Big Data and Machine Learning – Journey as Beautiful as Sunset

Big Data and Machine Learning are the two hot topics in today’s IT world. In this article, we will study the relationship between the two.

The article first gives a short introduction to Big Data and Machine Learning. Then we will see the relationship and difference between machine learning and big data. At last, we will explore some of the use cases of Big Data with Machine Learning.

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What is Big Data?

Big Data refers to the vast amount of data that we cannot handle with the traditional database systems.

It is defined by 5V’s which refers to the volume of data (Volume), different types of data (Variety), the velocity at which the data must be processed (Velocity), the data quality and consistency (Veracity), and the end-stage is extracting the useful data (Value).

Big data analysis is the process of collecting and analyzing Big Data. It helps in discovering important hidden patterns and other information such as market trends, customer choices, etc.

These are very beneficial for the organizations to remain informed and take customer-oriented business decisions.

The main challenges while dealing with Big Data includes capturing, storing, processing, transforming, analyzing, sharing, and visualizing Big Data.

What is Machine Learning?

In simple words, we define machine learning as “Evolve through Learning”.
Machine learning is the branch of computer science that allows machines to learn from past experiences without explicitly doing the programming.

Thus, instead of writing code, we just feed data to the generic algorithm, and the algorithm itself builds the logic based on the given input data.

It helps computers/machines in predicting the future without the intervention of humans.
We can say that with the help of ML, software applications learn how to improve their accuracy to predict the outcomes.

Hence, Machine learning enables machines to learn from data, find out useful hidden patterns, and make decisions without human intervention.

Machine Learning in Big Data

Machine Learning algorithms are useful for data collection, data analysis, and data integration. ML algorithms are a must for the larger organizations which are generating tons of data.

We can apply ML algorithms to every element of Big data operation, including:

  • Data Labeling and Segmentation
  • Data Analytics
  • Scenario Simulation

All these stages are integrated for generating insights, patterns, which are then categorized and packaged into an easily understandable format. The fusion of Big Data with Machine Learning is a never-ending loop.

Relationship between Big Data and Machine Learning

It is always better to have several varieties of data to get them filtered for generating accurate results. But managing these wide varieties of data is very difficult. So it becomes a challenge to manage and analyze Big Data. Also, information is useless until it is well interpreted.

Thus, to use information, there is a need for talent, algorithms, and computing infrastructure.

Machine learning enables machines to use the data provided by Big Data and respond accurately thus leading to improved service quality, business operations, customer relationships, and more.

Machine learning algorithms take data from Big Data and learn more. Big data analytics provide varieties of data to the machines to show and give better results.

Thus, Businesses can fulfill their dreams and get the advantages of big data by using machine learning algorithms but with the help of skilled data scientists to run that data into knowledge.

Difference between Big Data and Machine Learning

1. Big data is related to data storage, ingestion & extraction tools such as Apache Hadoop, Spark, etc. whereas, Machine learning is a subset of AI that enables machines to predict the future without human intervention.

2. Big data is the analysis of vast amounts of data by discovering useful hidden patterns or extracting information from it. Thus, Big data is huge information analytics where we perform analysis on huge information. While Machine learning teaches computers to take input data and give desired outputs based on the machine learning models.

3. Big data analytics is all about collecting and transforming raw data into extracted information, and this data information is then used by the Machine Learning algorithms to predict better results.

4. Machine Learning is a part of Data Science while big data is related to high-performance computing.

5. Machine learning processes data and generates output without human intervention whereas big data analysis involves human interaction.

6. We can set up both Machine Learning and Big data to automatically look for particular types of data, parameters and relationship between them. But Big data can not see the relationship between existing pieces of data and parameters with the same depth as machine learning.

Big Data and Machine Learning Use Cases

The fusion of Machine Learning and Big data is the reason behind the growth of many industries. Here we have listed some use cases of machine learning and big data.

1. Market Research and Target Audience Segmentation

In order to gain profits, knowing the audience is one of the most critical elements for a profitable business. Machine learning algorithms study the market and help business organizations to understand their target audience.

By using supervised and unsupervised ML algorithms, organizations can find out the portrait of their target audience, patterns of their behavior, and their preferences. This technique is used in eCommerce, Media & Entertainment, Advertising, and in many other domains.

2. User Modeling

It is an elaboration on Target Audience Segmentation. User Modeling dwells inside the user behavior and creates a detailed portrait of a specific segment. By using machine learning algorithms for big data analytics, we can predict users’ behavior and make intelligent business decisions.

Facebook is an example of such a user modeling system. It creates a detailed portrait of the user for suggesting friends, pages, communities, ads, etc.

3. Recommendation Engine

Recommendation Engine is the best use case of Big Data with Machine Learning. This system provides the best suggestions for the types of products to be brought together, contents the user might be interested to read or see.

Based on the combination of context and user behavior prediction, this system can shape user experience according to the user’s expressed preferences and behavior on the site.

Recommendation engines apply content-based data filtering for extracting insights. Thus, the system learns from the user’s preferences and tendencies.

Amazon and Netflix popularly used Recommendation engines.

4. Predictive Analysis

Big Data with machine learning plays a vital role in shaping the bright future of retail industries. For retail, knowing customers’ needs is one of the most important elements. Thus they use Market Basket Analysis.

Big data allows retailers to calculate the probabilities of different outcomes and decisions. Predictive Analytics helps them by providing suggestions for extra products on eCommerce platforms.

eBay’s system is an example of predictive analysis that reminds us of abandoned purchases, incoming auctions, or hot deals.

5. Chatbots

Chatbots also are known as the Conversational User interface is another most important use case of Big Data with Machine Learning. By using machine learning algorithms, a chatbot can easily adapt to a particular customer’s preferences after interactions.

Amazon’s Alexa and Apple’s Siri are the most well known AI assistants.


In short, we can say that Big Data and Machine learning are different from each other but these two hot trending technologies are used in combination for a successful business.

The input to the Machine learning algorithms is the information extracted by big data analysis. This input is then learned by the machine learning models to predict desired outputs.

The article enlisted various Big Data with Machine Learning use cases like Recommendation engines, chatbots, User modeling systems, etc.

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