plug in a tool of choice, with native connectors and robust integrations from a broad ecosystem of partners. The platform also provides powerful tools for building data applications with autoscaling and native support for data structures.
Recent Snowflake enhancements include a tool for ARM customers that makes it easier to leverage and manage the lifecycle of their data in a single location, using a single data set; and a data-driven framework for decision making that delivers applications directly to data, thus eliminating the need to move sensitive data between systems.
A new Snowflake Native Application Framework allows developers to build, monetize, and deploy applications on Snowflake Marketplace. Consumers can securely install and run these applications directly on their data inside Snowflake.
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BigQuery vs. Snowflake: Interface Comparison
BigQuery: As part of Google Cloud, BigQuery offers a cloud console with a graphical user interface (GUI) that’s used to create and manage resources and run SQL queries. The console also offers visibility into various resources, including cloud storage.
Snowflake: The web interface is accessible through Chrome, Firefox, Safari, Opera and Edge browsers (though the company recommends Chrome). The platform delivers a single view into resources and functions. Snowsight, the vendor’s web interface, delivers SQL and other functionality.
BigQuery vs. Snowflake: Comparing Backup and Recovery
Big Query: With data centers located all over the world and auto-replication always-on, there’s virtually no chance of losing data. Google relies on a data backup and recovery framework that lets users query point-in-time snapshots over 7 days of data changes.
Snowflake: The vendor doesn’t operate a dedicated backup system. Instead, it uses a fail-safe technology that recovers system failures for the prior 7 days.
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BigQuery vs. Snowflake: Security and Compliance Comparison
BigQuery: The platform integrates with various Google security and privacy services, including Identity and Access Management (IAM) to handle roles and permissions. In addition, BigQuery offers both column level and row level security with controls over key functions, along with default encryption at rest and in motion. It includes strong governance and compliance features. Part of Google Cloud, it supports HIPAA, FedRAMP, PCI DSS, ISO/IEC, SOC 1, 2, 3, and others.
Snowflake: The company offers comprehensive security features, including private network access to all three clouds it uses, dynamic data masking and end-to-end encryption for data at rest and in motion. Snowflake also provides strong identity and access controls built on OAuth and SAML, along with fine-grained governance. Its Enterprise + tier offers HIPAA support, and it is PCI compliant. In addition, a Virtual Private Snowflake (VPS) option offers customer-dedicated virtual servers. It also supports FedRAMP, DSS, ISO/IEC, SOC 1, 2, 3 and others.
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BigQuery vs. Snowflake: Comparing Support
BigQuery: Google offers basic, standard, enhanced and premium support. Basic is included for all customers; it includes community support and online documentation. Other tiers are available with varying features and prices. Google’s knowledge base is extensive and there is a large and active online community.
Snowflake: The vendor offers professional service in the form of Service Engagements, which pair Snowflake domain experts with an organization’s IT staff. Support comes in two categories: Premier and Priority. Both offer an unlimited number of cases and tickets across AWS, Azure and Google Cloud, but the Priority level prioritizes responses and includes several features that aren’t available in the Premier tier. There’s also an extensive online knowledge base and a large and active online community.
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BigQuery vs. Snowflake: Price Comparison
BigQuery: Google charges for data storage, streaming inserts, and data queries. However, there’s no charge for loading and exporting data. Storage costs $.02 per gigabyte per months, and $.01 per month for long term storage.
Streaming inserts cost $.01 per 200 megabytes. Users have a choice of two data analysis pricing models: on-demand pricing and flat-rate pricing. The former runs $5 per terabyte, with the first terabyte per month free. Flat rate pricing starts at $1,700 per month for a dedicated reservation of 100 slots. Google charges $4 per hour for 100 Flex slots.
Snowflake: The company has a fairly complex pricing model that’s dependent on the platform (AWS, Azure or Google Cloud) and region. For instance, AWS and US West (Oregon) varies across four tiers. The Standard Tier offers a complete SQL data warehouse, always-on encryption, federated authentication and customer-dedicated virtual warehouses at $40 per terabyte per month on-demand storage plus $2 per credit (a unit of resource measure) once an organization has reached their purchased capacity.
The enterprise plan also cost $40 per terabyte per month for on-demand storage plus $3 per credit. It includes numerous other features. A Business Critical Enterprise Plus plan runs $23 per month for capacity storage with $4 cost per credit. It includes other advanced features, including database failover and fallback.
BigQuery vs. Snowflake: Conclusion
Both platforms deliver state-of-the-art data warehousing and science features, and they are both exceptionally powerful, flexible and scalable. Much of the decision depends on what vendors and platforms a business already relies on, and which of these two vendors is a better fit for storage and compute, including pricing.
BigQuery may have a slight edge for data mining and organizations that have variable workloads, while Snowflake has a slight advantage for organizations that require nearly unlimited automatic scaling.
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