Connecting Google Drive to BigQuery: 2 Easy Steps

on Data Warehouse, ETL Tutorials, Google BigQuery, Google Cloud Platform, Google Drive • March 1st, 2022 • Write for Hevo

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Google BigQuery is a serverless, fully-managed analytics data warehouse that regularly releases new features and upgrades with no downtime or user burden. The Google BigQuery team strives to deliver features that improve user productivity and interoperability, as well as make Google BigQuery even easier to use, for enterprise customers.

The Google BigQuery team has announced a partnership with Google Drive. You are now able to perform 3 Google Drive to BigQuery functions, Google BigQuery UI allowing you to save query results directly to Google Sheets. Directly querying files from Google Drive without first loading them into Google BigQuery also Google BigQuery can query Google Sheets spreadsheets as you edit them in Sheets!

In this blog, you’ll learn how to connect Google Drive to BigQuery, and an introduction to the platform and its key features respectively.

Table of Contents

Prerequisites

  • A Google Account

Introduction to Google BigQuery

Google Drive to BigQuery - Google BigQuery logo
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Google BigQuery was created as a flexible, fast, and powerful Data Warehouse that is tightly integrated with the other Google Platform services. It has a Serverless Model, user-based pricing, and is cost-effective. The Analytics and Data Warehouse platform of Google BigQuery uses a built-in Query Engine on top of the Serverless Model to process terabytes of data in seconds.

With Google BigQuery, you can run analytics at scale with a lower three-year TCO of 26 percent to 34 percent than other Cloud Data Warehouse alternatives. Because there is no infrastructure to manage or set up, you can concentrate on gaining meaningful insights using Standard SQL and flexible pricing models that include Flat-rate and On-demand options.

Google BigQuery’s column-based Storage service fueled the Data Warehouse’s speed and ability to handle massive amounts of data. Because Column-based Storage allows you to process only the columns of interest, you can get answers faster and use resources more efficiently. There are various Business Intelligence tools that can be integrated with Google BigQuery to provide Standard SQL Access. As a result, it is more advantageous to store data by column in analytical databases.

Key Features of Google BigQuery

Here are a few of Google BigQuery’s key features:

  • Serverless Computing: In general, organizations in a Data Warehouse environment must commit to and specify the server hardware on which computations will run. Administrators must then plan for performance, dependability, elasticity, and security. A Serverless Model aids in overcoming this limitation. In a Serverless Model, the processing is automatically distributed across a large number of parallel machines. Database Administrators and Data Engineers can focus less on infrastructure and more on server provisioning by using Google BigQuery’s Serverless model. As a result, they can gain more valuable insights from data.
  • Support for SQL and Programming Languages: Users can connect to Google BigQuery using Standard SQL. Aside from that, Google BigQuery has client libraries for writing data-accessing applications in Python, C#, Java, PHP, Node.js, Ruby, and Go.
  • The architecture of Trees: By structuring computations as an Execution Tree, Google BigQuery and Dremel can easily scale to thousands of machines. A Root Server receives incoming queries and forwards them to branches known as Mixers. The incoming queries are then modified by these branches and delivered to Leaf Nodes, also known as Slots. The data is then filtered and read by the Leaf Nodes, who work in parallel. The results are moved back down the tree, where Mixers accumulate them before sending them to the root as the answer to the query.
  • Multiple Data Types: Google BigQuery offers support for a vast array of data types including strings, numeric, boolean, struct, array, and a few more.
  • Security: Data in Google BigQuery is automatically encrypted either in transit or at rest. Google BigQuery can also isolate jobs and handle security for multi-tenant activity. Since Google BigQuery is integrated with other GCP products’ security features, organizations can take a holistic view of Data Security. It also allows users to share datasets using Google Cloud Identity and Access Management (IAM). Administrators can establish permissions for individuals and groups to access tables, views, and datasets.

Introduction to Google Drive

Google Drive to BigQuery - Google Drive logo
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Google Drive is a Cloud Storage Service that allows you to save files online and access them from any smartphone, tablet, or computer that has an Internet connection. There are several advantages to using a Cloud Storage Service such as Google Drive, such as easier file sharing and a remote location to back up your files, but when compared to competitors such as DropBox and Apple’s iCloud service, Google Drive’s popularity has been built on helpful collaboration tools and built-in integrations with Google’s product – and service suite.

You can also use free Web-Based tools to create Documents, Spreadsheets, Presentations, and more by integrating Google Drive with other Google products. Google Drive is a free service that allows people to organize and share files on a personal and professional level. Businesses use Google Drive because of its simple interface, dependability, and security, all of which come at a low cost.

Key Features of Google Drive

The following are some of Google Drive’s key features:

  • Gmail Attachments Should Be Saved: This is one of Google Drive’s most popular features, which allows you to save attachments from emails. When you receive an email with images or attachments, it’s simple to save them to Drive. After you save it, simply click the Attachment icon to move it to any folder on the Drive while using Gmail.
  • Mode Offline: After activating Offline mode, you can work offline even if you don’t have an internet connection.
  • Simple to Use Interface: When you log in to your Google Drive account, you’ll see your most recent documents at the top of the page, along with a list of all your folders and easy navigation on the left that allows you to view all the documents shared outside of your personal drive.
  • Sharing and personalization: Every file or folder in Google Drive has its own Share Link, and you can grant other users the ability to customize the file.
  • SSL Encryption: Google Drive, according to Google, is also secured with the same SSL encryption that is used in Gmail and other Google Services.

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  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, Databases, analytics, and BI tools. It supports various destinations including Google BigQuery, AmazonRedshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.  
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Setting up Google Drive to BigQuery Connection

Here are 2 steps to establish Google Drive to BigQuery Connection

Google Drive to BigQuery Connection Step 1: Using Google Spreadsheets as tables

BigQuery allows you to create tables that reference your Google Sheets spreadsheets.
I use Google Sheets to keep track of musicians I’m interested in right now.

Google Drive to BigQuery - Google Sheet
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Aside from the quality of my musical tastes, I’d like to get a list of the most popular songs by these artists based on public playlist data in Google BigQuery. I have a habit of changing my preferences on a regular basis. That’s how I want it.

I define a Google BigQuery table that reads my Google Sheets spreadsheet of preferred artists using BigQuery’s new table create UI.

Google Drive to BigQuery - Google BigQuery table
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Now that the Sheets-backed table has been defined, I can query it against a list of playlists to find out which songs are the most popular.

Google Drive to BigQuery - Query
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Let’s say we just want to have a good time and break our promise to never abandon Rick Astley in favor of Cyndi Lauper. We simply make changes to our Google Sheets spreadsheet.

And we ran the SQL query again in Google BigQuery. Because the table “artists” read directly from our spreadsheet, our preference for Cyndi Lauper is seamlessly registered in Google BigQuery.

Google Drive to BigQuery - Query
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We can make changes to our Google Sheets spreadsheet at any time, and Google Drive to BigQuery connections will automatically pick up the changes the next time we run a query against the spreadsheet!

Google Drive to BigQuery Connection Step 2: Saving the results of the Query to Google Sheets

In the Google BigQuery user interface, all users should see a “Save to Google Sheets” button. When you click this button, the query results will be saved to a Google Sheet and you will be prompted to open that Google Sheet.

Google Drive to BigQuery - Query
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When you click this button, the query results will be saved to a Google Sheet and you will be prompted to open that Google Sheet. And a copy will be available in Google Drive

Benefits of Connecting Google Drive to BigQuery

  • It is simple to set up: You don’t want to spend hours trying to set up a data tool to aggregate all of your information when you’re busy running your business. The most significant advantage of BigQuery is that it is simple and quick to set up. A Data Warehouse can be set up in seconds.
  • Simple to use: One of Google BigQuery’s most significant advantages is its ease of use. Building your own data center is not only costly but also time-consuming and difficult to scale. It frustrates you and can even waste your time as you try to understand your data.
  • Scales with ease: Google BigQuery separates data storage and computation. This process enables elastic scaling, which allows you to scale at a faster rate. It works seamlessly for real-time analytics and scales your data appropriately to help you make sense of it.
  • Insights gained more quickly: Google BigQuery provides a comprehensive view of your data. You can use data tools to help you digest and break down your data even further. Tableau and Data Studio, for example, work in tandem with Google BigQuery to help you better understand your data.
  • Data is safeguarded: Your data is valuable to your company. Google BigQuery safeguards your data and ensures its safety. Although you should always have a disaster recovery plan in place, this process alleviates the burden of having disaster recovery in place in case your data is compromised or lost.

Conclusion

As always, Connecting Google Drive to BigQuery delivers these features seamlessly — with no downtime and no user action or configuration required. This is how fully managed is supposed to be. This article has introduced you to how to Connect Google Drive to BigQuery.

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