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Data Analytics, Session Recording, Storage and Data Transfer, Al and Machine Learning

BigQuery vs. Snowflake: Making the Right Choice for Your Data Warehousing Needs

By Nick Sharafinski | January 31, 2024

Data Warehouse Platforms

BigQuery and Snowflake are data warehouse platforms. Both allow organizations to have access to an all-in-one platform that manages data analytics, data transfer, and virtual workloads. As technologies and business practices change, data extraction methods need to be able to keep up, to ensure there are no bottlenecks or errors throughout information management. 

By fully incorporating a data warehouse, organizations can have full control over product SKUs, customer information, and other forms of secure data with ensured future scalability.



Announced by Google in May 2010, BigQuery is a fully managed cloud-based data warehouse that has the ability to analyze petabytes of customer data at a scalable level. BigQuery acts as a Platform as a Service (PaaS) and uses a dialect of Structured Query Language (SQL) to support querying and overall data management. 

BigQurey was made available to the general public in November 2011.


BigQuery Features

Machine Learning

BigQuery has the capability to use GoogleSQL to create and execute machine learning models. Users can pair BigQuery ML functionality by using it in conjunction with one of the following platforms:

  • Google Cloud
  • BigQuery's REST API
  • Integrated Colab Enterprise notebooks in BigQuery
  • The bq command-line tool
  • Other external tools

Data Management

BigQuery allows for the manipulation of dashboard views, tables, and most other functions that allow user customization. Since data management is a more streamlined process, fewer tools are needed, ensuring a more simplified process. Google Storage data can be imported from most standard sources such as:

  • JSON
  • CSV
  • Parquet

Query Capabilities

BigQuery has the ability to run terabytes of queries in a matter of seconds. This data is expressed in a SQL dialect. The results are then returned in a JSON format. Typically, query results have a maximum reply length of up to 128 MB, but the reply length has the ability to support unlimited sizes when enabling the platform's large query results.

Integration With Google Apps Script

When using BigQuery alongside Google Apps Script, users can make their Google Docs fully customizable without having to write any code. 

Flexible Access and Data-Sharing Controls

Access to datasets and analytics in BigQuery can easily be shared with specific groups, locked off from others, or shared with anyone that an organization might desire. 

Cross-cloud Analytical Capabilities

Using BigQuery, dataata can easily be analyzed across any applicable platform for a complete analytical view. Systems can gather and digest data across platforms that include:

  • Google Cloud
  • Amazon Web Services
  • Microsoft Azure

In-Memory analysis service

BigQuery boasts a fully integrated business intelligence engine that allows users to view complex datasets and analyze them at a more granular level. Data can be imported into Data Studio and dissected with sub-second query response times and high concurrency.

Flexible Pricing

Flexible pricing for BigQuery is available, and falls under two main structures: 

  • Compute Pricing - the cost associated with processing various query types, user-defined functions, scripts, and applicable data manipulation language (DML) and data definition language (DDL) statements.
  • Storage Pricing - the cost associated with storing any data that organizations specifically load into BigQuery.



Snowflake Inc. is a cloud computing - based data and analytics company founded in July 2012. Much like BigQuery, Snowflake offers a wide suite of cloud-based data storage and analytics services that allow organizations to manage data using cloud-based hardware and software. 

Snowflake has been running on Amazon S3 since 2014, on Microsoft Azure since 2018 and on the Google Cloud Platform since 2019.


Snowflake Features

Support for Both Standard and Extended SQL Support

Snowflake offers support for most Data Definition Language (DDL), as defined in SQL:1999 including: 

  • Databases (schemas, tables, etc.)
  • Core data types
  • SET operations
  • CAST functions

Snowflake also supports parts of the SQL:2003 analytic extensions, including:

  • Windowing functions
  • Grouping sets

Snowflake Information Schema can also be used for querying both historical warehouse data and object and account metadata.

Customizable Tools and Interfaces

Snowflake includes numerous tools for data management like Snowsight, which has the ability to oversee general management over all accounts and data queries. Virtual warehouses can be easily managed from anywhere, and even resized, suspended or dropped altogether. 

SnowSQL, on the other hand, serves as Snowflake's Python-based command line client. Virtual warehouse management from the GUI or command line, including creating, resizing (with zero downtime), suspending, and dropping warehouses.

Replication and Failover Support

Depending on regional restrictions, Snowflake allows for objects or data to be replicated between multiple Snowflake accounts within the same organization. Failover support can also be configured to ensure automated responses in the face of any emergency. 

Tailored Applications and Connectivity Solutions

Snowflake allows users to build applications that process data, but without the added hassle of moving data to the system that actually runs the app code. Snowflake includes APIs for the following:

  • Java
  • Python
  • Scala 

Snowflake also provides a wide variety of client connectors and drivers, including:

  • Python connector
  • Spark connector
  • Node.js, .NET and PHP PDO drivers
  • JDBC and ODBC client drivers

Data Import, Export and Sharing Capabilities

Snowflake allows for bulk loading and unloading a variety of data sources, including XML, JSON, and Parquet formats. A command line client and web interface allow files to be loaded from either cloud storage or local sources.

Snowflake also includes Snowpipe, which has the ability to load different types of data in micro-batches from either internal or external stages.


BigQuery vs. Snowflake - Choosing a Data Warehouse Solution

So how does an organization choose the right data warehouse solution? When deciding between BigQuery and Snowflake, the choice really comes down to preference and a complete understanding of the needs of the business. BigQuery works seamlessly throughout the Google Cloud Platform, whereas Snowflake makes more use of Amazon and Microsoft platforms. 

Both have flexible pricing options as well. That said, it's important to note that pricing between the two platforms is not the same. Snowflake typically charges for query execution time. BigQuery, on the other hand, specifically charges based on data returned by queries. 

Organizations should therefore understand what the end goal is for using these platforms. When running complex queries within larger datasets, Snowflake may be a more cost-effective option, while BigQuery may be more cost-effective while running more simple queries on small datasets.


Important Similarities Between BigQuery and Snowflake

Reporting Capabilities

Both BigQuery and Snowflake offer unparalleled support in terms of reporting and dashboard views by allowing individual workloads to be isolated for more granular reporting. That said, neither platform includes the performance needed to support interactive or ad hoc queries at scale, which keeps both platforms from being ideal for operational and customer-facing use cases.

One important difference to note is that while Snowflake does not support low latency streaming (below one-minute ingestion levels), BigQuery does. 

Data Quality and Privacy Protections

Both BigQuery and Snowflake are scalable in terms of compliance with government laws and regulations, and include integrated privacy controls, data filters, and Demand Signal Repository (DSR) capabilities. These features ensure organizations always remain in line with ever-changing privacy policies and regulatory restrictions. 


The Decision is Yours

At the end of the day, the argument of BigQuery vs. Snowflake comes down to preference, business needs, and what organizations already have set in place. 

While Snowflake supports more data types than BigQuery and provides encryption for all data at rest, BigQuery works seamlessly across all Google Cloud platforms and provides automatic encryption not only for data at rest, but for data in transit as well. 

"The data warehouse landscape is constantly evolving, and both BigQuery and Snowflake offer compelling options, - says Scott Sanders, CIO at Rackspace Technology. "We embrace a platform-agnostic approach. Our data experts can manage your chosen platform, whether BigQuery or Snowflake, with equal proficiency. Additionally, we offer consultancy services to help you stay ahead of the curve, explore emerging platforms, and optimize your data strategy for the future. -

Of course, BigQuery and Snowflake are just two peaks of the data warehouse iceberg. To learn more about designing data warehouses within BigQuery or finding a solution tailored to your organization's needs, visit the C2C community today.

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