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What is Data Visualization?

Which image gives you a better idea of the data represented? Most people would get a clear view of the second image. This is something data visualization is all about. It is merely the representation of data in graphical form.




What is Data Visualization

“There is magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.” – Henry D. Hubbard

Whenever you are given a lot of information, you may be converting it into tabular form to get an easy understanding. Data is essential only when you can get insights from it, and else it is redundant.

Which image gives you a better idea of the data represented? Most people would get a clear view of the second image. This is something data visualization is all about. It is merely the representation of data in graphical form.

If we have a small data set, understanding it in a tabular form is secure. But if the amount of data is enormous, assuming it becomes too complicated, hence a need to visualize it. By using representations like charts, graphs, or maps, you get an accessible way to understand the trends and patterns in the data.

It is a personal tendency to get attracted to colors and patterns rather than standard text. The human brain is quick to differentiate between green and red, or square and circle. Visual effects create a better impression on our minds.

For example, a process explained through animations is understood quicker compared to that described in a book. This is why data visualization is essential for people as well as businesses.

Today, companies believe in making data-driven decisions as it allows them to meet customer needs and develop best-in-class products.

Rising technologies like Artificial Intelligence, Machine Learning, and Data Analytics have made data visualization more critical than ever.

Data visualization helps in converting trillions of rows of data generated into a form that is easy to understand, and through which we can find growth patterns, profit/loss, Return on Investment (ROI), total sales, and a lot more information that can be conveyed to stakeholders.

Read on to know why data visualization is essential, its types, data visualization tools, and why taking Tableau course training should be your next career move.

1. Importance of Data Visualization

Whether you are working in retail, banking and finance, marketing, sales, education, eCommerce, or even in governmental organizations, data visualization is crucial to communicate information clearly and efficiently. It is also one of the steps in the process of data science.

The data science process involves collecting data, processing, and modeling it, and after that, the data should be visualized to draw conclusions and make better decisions.

Data Visualization has witnessed continuous growth over the past few years. The global data visualization market which was worth USD 4.51 billion in 2017 is expected to reach USD 7.76 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 9.47 percent over the forecast period of 2018 to 2023.

The driving factor of data visualization is the fact that there is an increasing emphasis on measuring the performance of every operation to check the daily progress.

2. Here are some of the points depicting the importance of data visualization:

Traditional descriptive statistics are not capable of giving insights from the massive amount of data that we generate today. It is a data visualization that gives improved insights.

Going through spreadsheets or reports is a tedious task that delays decision-making. Data visualization allows for quicker decisions by presenting complex data through interactive dashboards and graphs.

Data visualization can also be used to identify areas that need improvement, understand which factors affect consumer behavior, predict sales volume, and more.

Companies today are focusing more on big data and data analysis-related projects, and data scientists take the help of data visualization to ensure that the process is giving desired results.
Data visualization also gives an increased understanding of the next steps to be taken to improve the processes.

3. Types of Data Visualizations

Data can be visualized in various ways:

  • Charts
  • Tables
  • Maps
  • Infographics
  • Graphs
  • Dashboards

These mentioned ways again have sub-types like line charts, pie charts, treemaps, histograms, bullet graphs, dot distribution maps, heat maps, population pyramids, Gantt charts, and more.

4. Data Visualization Tools

Data scientists can achieve data visualization through many powerful tools:

  • Tableau
  • Qlikview
  • Plotly
  • Power BI
  • Datawrapper
  • Highcharts
  • Sisense

Tableau is one of the numerous robust data visualization tools which can prove to be the most secure and flexible end-to-end analytics platform for your data. Here are the products that it offers:

Tableau Prep – It can be used to quickly combine, shape, and clean data and make it ready for analysis

Tableau Desktop – This is where the analytics happen. There are interactive dashboards to help you uncover hidden insights.

Tableau Online – This analytics platform is cloud-based, and you can access it anywhere or share the dashboards with stakeholders or clients.

Tableau Server – It allows the organization to run the tool in a trusted environment where the data and analytics are secured, and employers have the freedom to explore data.

Now that you know why data visualization is essential get ready to learn it. If you continue beginning a career in the field of Artificial Intelligence, Machine Learning, Data Science, or Big Data Analytics, then it is a must-have skill.

You can take a Tableau online course to gain hands-on experience with this powerful tool. Industry experts often make online courses, and they not only train you to design interactive dashboards but will also help you visualize data more effectively.

We are an Instructor, Modern Full Stack Web Application Developers, Freelancers, Tech Bloggers, and Technical SEO Experts. We deliver a rich set of software applications for your business needs.

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Big Data

How to Work with Strings in Google BigQuery?

How to Work with Strings in Google BigQuery? Key Features of Google BigQuery, BigQuery String Functions, BigQuery Substring Function




How to Work with Strings in Google BigQuery

Organizations nowadays produce a massive amount of data, simply storing and organizing, which is not enough and serves no purpose. It, therefore, becomes essential for businesses not just to collect and store the data but also to analyze it to derive valuable business insights.

Still, there comes a challenge of managing, maintaining, and analyzing these exponentially growing data using the outdated data warehouse technologies and here comes the role of Google BigQuery, one of the well-known and widely accepted Cloud-based Data Warehouse Applications, which allows you to trawl through vast amounts of data and find the correct data for analysis.

It offers various functions such as BigQuery Create Table, which helps store the records, BigQuery Substring, which eases down the complex calculations, and many others.

In this article, we will introduce you to Google BigQuery and its key features? We will also have an overview of the different BigQuery String Functions and BigQuery Substrings and how to work with them.

1. What is Google BigQuery?

Google BigQuery is a cost-effective enterprise data warehouse solution and part of Google Cloud’s comprehensive data analytics platform for business agility.

It helps businesses manage and analyze the data with the help of inbuilt features like Machine Learning, Business Intelligence, and Geospatial Analysis.

Google BigQuery’s serverless architecture allows high-scale operations and execution of SQL queries over large datasets.

It is an enterprise-ready cloud-native data warehouse that covers the whole analytics ecosystem, including ingestion, processing, and storage of data, followed by advanced analytics and collaboration, enabling scalable analysis of the stored data.

2. Key Features of Google BigQuery

Key Features of Google BigQuery

Given below are some of the key features of Google BigQuery: –

  • Scalability – Google BigQuery is quite elastic in that it manages a vast amount of data and allows upscale or downscale per demand.
  • Automated Data Transfer – Google BigQuery supports automated data transfer through the BigQuery Data Transfer Service, which automates data movement into BigQuery regularly.
  • Real-Time Analytics – Google BigQuery facilitates the analysis of high-volume data in real-time.
  • User-Friendly Interface – BigQuery is a highly user-friendly platform and requires just a basic understanding of SQL commands, ETL tools, etc.
  • Multicloud Functionality – Multicloud Functionality is another feature of Google BigQuery which allows data analysis across multiple cloud platforms. BigQuery can compute the data at its original location without moving it to different processing zones.

3. BigQuery String Functions

Strings are a crucial part of the dataset whose manipulation and transformation significantly impact your analysis. There are various functions to modify and transform the Strings in Google BigQuery. Let us have a look at some of the essential BigQuery String Functions: –


The CONCAT function helps to combine two or more strings to provide a single result. Here all the values must be Bytes or Data Types; if any of the input arguments is null, then the function will return the null value.

Syntax: –

CONCAT(‘A’, ” “, “B”)


The TRIMMING function removes any particular character from the String. Trimming operations are of three types: –

c) TRIM (value1[, value2]):

TRIM removes all the leading and trailing characters that match value2. In case no character is specified, whitespaces will be removed by default.

d) LTRIM (value1[, value2]):

LTRIM Function removes the character specified from the left, and similar to the TRIM Function, if a character is not defined, it will remove the whitespaces by default.

e) RTRIM (value1[, value2]):

RTRIM Function removes the character specified from the right side, and again if no character is defined, then whitespaces will be removed by default.

Example: –

‘Original String_’,
TRIM(‘ Original String_’) AS trimmed,
LTRIM(‘ Original String_’) AS left_trim,
RTRIM(‘ Original String_’, “_”) AS right_trim


The REPLACE function can replace all the substrings within a string with new substrings.

Example: –

SELECT complaint_description,REPLACE (complaint_description,’Coyote’,’doggy’)as replaced_value FROM `bigquery-public-data.austin_311.311_service_requests` LIMIT 5
Here “Coyote” will be replaced with “doggy”.


CASE functions are used to change the case of a particular string, and they are of two types LOWERCASE and UPPERCASE.


LOWERCASE can be used to return the original String with all the alphabetic characters in the lower case for string arguments.

Syntax: –



UPPERCASE can be used to return the original String with all the alphabetic characters in the upper case for string arguments.

Syntax: –


4. BigQuery Substring Function

BigQuery Substring Function helps to extract a section of the String in BigQuery. It helps make calculations and visualizations easier for the users and can be used in conjunction with other BigQuery parameters, which can help enhance the performance.

Syntax: –

SUBSTR (value, position[, length])


In this article, we discussed Google BigQuery and the key features that make it useful for businesses. We also discussed different BigQuery String Functions, which can be used to transform and manipulate strings in BigQuery, such as CONCAT, TRIMMING, etc., along with their syntax.

In the end, we will also discuss the BigQuery Substring function, which helps extract a section of the String in BigQuery and thus helps enhance the performance.

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