If you have been looking to find an answer to this question: BigQuery vs Snowflake – Which data warehouse to choose, then you have landed at the right place.  

We at Hevo, help our customers bring all their into the data warehouse of their choice without having to write any code. Naturally, many customers who are setting up their data analytics stack come to us seeking advice and recommendations on which warehouse to opt for – Google BigQuery or Snowflake. 

This blog aims to answer this question by providing nuanced differences between Google BigQuery and Snowflake. These inputs, in turn, would help you evaluate which of these cloud data warehouses suit your needs best.

What is Google BigQuery?

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BigQuery is Google Cloud Platform’s enterprise data warehouse for analytics. This technology has been used by Google internally for various analytic tasks for over a decade. It is a good tool for analyzing huge amounts of data quickly to meet your Big Data processing requirements. BigQuery offers exabyte-scale storage and petabyte-scale SQL queries. Data in BigQuery is encrypted, durable, and highly available.

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Why Use BigQuery?

As the business grows, managing the data spread across the gazillion applications used by teams becomes hard. This, in turn, further makes it difficult to analyze the data within these systems to get meaningful insights. Often, precious engineering resources are deployed to set up a centralized data store that hosts all this data and opens the door for BI. 

By using BigQuery, developers can now get back to focusing on essential activities such as building queries to analyze business-critical data. Also, BigQuery’s REST API enables businesses to easily build App Engine-based dashboards and mobile front-ends. Companies can then truly unleash the power of this data and empower all the stakeholders of the organization to derive insights from this. 

Advantages of Google BigQuery

  1. Managed storage: One of the major benefits of BigQuery is its managed storage. BigQuery offers durable and persistent storage for your Data Warehouse and this helps you dramatically reduce your data operations. Tables are stored in an optimized columnar format. Each table is compressed and encrypted. All tables in BigQuery will accept streaming ingestion. Storage on BigQuery is durable and consistent seeing that each table is replicated across multiple data centers. With BigQuery, you also don’t have to worry about data replication and Disaster Recovery because in BigQuery, when you’re running in a regional configuration, your data is in multiple zones within a region, and when you’re running in a multi-region configuration, your data is spread across multiple geographically distinct regions. Therefore, you don’t have to worry about downtime from just a region or a zone going down because you’ll have that durability and consistency across regions.
  2. BigQuery removes resource constraints: Their cloud-powered massively parallel query service can read from ~100k disks using thousands of CPUs in parallel. There is also the separation of storage and compute so you don’t have any scaling bottlenecks.
  3. BigQuery supports a wide variety of formats for data ingestion:
    • Avro
    • Parquet / ORC
    • CSV
    • JSON

When optimizing for load speed in BigQuery, you should prefer using the Avro format in your ETL processes. Avro is a binary row-based format that enables BigQuery to split it and read it with multiple worker nodes.

  1. BigQuery has the capability to leverage nested and repeated fields for:
    • Tightly-coupled or immutable relationships for example:
      1. Sessions with Events
      2. Orders with Order Line items
      3. Infrequently changing data (country, region, date, etc.)
    • Simplifying queries:
      This enables you to store semi-structured data very naturally within BigQuery and then query over it. Oftentimes this is a very different concept for people used to a row-based architecture where they are not used to storing semi-structured data within the data warehouse.
      For Nested Fields: Leverage them when you have tightly coupled or immutable relationships. For example: Let’s take the case where we have an Order, and we have an Order Line Items table. In this case, it is safe to say that Line Items will never really be used without the order table, and these are naturally coupled often immutable relationships. The best practice here is to denormalize that into a single table as opposed to having two tables (Order and Order Line Items). Similarly, you can do that with a Sessions and Events table. So you can have an Event within a Session, as opposed to having separate tables for Sessions and Events.
  2. Built-in ML and GIS for Predictive Analytics:
    BigQuery has strong AI/ML capabilities and supports very broad analytical use cases using:
    • AutoML Tables – For problems that require best-in-class accuracy. This feature is fully automated and it will discover the best model for the problem. It has a code-less graphical UI. 
    • BigQuery ML – For problems that require fast experimentation and development time, for example, Logistic Regression, K-means, Naïve Bayes, etc. It has an SQL interface and also supports AutoML tables as a model type.

What is Snowflake?

Snowflake is a true multi-cloud data platform. They are able to offer their customers High Availability and secure data across 3 Clouds and in multiple Regions. Snowflake is available on AWS, Azure, and the Google Cloud Platform.

With Snowflake, you have a technology solution to build a scalable, highly resilient cloud environment with the agility that your business demands while delivering valuable insights to help your business and customers.

Snowflake’s unique architecture and the flexibility of the cloud have meant that customers can use Snowflake across many use cases and workloads in their business.

Snowflake initially started out as a Data Warehouse but as they’ve been able to manage more and more data types, customers have started to use Snowflake as a SQL Data Lake. You can also read more about the Snowflake query.

Customers are also able to share their data securely across their businesses and also with external data partners using the Snowflake Data Exchange. This greatly enhances their own datasets to then run more advanced and complex Data Analytics for Data science use cases. 

What Makes Snowflake Unique?

  1. Architecture:
    Snowflake has a multi-cluster, shared data architecture, which means similar to BigQuery, they have an architecture that separates their storage and compute layer. This enables them to scale up and down automatically as demand requires without impacting performance. Their architecture features micro-partitioning. This means that they are able to manage semi-structured and structured data. So they can manage JSON, Parque, etc natively within Snowflake and they can do that at an infinite scale.
  2. Delivered as-a-service:
    This makes it incredibly easy to use with near-zero management. Once your data is in Snowflake, they take care of the rest, there is no need to index, prune, etc, allowing customers to focus on the value within their data.

Advantages of Snowflake

  1. Snowflake is a complete ANSI SQL database and data warehouse. That means that they are a very good landing point for Legacy Data Warehouses and Data Platforms that want to move into the cloud. They have very good compatibility for multi-statements transactions and complex joins.
  2. Customers can isolate workloads across the business and allow different areas of the business and different applications to use Snowflake. The platform is, therefore, able to support data scientists, executive reporting, data analysts, and program managers within one platform while maintaining a single source of truth.
  3. Virtually unlimited query concurrency. Using Snowflake, you can scale up as demand requires, but when that demand is not required, Snowflake will automatically scale down. All of your users will be able to access all of the data they need at the same time.
  4. High-performance queries on semi-structured data. Snowflake provides fast access to JSON, AVRO, ORC, and Parquet data thus enabling a more complete view of your business and customers, for deeper, more revealing insights.
  5. Quickly scale up, down, and out elastically, without disruption to running queries. You also incur no compute charges when the system is idle.
  6. Per-second computes pricing and cost-effective compressed data storage pricing.

Google BigQuery vs Snowflake: Factors that Drive the Decision

Deciding on the right data warehouse for your business needs and objectives is a crucial component of your big data strategy. We can see that these two data warehouses are closely stacked together seeing that they both have rich feature sets. From the leading industry standard TPC Benchmark, there is little that separates Snowflake from BigQuery in terms of performance. They both deliver unlimited concurrency and complete elasticity.

The key factors that drive the Google BigQuery vs Snowflake decision are as follows:

1) BigQuery vs Snowflake: Architecture

  • Snowflake: Snowflake architecture is a hybrid system combing both the characteristics of traditional shared-disk and shared-nothing database architectures. It is natively designed for the Cloud but combines an innovative SQL query engine and it comprises three core layers: Database Storage, Query Processes, and Cloud Service. It has a centralized data repository for a single copy of data that can be accessed by all users from all independent Compute Nodes like a shared-disk architecture but also has Nodes in a Cluster where each Node stores portions of the entire data locally.
  • Google BigQuery: Google BigQuery architecture is a Serverless Cloud offering and its main component is the Dremel upon which it is built upon. It possesses the Massively Parallel Processing (MPP) architecture used to query data by reading thousands of rows in a second. Like Amazon Redshift, its nomenclature is the shared-nothing architecture in which data is stored in replicated, distributed units and is being processed in Compute Clusters made up of Nodes. This structure offered by Google BigQuery is flexible whereby different users can transfer their data to a Data Warehouse and start an analysis of the data using standard SQL queries.

2) BigQuery vs Snowflake: Mode of Operation / Performance

  • Snowflake: Snowflake separates its compute power from its storage thereby allowing for concurrent workloads to enable users to run multiple queries at a time. The workload does not affect each other as everything is done separately which results in faster performance.
  • Google BigQuery: Google BigQuery supports partitioning of storage, and compute as separate operations thereby resulting in improved query performance. Google BigQuery delivers fast and large query speeds on data sets with sizes up to a petabyte and data can easily be queried using standard SQL or through Open Database Connectivity (ODBC).

3) BigQuery vs Snowflake: Setup / Maintenance and Server Management

  • Snowflake: Snowflake does not require the setup of Storage and Compute power as they are separated and would be handled by the Cloud provider but the selection of a Cloud service provider is needed. As for maintenance, a low maintenance level will be required as almost everything is done for you including automatic and rapid provisioning of computing resources. For management, Snowflake is regarded as a more Serverless Management System as every operation occurs on the Cloud provider so there will be close to zero management required from the end-users.
  • Google BigQuery: Google BigQuery can be regarded as a Serverless System as most of its operations are handled by Google on the Google Cloud Platform and no sizing is needed in setup as its Storage and Compute Nodes are separated. As for maintenance, a low level of maintenance is needed from the end-user.

4) BigQuery vs Snowflake: Scalability

  • Snowflake: Snowflake gives room for a seamless, non-destructive scaling that occurs both vertically and horizontally. This is made possible with its Multi-cluster Shared Data Architecture and it does not require the input of a database operator as the scaling is done automatically by Snowflake harnessing all the Cloud tools on offer. For this reason, it is preferred by companies with little resources.
  • Google BigQuery: Google BigQuery like Snowflake, separates its Compute and Storage Nodes and therefore, users can decide how to scale the processing and memory resources based on their needs. This gives rise to obtaining high scalability of data which is executed in real-time both vertically and horizontally and up to petabytes of data.

5) BigQuery vs Snowflake: Loading of Data

  • Snowflake: Snowflake supports Extract Load Transform (ELT) and Extract Transform Load (ETL) Data Integration methods in which data can be transformed during or after loading into Snowflake. It captures the raw data and then decides the best way to transform it.
  • Google BigQuery: Google BigQuery also uses the traditional ELT/ETL Batch Data Loading approaches by using standard SQL dialect and it uses Data Streaming to load data row by row using Streaming APIs.

6) BigQuery vs Snowflake: Pricing

  • Snowflake: Snowflake provides on-demand and pre-purchasing pricing plans. Since the usage of Storage and Compute Nodes is different, you can pay for computing on a per-second basis depending on your business and data requirements.
  • Google BigQuery: Google BigQuery platform offers on-demand and flat-rate subscription models where you are charged for the amount of data returned from each query and for the amount of data storage used.

7) BigQuery vs Snowflake: Ease of Use / Data Type Supported

  • Snowflake: Snowflake is intuitive and simple to use though it requires you to have solid SQL and Data Warehouse knowledge. It supports data types such as JSON, XML, Avro, Parquet, etc.
  • Google BigQuery: Google BigQuery is a very user-friendly platform that requires common knowledge of SQL commands, ETL tools and supports JSON, and XML data types.

8) BigQuery vs Snowflake: Backup and Recovery

  • Snowflake: Snowflake uses fail-safe technology instead of backup. Hence, it recovers data that may be lost or damaged due to system failures within a 7-day period.
  • Google BigQuery: Google BigQuery services have data backup and disaster recovery mechanisms that allow users to query point-in-time snapshots from 7 days of data changes.

9) BigQuery vs Snowflake: Security

  • Snowflake: Security on Snowflake is based on your Cloud provider’s features and it provides controlled access management and high-level data security as it is compliant with most Data Protection standards including SOC 1 Type 2, SOC 2 Type 2, PCI DSS, HIPAA, HITRUST, etc.
  • Google BigQuery: Google BigQuery offers Column-level security that allows for the checking of identity and access status, creating security policies as all data is encrypted and in transit by default. As it is part of the Google Cloud environment, it is compliant with security standards like HIPAA, FedRAMP, PCI DSS, ISO/IEC, SOC 1, 2, 3, etc.

10) BigQuery vs Snowflake: Integrations

  • Snowflake: Snowflake provides native connectivity with various Data Integration, Business Intelligence (BI), and Analytical tools.
  • Google BigQuery: Google BigQuery integrates with Google Workspace and Google Cloud Platform systems, a wide array of Data Integration, Business Intelligence (BI), and Analytical tools.

11) BigQuery vs Snowflake: Compute Layer

  • Snowflake: Snowflake is a Proprietary Computing Engine with intelligent predicate pushdown and smart caching that runs on commodity virtual machines (AWS, GCP, or Azure) depending on the cloud. C-Store, MonetDB, and other hybrid columnar systems served as inspiration for this Hybrid Columnar System.
  • Google BigQuery: Google BigQuery is a Distributed Computation Engine. This is powered by Borg, a cybernetic life-form that Google has imprisoned inside strategically located data centers throughout the world.

12) BigQuery vs Snowflake: Storage Layer

  • Snowflake: The storage layer in Snowflake is Aa proprietary columnar format that runs in-memory / SSD / object-store on computing/object storage in your chosen cloud. The data is stored in a hybrid columnar format i.e, PAX with aggressive metadata caching.
  • Google BigQuery: The storage layer in Google BigQUery is proprietary that is stored on the Colossus filesystem and leverages ColumnIO as a storage format. Distributed computing and storage are completely separated.

13) BigQuery vs Snowflake: Compression

  • Snowflake: Snowflake has its own compression layer that is invisible to the user. Unlike BQ, you are not charged for bytes scanned, although this does imply that the query planner can use compression and table statistics to scan less data thus reducing compute cost.
  • Google BigQuery: It uses proprietary compression that is invisible to the user and is handled by the ColumnIO columnar format. BigQuery continuously compresses data under the hood for you, but your queries are still billed as if you were scanning uncompressed bytes.

14) BigQuery vs Snowflake: Support for Third-Party Tools

  • Snowflake: Snowflake has Snowsight which can perform some basic data visualizations.
  • Google BigQuery: Google GCP has its own general access visualization/data modeling software i.e., Data Studio respectively. In December 2020 GCP acquired Dataform which is a remarkable Javascript-based data modeling solution that can compile queries in real-time.

15) BigQuery vs Snowflake: Caching

  • Snowflake: It has cold data storage separated from hot and warm query caches in intermediate storage. The results of queries are cached for 24 hours (on both local and remote “discs”).
  • Google BigQuery: It caches queries and offers an adjustable intermediate cache (BI Engine) for Data Studio querying that provides a hot in-memory cache. Cached inquiries are free of charge.

16) BigQuery vs Snowflake: Use Case

  • Snowflake: Snowflake is best suited for companies that are looking for ways to minimize costs by leveraging on the availability of a Cloud Data Warehouse with nearly unlimited, automatic scaling and creditable performance levels.
  • Google BigQuery: Google BigQuery is best suited for organizations with varying workloads as it allows you to decide how you want to go about querying your data and it is also suited for those interested in Data Mining activities.

Conclusion

Still unsure which path to choose between Google BigQuery vs Snowflake? Explore Amazon Redshift, an equally famous cloud data warehouse. You can also visit the following articles to have information about Snowflake vs Redshift vs BigQuery, Amazon Redshift vs Snowflake, and Google BigQuery vs Amazon Redshift. Google BigQuery and Snowflake are widely used that deliver great performance and help companies store and analyze their business data to generate insights from it.

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Hevo Data with its strong integration with 150+ data sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice such as Snowflake, and Google BigQuery but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

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Share your experience of learning about the comparative study of Google BigQuery vs Snowflake in the comments section below! We would love to hear your thoughts.

Sarad Mohanan
Software Engineer, Hevo Data

With over a decade of experience, Sarad has been instrumental in designing and developing Hevo's fundamental components. His expertise lies in building lean solutions for various software challenges. Sarad is passionate about mentoring fellow engineers and continually exploring new technologies to stay at the forefront of the industry. His dedication and innovative approach have made significant contributions to Hevo's success.

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