Google Sheets to BigQuery: 3 Ways to Connect & Migrate Data

As your company grows and starts generating terabytes of complex data, you will have data stored in different sources. That’s when you need to incorporate a data warehouse, such as BigQuery, into your data architecture for migrating data from Google Sheets to BigQuery. Sifting through terabytes of data on spreadsheets is a monotonous endeavor that places a ceiling on what is achievable in data analysis.

At this stage of growth, incorporating a data warehouse like BigQuery becomes a necessity. In this blog post, we will cover how to move data from Google Sheets to BigQuery in detail.

Overview of Google Sheets

Google Sheets is a cloud-based spreadsheet application that offers a flexible and user-friendly platform for data organization, analysis, and collaboration. You can access it from any device connected to the Internet, enabling real-time collaboration and a range of other valuable features.

Key Features:

1. Real-Time Collaboration: It enables multiple users to work on the same spreadsheet, with all changes being saved automatically. The changes show up in real-time for all users, ensuring up-to-date data.
2. Accessibility and Convenience: It is accessible through any web browser or the mobile Google Sheets app. It also supports offline editing and automatically synchronizes changes once back online.
3. Data Management and Analysis: It provides extensive support for functions and formulas used in calculations and data analysis, along with tools for creating various charts and graphs that facilitate easy data analysis for its users.
4. Integration and Automation: It offers  numerous add-ons to extend functionality, including third-party integrations.Overview of

Overview of BigQuery

Google BigQuery is a fully managed, serverless data warehouse offered by Google Cloud, enabling the execution of super-fast SQL queries and harnessing the power of Google’s infrastructure. It’s designed for large-scale data analytics, helping businesses and organizations to analyze vast datasets quickly and effectively.

Key Features:

1. Serverless Architecture: BigQuery manages servers and storage in the background, so users do not need to.
2. High Scalability: It scales seamlessly to handle petabytes of data.
3. SQL Compatibility: It supports ANSI SQL, which can be beneficial for people who already know SQL and want to write and run queries. This also enables users to combine various BI tools for data visualization.
4. Machine Learning: BigQuery ML allows users to train and run machine learning models in BigQuery using only SQL syntax.

Why Migrate from Google Sheets to BigQuery?

• Automation and Real-Time Data: BigQuery offers seamless integration with other Google Cloud services for automated, real-time data ingestion and reporting, which can be more manual and complicated in Google Sheets.
• Scalability: Google Sheets has limitations while handling large datasets, whereas BigQuery can manage and analyze petabytes of data in minutes, making it ideal for meeting growing data needs.
• Performance: BigQuery offers faster query execution and analytics on large datasets compared to Google Sheets, which tends to slow down with larger data volumes.
• Advanced Analytics: BigQuery supports SQL-based queries and integrates with machine learning tools, enabling more advanced data analysis than what’s possible in Google Sheets.
• Business planning: Build and prepare datasets, then allow others to find insights from the data. For example, analyze sales data to determine which products sell better in different locations.
• Customer Service: Identify which stores receive the most complaints per 10,000 customers.
• Sales: Create internal finance and sales reports, and share revenue reports with sales reps.

You can also check out how to connect your Google Sheets data with a database like PostgreSQL.

Methods to Connect Google Sheets to BigQuery

Prerequisites:

1. Google Cloud account.
2.  Business, Enterprise, or Education G Suite BigQuery accounts.
3. Google Cloud project with billing enabled.
4. Necessary IAM permissions. 

Method 1: Using Hevo to Move Data from Google Sheets to BigQuery

For this method, we will be using Hevo — a real-time, no-code ELT data pipeline platform that automates data movement in a cost-effective and flexible manner, tailored to your specific needs.

Using a fully managed platform like Hevo, you bypass all the usual setup hassles and (supports as a free data source) import Google Sheets to BigQuery in just a few minutes. You can achieve this in 2 simple steps:

• Step 1: Configure your Google Sheets as a source by entering the Pipeline Name and the spreadsheet you wish to replicate.

• Step 2: Configure your BigQuery account and start moving your data from Google Sheets to BigQuery by providing the project ID, dataset ID, Data Warehouse name, and GCS bucket.

For more details, Check out:

• Google Sheets Source Connector
• BigQuery Destinations Connector

Method 2: Using BigQuery Web Console to Move Data from Google Sheets to BigQuery

You can easily upload using BigQuery’s data connector. The steps below illustrate how:

• Step 1: Log in to your Google Cloud Platform (GCP) console and navigate to the BigQuery UI using the hamburger menu.

• Step 2: Inside BigQuery, select ‘Create Dataset’.

• Step 3: After creating the dataset, next up, we create a BigQuery table that will contain our incoming data from sheets. To create a BigQuery table from a Google Sheet, click on ‘Create a table.’ In the ‘Create a table‘ tab, select Drive.

• Step 4: Under the source window, choose Google Drive as your source and populate the Select Drive URL tab with the URL from your Google Sheet. You can select either CSV or Sheets as the format. Both formats allow you to select the auto-detect schema. You could also specify the column names and data types. 
• Step 5: Fill in the table name and select ‘Create a table.’ With your Google Sheets linked to your Google BigQuery, you can always commit changes to your sheet and it will automatically appear in Google BigQuery.

• Step 6: Now that we have ingested data into BigQuery, we can perform SQL queries on it. The following image displays a brief query we executed on the data in BigQuery.

Method 3: Using Connected Sheets to Move Data from Google Sheets to BigQuery

This method to upload Google Sheets to BigQuery is only available for Business, Enterprise, or Education G Suite accounts. This method allows you to save your SQL queries directly into your Google Sheets. Steps to using the Connected Sheets are highlighted below with the help of a public dataset:

• Step 1: For starters, open or create a Google Sheets spreadsheet.
• Step 2: Next, click on Data > Data Connectors > Connect to BigQuery.

• Step 3: Click Get Connected and select a Google Cloud project with billing enabled.

• Step 4: Next, click on Public Datasets. Type Chicago in the search box, and then select the Chicago_taxi_trips dataset. From this dataset, choose the taxi_trips table and then click on the Connect button to finish this step.

This is what your Google Sheets spreadsheet will look like:

You can now use this spreadsheet to create formulas, charts, and pivot tables using various Google Sheets techniques.

Managing Access and Controlling Share Settings

It is pertinent that your data is protected across both Sheets and BigQuery; hence, you can manage who has access to both Sheets and BigQuery. To do this, simply create a Google Group to serve as an access control group.

By clicking the share icon on sheets, you can grant access to your team members can edit, view, or comment.

Any changes made here will also be replicated in BigQuery.

This will serve as a form of IAM for your data set.

Limitations of using Sheets Connector to Connect Google Sheets to BigQuery

In this blog, we covered how you can incorporate BigQuery into Google Sheets in two ways so far. Despite the immeasurable benefits of the process, there are some limitations that you should be cautious about

• This process is not viable for supporting volumes of data greater than 10,000 rows in a single spreadsheet.
• To make use of the sheets data connector for BigQuery, you need to operate a Business, Enterprise, or Education G suite account. This is an expensive option. 

Best Practices for Connecting Google Sheets to BigQuery

When linking Google Sheets to BigQuery, follow these guidelines:

• Read-Only Access: You can view BigQuery data in Google Sheets, but modifications must be made in BigQuery.
• Preview Limitations: Sheets displays only the first 500 rows as a preview; however, formulas, pivot tables, and charts utilize the full dataset.
• Data Limits: To ensure optimal performance, it is suggested to limit the number of rows in data extracts and pivot table results to 50,000.

These practices help you work effectively with BigQuery data in Google Sheets.

Additional Resources on Google Sheets to Bigquery

• Move Data from Excel to Bigquery

Conclusion

In this blog, we have provided you with three different methods to move your data from Google Sheets to BigQuery in a seamless manner. We recommend using ETL pipeline tools, such as Hevo, to transfer data from any source to your destination. Hevo ensures that your data is consistently and securely moved in real-time, enabling real-time reporting.

Sign up for a 14-day free trial with Hevo Data to streamline your data migration and effortlessly connect Google Sheets to BigQuery for real-time analysis—no coding needed!

FAQs to connect Google Sheets to BigQuery