Publish Date: 2^nd August 2024

If you are a student/data engineer struggling with slow queries and large data sets, Google BigQuery is your saviour.  

It must be your go-to tool, as it is a cloud-based and serverless data warehouse which is designed to process large datasets quickly. 

Google BigQuery saves your time, makes data analysis faster and more efficient. 

Also, if you are dealing with structured or semi-structured data, it simplifies the process. So you can focus on insights.

In this blog, we will learn about: 

• What is Google BigQuery?

• What are the advantages of using BigQuery?

• How BigQuery can help in real world solutions?

• How to get started with Google BigQuey?

• How to add data into Google BigQuery?

Without further delay, let’s roll into getting a clear understanding of how to use Google BigQuery and analyze data effortlessly. 

What is Google BigQuery?

As discussed above, Google BigQuery is a serverless cloud data warehouse that enables businesses to store, process, and analyze large datasets with speed and efficiency.

Its highly organised structure allows businesses to process petabytes of data easily. 

Also, it allows real-time analytics and smarter decision-making without worrying about infrastructure.

Not only this but with its ability to manage large-scale data effortlessly, Google BigQuery is transforming the way businesses derive insights and make data-driven decisions. 

Now that we understand its key uses, let’s explore the advantages of using BigQuery.

What are the advantages of using BigQuery?

As you already know, Google BigQuery is more than a data warehouse. It is a powerful, flexible, and efficient tool designed to manage datasets with ease. 

Let’s learn the advantages of BigQuery for data-driven organizations:

1.  Easy Scalability

If your business is growing or handling large-scale analytics, BigQuery can automatically scale to keep up with your needs and ensure smooth and fast performance.

BigQuery is built to handle everything from gigabytes to petabytes of data. 

2.  High-speed Data Analysis

When it comes to analyzing data, BigQuery processes complex queries in seconds. 

It means that BigQuey can provide real-time insights, faster decision-making, and quicker responses to business needs.

3.  Fully Managed & Serverless

When you are working with data, you can easily forget about managing it because BigQuery is fully managed. It can manage provisioning servers, updates, or maintenance. 

Google takes care of it all, so you can focus on what matters i.e. analyzing your data.

4.  Cost-Effective

BigQuery follows a flexible, pay-as-you-go pricing model i.e. you only need to pay for the storage and queries you use.

This makes it an affordable choice for businesses of all sizes.

5.  Seamless Integration with Google Cloud

When we talk about other Google Cloud services like Google Analytics, Google Cloud Storage, and Looker Studio, BigQuery works effortlessly with each service. 

And, BigQuery working with all other services makes data visualization, reporting, and storage more efficient.

With all these advantages, BigQuery is transforming the way businesses store, analyze, and leverage data for smarter decision-making. 

But how does it apply to real-world scenarios? 

Let’s explore how BigQuery is making an impact across industries.

How BigQuery can help in real world solutions?

You must already be aware that from customer purchases to website traffic, IoT sensor readings, and more, businesses generate large amounts of data. 

But raw data alone does not add any value. 

So, you need the right tools to process, analyze, and find insights.

This is exactly where Google BigQuery comes in. 

With advanced features like machine learning, geospatial analysis, and seamless cloud integration, BigQuery helps companies transform raw data into actionable insights. 

So, businesses can make smarter, faster decisions without having to worry about infrastructure or scalability.

Here are some practical ways BigQuery is used:

1. Business Intelligence 

Generally, companies use BigQuery to analyze sales trends, customer behavior, and marketing performance. It helps companies to make data-driven decisions easily.

2. Internet of Things (IoT) 

When IoT devices generate tons of data from sensors, logs, and connected devices,  BigQuery processes this data.

It deals with the data in real-time for predictive maintenance, security monitoring, and efficiency improvements.

3. Machine Learning

With built-in ML models, businesses can train predictive models directly in BigQuery. By doing so, BigQuery eliminates the need for separate AI platforms.

4. Data Warehousing

Almost always, organizations store and manage large-scale structured and semi-structured data with BigQuery. And doing so ensures fast and efficient access.

5. Real-time Analytics 

If you need real-time insights on website traffic or social media trends? 

BigQuery enables instant analytics, helping businesses respond faster to market demands.

By simplifying complex data operations, Google BigQuery boosts businesses to work smarter by driving efficiency, innovation, and smarter decision-making.

But how do you actually start using BigQuery?

Let’s break it down step-by-step.

How to get started with Google BigQuey?

One of the best things about Google BigQuery is that it is cloud-based.

This means that you don’t have to go through any complicated installations or software setup. You can start analyzing data right away.

Here’s the step-by-step process of how to start with Google BigQuery:

Step 1: Access Google BigQuery

Firstly, you need to Go to the Google Cloud Platform (GCP) and click on the “Console” button at the top right corner of the page. 

This will take you to the GCP dashboard, where you can start working with BigQuery.

Step 2: Create a New Project

Secondly, you need to follow the on-screen instructions and click “Create or select a Project” or “Select a project”  to set up your workspace.

Step 3: Name Your Project

Thirdly, you need to give your project a unique name and hit “Create” to finalize it.

Step 4: Create a BigQuery Dataset

Fourthly, you need to click the hamburger menu (☰) on the left, navigate to BigQuery, and select “Create Dataset” to start storing your data.

Step 5: Define Your Dataset

Lastly, you will be asked to:

• Enter a Dataset ID (a unique name for your dataset).

• Choose a Data Location (where your data will be stored).

Once done, click “Create Dataset.”

So, with your BigQuery project and successful set up of the dataset, you are now ready to start working with data. 

Now that your dataset is all ready, the next step is to bring in the data.

Wonderfully, Google BigQuery allows you to upload data from multiple sources, including CSV files, Google Sheets, or even real-time streams. 

Let’s go through the process of adding data so you can start running powerful queries.

How to add data into Google BigQuery?

Before you can start analyzing data in BigQuery, you need to bring in your dataset.

Let’s walk through the steps to add data into your BigQuery dataset.

Step 1: Open Your Dataset in Google Cloud

Firstly, you need to navigate to Google Cloud Platform (GCP), open the dataset you previously created and click on “Create Table” to begin adding your data.

Step 2: Choose Your Data Source

Secondly, under the “Create Table From” field, you need to select “Upload” as the data source. This option will allow you to upload files directly from your local storage.

Step 3: Upload Your CSV File

Thirdly, you need to click “Browse”, locate the supermarket stores dataset CSV file, and select it. Once you are done uploading, BigQuery will process the file for further configuration.

Step 4: Name Your Table

Fourthly, in the “Destination Section”, enter a relevant table name to easily identify your data. The table name should be unique within your dataset.

Step 5: Auto-Detect Schema & Create Table

Lastly, you need to check the “Auto Detect” checkbox to allow BigQuery to analyze the structure and define column types automatically. And, click “Create Table” to finalize the setup.

Now, your table is created. To confirm the successful upload, navigate to the “Preview” tab and explore the newly added supermarket stores data.

With this, you are all set to run powerful queries and extract insights from your dataset.

Conclusion 

As we already know, Google BigQuery is a game-changer for businesses looking to make use of the power of data without worrying about structure or scalability. 

GoogleBigQuery has got it all covered for you. From real-time analytics and machine learning to business intelligence and IoT applications, it boosts organizations to analyze large datasets quickly and efficiently. 

With its serverless design, seamless cloud integration, and fast performance, BigQuery is changing the way organizations store, process, and use data.

Now that you know how to set up, upload, and query data, the next step is exploring its full potential. 

If you are working on business intelligence, machine learning, or real-time analytics, BigQuery is your go to tool.

Now, are you ready to dive in and try it? Start using BigQuery today and turn raw data into real value.

Author Bio:

Sumit Gupta, data science leader with experience leading analytics teams in sales, marketing and product domain. Sumit is also a published author on Tableau with title “The Tableau Workshop”. Sumit writes about all things analytics and career progression for immigrants and more! You can follow Sumit on LinkedIn