What Is Bigquery for High-Speed Data Analysis?

Many people know that there is a data analysis tool called BigQuery, but many people may not know what it can do specifically and what benefits it can bring.

BigQuery is a big data analysis tool provided by Google.

In recent years, when the importance of data utilization has been emphasized, an increasing number of companies are accelerating their business efficiency and business expansion by analyzing big data.

In this article, we will introduce what you can do with BigQuery, the benefits and costs of introducing it, and case studies.

What is BigQuery

BigQuery is a cloud-based data warehouse provided by Google Cloud.
A data warehouse, literally translated as a “data warehouse”, is a system in which various company data is stored.

BigQuery enables high-speed analysis of stored big data.
Since the introduction cost is low and the service does not require environment construction, it is currently being introduced by many companies.

What you can do with BigQuery

Big data analysis

BigQuery makes it possible to analyze big data quickly and easily.

For example, by using BigQuery, you can analyze a huge amount of customer data and find out which method of attracting customers leads to sales. You can also analyze large amounts of store data to see the characteristics and trends of stores that sell well.

The introduction cost is low and it can support various data analysis. It can be said that it is a service suitable for continuous data analysis for companies that want to make good use of their own data and connect it to business growth.

Real-time analytics

BigQuery, which is capable of high-speed processing of large amounts of data, enables real-time analysis of constantly fluctuating data.

In today’s world where the amount of data generated is increasing explosively, it is important to analyze fresh data in real time to improve the speed and accuracy of decision making.

For example, it is possible to quickly analyze real-time trends such as breaking news and reflect them in the creative of advertisements.

Coordination with other analysis services

One of the advantages of BigQuery is that it can be easily linked with other functions of Google Cloud Platform.

For example, by using a function called BigQuery ML, it is possible to use machine learning with basic knowledge of SQL. There is no need to move data from BigQuery, so the hassle is minimized.

It is also possible to easily link with BI tools such as Google Data Portal and Looker, which was acquired by Google in 2019. By visualizing data in collaboration with these BI tools, issues can be extracted and improved, which can be used to improve business performance.

In addition, it is possible to cooperate with major BI tools other than Google.
For example, linking with Salesforce, the largest CRM, will help improve the efficiency of the sales department, and linking with Tableau, which specializes in data visualization, will make it possible to use analytical data more intuitively.

Read Also: How To Determine A Web Marketing Budget And How To Evaluate It

Analysis of ad delivery data

Advertisement distribution data such as Google Ads, Yahoo! Ads, and Facebook Ads can also be output to BigQuery, and data utilization can be promoted by linking with BI tools such as Google Data Portal.

However, it is troublesome to frequently download ad distribution data from each medium, process it for BigQuery, and output it to BigQuery.

It is possible to automate using RPA and API, but maintenance such as responding to API version upgrades of media and fixing defects is indispensable, so even if it is automated, it cannot be operated without letting go.

In that case, if you use advertising company support SaaS, etc., it will be easier to analyze ad distribution data using BigQuery.

For example, “ATOM”, which has the No. 1 market share* for advertising company-supported SaaS, is a typical example. Please try to reference.

BigQuery Features and Benefits

No expertise needed

BigQuery is relatively easy to use, even for non-engineers without advanced database knowledge.

For example, it does not require the work of improving query speed that is required in traditional databases. In addition, since data processing and storage are performed on the cloud, no server-side knowledge is required.

With simple SQL knowledge, you can easily operate the intuitive management screen.

Analyze data in the way you want to analyze it

As mentioned above, BigQuery allows you to search, insert, create and delete data as you like with just a simple knowledge of SQL.

Because it can handle a large amount of data at high speed, analysis that required a narrower reference range in other databases can be performed over the entire range, which will expand the range of analysis.

Good value for money

Compared to other data warehouses, it is also a big advantage that it can be introduced at a low cost.

Compared to Amazon Redshift and Microsoft Azure Synapse Analytics, which are also cloud-based data warehouses, the storage usage fee charged according to the stored data is overwhelmingly cheap.

Basically, it is a pay-as-you-go system that charges only for the amount used, so it has the advantage of being easy to adjust the cost according to the amount of use, such as when the frequency of use is extremely low depending on the time of year.

In addition, since the amount of data is known before processing the data, you can use it with confidence because you can know the approximate cost.

Fast data analysis speed

And the feature of BigQuery is the speed of data analysis.

With general data analysis tools, it takes time to analyze huge amounts of data such as TB (terabytes) or PB (petabytes), but with BigQuery, the process can be completed in a few seconds to a few minutes.

This high-speed data processing is achieved by parallel processing of queries using two technologies: “columnar data store” and “tree architecture”.

Can be linked with various Google services

Another unique feature of BigQuery provided by Google Cloud is that it can be easily linked with other Google services.

For example, by linking with Google Sheets, it is possible to use data that was previously managed in a spreadsheet as a table.

In addition, since it is possible to link with GA4, an access analysis tool provided by Google, more advanced access analysis can be realized.

BigQuery Business Success Stories

Next, I would like to introduce a company that has improved operational efficiency, reduced costs, and improved advertising by introducing BigQuery.

It will be a reference for the specific benefits that implementing BigQuery in your company will bring.

Differences between BigQuery and other data analytics services

​​When you hear data analysis, many people associate the terms BI tool and SQL.
What are the differences in roles and meanings between BI tools, SQL, and BigQuery?

Differences from BI tools

BI is an abbreviation for Business Intelligence, and is a method of collecting, accumulating, analyzing, processing, and visualizing data owned by a company, and using it to make management decisions.

BigQuery can be said to be a type of BI tool that is good at accumulating and analyzing data.

However, it is not good at visualizing data, so combining it with a BI tool that specializes in visualization, such as Google Data Portal, will help improve the speed and accuracy of business decision-making.

Differences from SQL

As mentioned above, BigQuery is a data warehouse for storing data.

On the one hand, SQL is a database language for manipulating data. By using SQL, you can search, create, and delete data.

BigQuery can use SQL, so BigQuery can not only store data, but also manipulate data.

Cost of using BigQuery

I will explain in detail the cost, which is a major feature of BigQuery.

BigQuery’s billing system is a pay-as-you-go system, which is divided into storage charges that occur according to the amount of data stored and analysis charges that occur for query processing.

When it comes to storage pricing, it’s split between active storage and long-term retention (data that hasn’t changed for more than 90 days).

Please refer to the table below for detailed costs.

In addition, you can perform the following operations for free.

BigQuery has very reasonable pricing. For example, let’s say you incur a storage charge of 5TB and a query charge of 100TB.

Calculating according to the above table, the storage fee is $115 (about 11,650 yen), the query fee is $600 (about 6,600 yen), and the total is $715 (about 18,250 yen), which is very cheap.

However, because it is a pay-as-you-go type, some people may feel uneasy that it will cost more than expected.

Steps to use BigQuery

Finally, I will explain the steps to get started with BigQuery. BigQuery has an account structure of project → dataset → table, so we will create it in order from the project.

Step 1 Register Google Cloud Platform

• Access this page.
• Click continue and you will be taken to the screen below.
• You will need to set up a credit card, but please rest assured that you will not be automatically renewed to a paid plan. Continue through the page and click Start Free Trial.

Step 2 Create a BigQuery account

• When the registration of Google Cloud Platform is completed, the following screen will appear. Click “BigQuery” on the left menu.

Your BigQuery account has now been created. Next is creating a project.

Step 3 Create a project

• Click “MyProject” at the top of BigQuery that you created earlier.
• A pop-up window will appear, click “New Project”.
• Enter the project name and project ID and click “Create”.

The project creation is now complete. Next is creating the dataset.

Step 4 Create dataset

• Click the 3-point button in the red frame on the screen.
• Then, the following popup will appear, so click “Create Dataset”.
• Set an arbitrary dataset name, data location to default, encryption to the encryption key managed by Google, and click “Create dataset”.

The dataset is now created. Finally, create a table.

Step 5 Create a table

• Click the three-dot button next to the dataset name you just created.
• Then, click the “+” button in the red frame to transition to the following screen.
• Select the source of the data you want to import and click “Create table” and you’re done.

Although there are many steps, it does not require any difficult work, and you can use BigQuery more easily than you can imagine.
Let’s start with a simple analysis, such as sample data, and gradually challenge ourselves to analyze a large amount of data.

Summary

BigQuery is a cheap, easy, and fast tool for big data analysis. Because it is a Google service, it is easy to work with spreadsheets, data portals, GA4, etc., making it a highly scalable service.

In the future, it is expected that the utilization of data accumulated in the company will become more and more important.

Please consider introducing BigQuery as a measure to improve operational efficiency and reduce costs by utilizing big data.