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
- Introduction to Google BigQuery
- Introduction to Google Drive
- Setting up Google Drive to BigQuery Connection
- Benefits of Connecting Google Drive to BigQuery
- A Google Account
Introduction to Google BigQuery
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 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.
Simplify Google BigQuery ETL & Analysis with Hevo’s No-code Data Pipeline
A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate and load data from 100+ Different Sources (including 40+ Free Sources) to a Data Warehouse such as Google BigQuery or Destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.Get Started with Hevo for Free
Check out some of the cool features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- 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.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (including 40+ free sources) that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
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
- Google Drive to BigQuery Connection Step 2: Saving the results of the Query to Google Sheets
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.
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.
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.
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.
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.
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.
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.
Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations such as Google BigQuery with a few clicks.Visit our Website to Explore Hevo
Hevo Data with its strong integration with 100+ 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 Google BigQuery, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.
Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing pricing, which will assist you in selecting the best plan for your requirements.
Share your experience of learning how to Connect Google Drive to BigQuery in the comment section below! We would love to hear your thoughts.