Database and Data Warehousing technology is evolving rapidly and new innovations in this field are becoming quite common. Nowadays, many tools help you connect different Data Warehouses with each other so that you can use the advantages of both technologies. One such type of process is connecting Snowflake to BigQuery.

Both Snowflake and BigQuery are some of the top Data Warehouses used by companies worldwide. The cloud has become the most popular way of data storage for companies. The reason is that it comes with several advantages compared to the on-premise storage options. When storing data in the cloud, there is no hardware to select, install, configure, and manage. Hence, the cloud is an ideal option for any enterprise that doesn’t want to dedicate resources for such.

Snowflake is a popular cloud storage option for companies. It allows companies to build a data warehouse for the storage of their data. After storing data in a data warehouse, companies will want to extract insights from the data. Such insights can then help the business managers to make evidence-based decisions. Google BigQuery is Google’s Data Warehousing platform that is serverless, cost-effective, highly scalable, and has Machine Learning built into it. Thus, by connecting Snowflake to BigQuery companies can leverage the advantages of both Data Warehouses.

This article provides a step-by-step guide to connect Snowflake to BigQuery. It also provides a general overview of both Snowflake and BigQuery to better understand these technologies individually. Furthermore, you will also come across a  few limitations of this process. Read along to find out how you can connect Snowflake to BigQuery for your business.


  • A Snowflake Account. 
  • A Google Account.
  • Basic working knowledge of Data Warehouses

Introduction to Snowflake

Snowflake Logo
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Snowflake is an ANSI-SQL-based cloud data warehouse that uses leading cloud services like AWS, GCP, and Azure. Snowflake is entirely a Software-as-a-Service that means it handles all the hardware maintenance and performance tuning activities, thereby making the user a hassle-free experience.

To learn more about Snowflake, click this link.

Introduction to Google BigQuery

Google Big Query Logo
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Google BigQuery is a Data Warehousing platform that is serverless, cost-effective, highly scalable, and has Machine Learning built into it. It uses the Business Intelligence Engine for its operations. It enables quick SQL queries to be combined with the processing power of Google’s infrastructure to manage business transactions, manage the data across different databases, and also allow access control policies for users to view and query data. 

BigQuery has a BI engine which is a fast memory analysis service that enables users to analyze large and complex datasets with high concurrency. It works very well with tools such as Google Data Studio and Looker for analysis.

To learn more about Google BigQuery, click this link.

Importance of Migrating from Snowflake to BigQuery

Most organizations have mastered the science and technique of data warehousing. These organizations have applied prescriptive analytics to their huge volumes of data, gaining insights into their business operations. Conventional Business Intelligence (BI) tools are good for querying, reporting, and Online Analytical Processing but that’s not enough. 

Today, the goal of businesses is to use descriptive analytics to understand past events as well as predictive analytics to predict the occurrence of future events. A combination of descriptive analytics and predictive analytics can help businesses to take real-time actions. That is why Google developed BigQuery. BigQuery provides its users with access to structured data storage and analytics that are flexible, scalable, and cost-effective. 

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Steps to Connect Snowflake to BigQuery

You can connect Snowflake to BigQuery by following these 2 steps:

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Before connecting Snowflake to BigQuery, it is important to understand a few parameters that make up this connection. Some of those parameters are:

  • Cloud Storage Environment

Before connecting Snowflake to BigQuery, it is important to set up the Cloud storage environment. You can rely on a cloud storage bucket to stage your data for initial loading and querying as an external source of data. If the location of the BigQuery dataset has been set to another value other than the United States, you should provide a regional or multi-regional cloud storage bucket in a similar region as the BigQuery instance. The architecture of Snowflake’s Cloud storage environment is given below.

Snowflake Cloud Architecture
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  • Schema

Database schema plays an important role when you are connecting Snowflake to BigQuery.

When data is imported in bulk from a file such as CSV, JSON, or an Avro, BigQuery automatically detects the schema, hence, there is no need to predefine it. If you want to change the schema during migration, first migrate the schema as-is. BigQuery supports different data model design patterns like the Snowflake schema and the Star schema. 

Note that BigQuery uses a case-sensitive naming convention while Snowflake supports a case-insensitive naming convention. This means that you must rectify any table-naming inconsistencies in Snowflake as well as those that arise during migration to Bigquery. 

BigQuery does not support some schema modifications, hence, they will require some manual workarounds. Examples include changing a column name, changing column data type, deleting a column, and changing the column mode.

  • Supported Data Types, File Formats and Properties

Snowflake and BigQuery support almost similar data types, but they sometimes use different names. Snowflake can export data to BigQuery in three file formats namely CSV, JSON (newline-delimited), and Parquet. If you need a quick load time, choose parquet. 

  • Migrating Tools

When you connect Snowflake to BigQuery some tools are needed. There are different tools that you can use to migrate data from Snowflake to BigQuery. Examples include the COPY INTO command, BigQuery Data Transfer Service, gsutil, bq, cloud storage client libraries, BigQuery client libraries, BigQuery query scheduler, etc.

  • Migrating the Data

You can export your Snowflake data into a CSV, Parquet, or JSON file and load it into the cloud storage. You can then use the BigQuery Data Transfer Service to load the data from cloud storage into BigQuery. 

You can build a pipeline that unloads data from Snowflake. The following steps can help you connect Snowflake to BigQuery:

Step 1: Unloading the Data from Snowflake

Unload the data from Snowflake into Cloud Storage. You can also use tools such as gsutil or the Cloud Storage client libraries to copy the data into Cloud Storage. 

Step 2: Copy the Data onto BigQuery

Use one of the following ways to copy the data from the Cloud Storage into BigQuery:

  • bq command-line tool. 
  • BigQuery Data Transfer Service. 
  • BigQuery client libraries. 

That’s it! You have successfully connected Snowflake to BigQuery!

This process does have a few limitations. Some of those limitations are:

  • The process is lengthy and complex. The user has to go through many steps to do configurations. 
  • The process of setting up Snowflake to BigQuery migration is too technical. This means that technical expertise may be needed. Companies without technical know-how may be forced to hire the services of a technical team. 
  • It is impossible to transfer data from Snowflake to BigQuery in real-time. 


This article gave you a step-by-step guide on connecting Snowflake to BigQuery. It also gave you an overview of both Snowflake and BigQuery. Overall, connecting Snowflake to BigQuery is an important process for many businesses and you can follow the simple steps above to achieve this.

In case you want to integrate data from data sources into your desired Database/destination like BigQuery and seamlessly visualize it in a BI tool of your choice, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.


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Share your experience with connecting Snowflake to BigQuery in the comments section below!

Nicholas Samuel
Technical Content Writer, Hevo Data

Nicholas Samuel is a technical writing specialist with a passion for data, having more than 14+ years of experience in the field. With his skills in data analysis, data visualization, and business intelligence, he has delivered over 200 blogs. In his early years as a systems software developer at Airtel Kenya, he developed applications, using Java, Android platform, and web applications with PHP. He also performed Oracle database backups, recovery operations, and performance tuning. Nicholas was also involved in projects that demanded in-depth knowledge of Unix system administration, specifically with HP-UX servers. Through his writing, he intends to share the hands-on experience he gained to make the lives of data practitioners better.

No-code Data Pipeline For Google BigQuery