Google BigQuery Console Simplified: The Ultimate Guide 101

Samuel Salimon • Last Modified: December 29th, 2022

BigQuery Console

The Google BigQuery platform uses a Serverless Computing Model to process petabytes of data in minutes and terabytes of data in seconds on a Cloud-based Data Warehouse.

Google Cloud Platform’s BigQuery service is a fast, flexible, and robust Data Warehouse tightly integrated with Google Cloud Platform’s other services. But what are the best practices for managing BigQuery resources? This can be done via the Cloud Console

This article will learn how to create and manage Google BigQuery Resources using the Google BigQuery Console!

Table of Contents

What is Google BigQuery?

BigQuery Console - Google BigQuery logo
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Google Cloud Platform’s BigQuery is part of the GCP Suite, and it integrates with other GCP tools and services. The Google BigQuery Data Warehouse can process data from other Google Cloud product services, including Cloud Storage, Cloud SQL, Cloud BigTable, Google Drive, and Spanner, Google’s Distributed Database.

Business Intelligence tools such as Tableau and Looker can be integrated with Google BigQuery, supporting standard SQL access. Utilizing Distributed Computing Technology within a Serverless Architecture, Google BigQuery can quickly process enormous amounts of data using multiple parallel servers to significantly increase processing speed.

5 Key Features of Google BigQuery

Here are some notable key features of Google BigQuery:

1) Tree Architecture

In Google BigQuery, computations are structured as Execution Trees to scale up to thousands of Machines. As queries arrive at root servers, they are routed to mixers, modified, and forwarded to Leaf Nodes, called Slots.

These Leaf Nodes read and filter data parallel to other Nodes. When the results return to the Root, Mixers agglomerate them into a unified development and send it to the Root as the answer to the Query.

2) Serverless Service

The Server hardware on which Computations take place must be specified and committed to by organizations in most Data Warehouse Environments. A System Administrator must ensure the system’s Performance, Security, Elasticity, and Reliability. A Serverless Model eliminates these constraints.

Serverless Models distribute processing over a large number of computers working in parallel. The Serverless Model of Google BigQuery lets Data Engineers and Database Administrators focus more on gaining insight from data and less on infrastructure.

3) Support for SQL and Programming Languages

Standard SQL can access Google BigQuery, which is familiar to many users. Google BigQuery also offers APIs in Java, Python, C#, Go, Node.js, PHP, and Ruby that allow you to create applications that access Google BigQuery data.

4) Real-time Analytics

Google BigQuery can perform Analytical Tasks and generate reports based on real-time data by leveraging other GCP services. Data Warehouses can be used for Analytics after storing and consolidating data from multiple sources – typically done in batches throughout the day. Data is streamed at a speed of millions of rows per second using Google BigQuery’s Batch Processing.

5) Security

Data security can be taken holistically with BigQuery because it is tightly integrated with GCP’s other security features. BigQuery automatically encrypts data while it is in transit or at rest. BigQuery is also capable of isolating jobs and handling multi-tenant security.

How to Use the Google BigQuery Console?

The Google Cloud Console lets you create Google BigQuery Resources and run SQL Queries using a GUI (Graphical User Interface). This section will introduce you to the Cloud Console Overview and use Google BigQuery to query publicly available data.

Please note that you must be logged into the Cloud Console and create a project before you can explore Google BigQuery. 

How to Open the Google BigQuery Page in Cloud Console?

The BigQuery Engine is enabled by default when creating a new project. In a preexisting project, enabling the BigQuery APIs can be done by going to enable the BigQuery API. Access the Google BigQuery Page in the Google Cloud Console by selecting or creating a Google Cloud Project on the Project Selector page.

Alternatively, the following URL can also be entered in your browser to access the Google BigQuery page.

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The Google Cloud Console

Google Cloud Console has the following three main sections:

BigQuery Console - Google Cloud Console overview
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A) Google BigQuery Console: The Navigation Menu

There are several Google BigQuery Resources available through the navigation menu:

  • The SQL Workspace: Datasets, tables, views, and other Google BigQuery Resources are displayed here. With this interface, you can run and create queries, work with tables and views, view the history of your BigQuery Jobs, and perform other commonly used Google BigQuery Tasks.
  • Transfers Tab: It takes you to the Google BigQuery Data Transfer Service.
  • Scheduled Queries: This section displays information about your Scheduled Queries.
  • Capacity Management: The section shows Slots committed, reservations made, and reservations assigned.
  • BI Engine: In this section, you’ll find the Google BigQuery BI Engine page.

Those who want to see only the icons of Google BigQuery can hide the navigation menu by clicking the Hide Google BigQuery Navigation menu. Holding down the pointer over the Navigation Menu will temporarily expand it. To maintain Labels visible in the navigation menu, click the Show Google BigQuery Navigation menu.

B) Google BigQuery Console: The Explorer Panel

From the navigation menu, select SQL Workspace to access the Explorer Panel. Listed here are your current project and all the projects you have pinned. When you expand a project, you can see the datasets you have access to. When you raise that dataset, you will see the tables, views, and functions contained within.

The search box can also find resources by name, project, dataset, or label. Your search box returns a list of resources that match your current project or contain matching resources.

With Google BigQuery’s search and autocomplete capabilities powered by Data Catalog, BigQuery loads your results on demand for search in the Explorer panel as well as for autocompleting in SQL Queries. The Cloud Console’s performance improves with the Data Catalog, and resources can be searched outside of your current and pinned projects.

C) Google BigQuery Console: The Details Panel

The Details panel of Google BigQuery’s Cloud console displays detailed information about its resources. When users select a dataset or another resource in the Explorer panel, a new tab is opened in the Details panel. The tab displays the resource’s information.

How to Query a Public Dataset in Cloud Console?

With Google Cloud Console, you can query tables and datasets, including the publicly available Google BigQuery datasets. This example will query the public USA Name Data dataset to find the most famous American names between 1910 and 2013.

You can query a Public Dataset by following these steps:

Step 1: Go to the Google BigQuery page in the Cloud Console.

Step 2: You will need to click +COMPOSE NEW QUERY if the Editor tab isn’t visible.

BigQuery Console - Cloud Editor Tab
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Step 3: Enter the following code in the Editor field:

  name, gender,
  SUM(number) AS total
  name, gender
  total DESC

In the case of valid queries, the number of processed records appears alongside a checkmark. A red exclamation point and the error message appear if the query is invalid.

Step 4: After that, click Run. The Query results page shows how long it took to process the query and how much data was returned.

Step 5: Click on the Results tab to see a detailed listing of the query results. The table’s header row contains the column names you chose during the query.

BigQuery Console - Public Dataset Query Results
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In this article, we discussed what Google BigQuery is all about and its key features. We also discussed using Google BigQuery Console and loading and Querying data using the Google Cloud Console. For further information on Google BigQuery Cloud Console, you can visit here.

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Share your experience working with Google BigQuery Console in the comment section below!

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