Getting Started with Big Data Analysis

Writer : Mr. George Miguel

Big data Analytics is a field that focuses on analyzing and extracting information from large amounts of data in order to arrive at accurate conclusions. These findings can be used to forecast the future or predict the business's future success. In addition, this helps to establish a trend in the past. The sheer volume of data necessitates the use of statisticians and engineers with deep domain expertise in order to properly analyze it. Traditional methods of analysis are inadequate for dealing with this data's complexity.

Three Vs can be used to describe Big Data.

  • In computing, volume refers to the amount of data generated each second. Organizations like social media, e-commerce, and airlines collect vast amounts of data on a daily basis, which they use to make decisions.
  • The rate at which the data is created. There will be a lot of data generated every second because people post comments, like photos, share videos, etc. on social media, which is used by everyone.
  • A wide range of data types can be used, including structured data like numbers and graphs, unstructured data like text and other non-numerical media, and semi-structured data like JSON or XML, among other things.

What are we going to do with all of this Big Data?

Big data can be processed and meaningful insights can be gleaned from it. Big data can be processed using a variety of frameworks. Big data developers and analysts frequently use the frameworks in the following list.

  • We can write a map-reduce program to process the data using Apache Hadoop.
  • For example, we can use spark programming to process the data; we can also process a live stream of data using this technology.
  • This is Apache Flink.

Big Data Analytics

Analyzing big data involves collecting, organizing, and deciphering large amounts of data in order to discover hidden patterns, correlations, and other meaningful insights. Improved operations, higher profits, and happier customers are all the results of effective data analytics. It helps an organization make sense of the information contained in its data and put it to good use.

Data scientists, predictive modelers, statisticians, and other analytic professionals can use Big Data analytics applications to sift through the ever-increasing amount of structured and unstructured data. Specialized software and applications are used to accomplish this task. Data mining, text mining, predictive analysis, forecasting, etc. can all be carried out using these tools; all of these processes are carried out separately and are part of high-performance analytics. A large amount of data can be processed with the help of Big Data analytic tools and software, allowing an organization to make better business decisions in the future.


Big data analytics relies on a number of key technologies.

It's all about getting the most valuable information out of your data using a variety of technologies.

1. Hadoop

Using commodity hardware and the open-source framework, large amounts of data can be stored and various applications run. Due to the ever-increasing variety and volume of data, it has emerged as a key technology for big data, and its distributed computing model allows for faster access to data.

2. Data Mining

For complex business questions, you can use data mining techniques to uncover patterns in the information that can be used for further analysis and data mining. It is possible to sift through all the noise and repetitive data to find only the relevant information that is needed to make informed decisions.

3. Text Mining

Text mining allows us to analyze data from the web, such as comments, likes on social media, and other text-based sources, such as email; we can determine if a message is spam. Analysis of a large amount of data using technologies such as machine learning or natural language processing is the goal of Text Mining.

4. Predictive Analytics

Machine learning and statistical algorithms are used to predict future outcomes based on historical data using predictive analytics. Organizations must have confidence in their current business decisions in order to provide the best future outcomes.

Analytics for Big Data: The Advantages

Big Data Analytics is becoming increasingly popular in a variety of industries. Data analytics is increasingly being used by businesses in the e-commerce and social media industries as well as the healthcare and banking industries, as well as the entertainment industries.

Take the e-commerce industry as an example:

Amazon, Flipkart, Myntra, and a slew of other e-commerce sites rely on big data.

A variety of techniques are employed to gather information about their customers.

  • Gather information on the items that the customer is looking for.
  • Specifics on what they like and don't like.
  • Information on the products' popularity and a slew of other facts.

Organizations can identify patterns in this type of data and provide the best possible service to their customers, such as

  • showcasing the most sought-after goods on the market.
  • The customer's purchases should be displayed in a way that shows the products that are similar.
  • Provide safe money transfers and identify if there are any fraudulent transactions taking place at any time.
  • Foresee how many people are going to buy the products and more.


Big Data has the potential to fundamentally alter the way businesses operate. Many companies are relying more heavily on data analytics to make strategic decisions and provide a better experience for their customers. Because even the tiniest improvement in efficiency or cost-savings can have a huge impact, many companies are turning to big data.

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I hope you've found this guide to Big data analytics useful. We've covered the basics here, including what Big data Analytics is, its advantages, and the technology that powers it. You can also check out this article for more information –

Information vs. information based on data


Consider the difference between "data" and "information." There is no special significance to data in and of itself. It's nothing more than a haphazard jumble of facts and figures. Structured, unstructured, and semi-structured data are all types of data. Information is what happens when raw data is organized, analyzed, and presented in a meaningful way.

To put it another way, information can be derived from data and used to make conclusions. The information is not dependent on the data, but the opposite is true. Data has more meaning and value when it is contextualized.

Data, such as a list of dates, is pointless. Because it's a list of holidays, the data has more meaning.

Many people confuse data analysis and data mining, just like they confuse data and information.

This process of finding patterns and trends in data relies on mathematical and scientific models. However, data analysis relies on analytics models and business intelligence software. Many people believe that data mining is only a subset of data analysis.


The importance of big data analytics can't be overstated.

Today's world is heavily reliant on data.

Each year, the amount of data being generated grows exponentially, and our brains will struggle to comprehend it. As a comparison, the volume of data expected to be generated in 2023 is nearly three times what was created in 2019.

For businesses, big data analytics cannot be ignored. Having a competitive advantage and being able to predict the future of the market is essential to success. BDA helps companies better understand their customers and find new ways to increase their lifetime value.

It is possible to reduce operational costs and find ways to increase efficiency by using big data technologies such as Hadoop. They can make decisions faster and better than humans, and they also have a better grasp on the wants and needs of customers.

After an event has occurred, traditional data analytics methods are used. Data can be collected and processed almost instantly in big data analytics because the analysis can be historical or real-time. In the healthcare industry as well as in manufacturing, transportation, and e-commerce, BDA is able to make significant strides because of this characteristic.


Analysis methods for large datasets

Data science relies on data as its primary raw material.

In addition to serving as evidence, historical data also aids data scientists in the creation of narratives. Using anecdotes like these, companies can make more informed decisions that aren't based solely on gut feelings. As a matter of fact, the BDA encourages businesses to rely on facts rather than emotions.

There are four types of big data analytics: descriptive, diagnostic, predictive and prescriptive.

The picture painted by all analytics isn't always the same. Depending on the data they provide, and the decision-making processes they support, they can answer a wide range of questions. Extracting information from large datasets is the primary goal of all four big data analytics.


1. Descriptive analytics

In other words, descriptive analytics tells us exactly what took place.

When it comes to data analytics, descriptive analytics is the most common and most elementary form of the practice. It provides a snapshot of a specific period of time in history. Historical data analysis is used to understand how a business has evolved over time.

By providing context, descriptive analytics aids businesses in gaining a better understanding of their own performance. Big data analytics relies heavily on data visualization.

It is possible to identify a company's strengths and weaknesses using descriptive analytics. Descriptive analytics can be used in a variety of ways.


2. Diagnostic analytics

Using diagnostic analytics, we can figure out what happened and why.

Using advanced analytics, it can provide valuable business information and uncover the reasons for specific results. Drill-down, data mining, data discovery, and correlations are some of the most common techniques used for this purpose. With this type of analysis, you'll get no useful information.

Analyzing the underlying causes of a problem is another term for diagnostic analysis. It's a common practice to look for patterns and connections in data. You can use it to determine what factors and events led to a specific result. Diagnostic analytics, for example, can help you figure out why sales rose or fell in a particular month based on a time series of sales data.


3. Predictive analytics

The question of "what is likely to happen?" is answered by predictive analytics.

When it comes to predictive analytics, fortune-telling is a good analogy, but without the guesswork. When it comes to large-scale data analysis, things get a little more complicated at this point. Predictive analytics, aided by AI and machine learning, can provide organizations with information on what is most likely to occur.

However, it is important to keep in mind that this type of data analytics cannot predict the future. Instead, it merely predicts the likelihood that a specific event will take place.

Customers who are at risk of leaving can be identified using predictive analytics. Allowing companies to take specific actions such as rewarding customers for their loyalty is a result of this.


4. Prescriptive analytics

Prescriptive analytics answers the question of "how to do it."

Rather than simply predicting the occurrence of a certain event using predictive analytics, prescriptive analytics recommends specific steps to take in order to achieve a specific goal. It also helps to identify and avoid certain activities that could lead to future problems.

When determining the fastest route, Google Maps uses prescriptive analytics, which takes into account variables such as current traffic conditions, the travel time required, and the mode of transportation being used.

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