The comparison between Big Data vs Machine Learning

The comparison between Big Data vs Machine Learning
Written by Debarghya DasDecember 3, 2021
9 min read
Machine Learning
Debarghya Das

Junior Front-End Developer

In this blog post, we will know the comparison between Big Data and Machine Learning.

First, what you know about Big Data?

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.


Next, we know about Machine Learning

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.


Key Differences Between Big Data and Machine Learning

Following is the key difference between Big Data and Machine Learning:

Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other. They superimpose each other’s activities and the relationship is best described as mutualistic. It is impossible to see a future with just one of them. But there are still some unique identities that separate them in terms of definition and application. Here’s a look at some of the differences between big data and machine learning and how they can be used.

  1. Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.
  2. Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.
  3. Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the relationship between existing pieces of data with the same depth that machine learning can.
  4. Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to fed to a machine learning system in order to do further analytics for predicting output results.
  5. Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.
  6. Machine learning performs tasks where human interaction doesn’t matter. Whereas, big data analysis comprises the structure and modeling of data which enhances decision-making system so require human interaction.

Comparison between Big Data and Machine

Big Data:

  • Data Use: Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc.
  • Foundations for Learning: Big data analytics pulls from existing information to look for emerging patterns that can help shape our decision-making processes.
  • Pattern Recognition: Big data analytics can reveal some patterns through classifications and sequence analysis.
  • Data Volume: Big data as the name suggest tends to be interested in large-scale datasets where the problem is dealing with the large volume of data.
  • Purpose: Purpose of big data is to store large volume of data and find out pattern in data

Machine Learning:

  • Data Use: Machine learning is the technology behind self-driving cars and advance recommendation engines.
  • Foundations for Learning: On the other hand, Machine learning can learn from the existing data and provide the foundation required for a machine to teach itself.
  • Pattern Recognition: However, machine learning takes this concept a one step ahead by using the same algorithms that big data analytics uses to automatically learn from the collected data.
  • Data Volume: ML tends to be more interested in small datasets where over-fitting is the problem
  • Purpose: Purpose of machine learning is to learn from trained data and predicts or estimates future results.


Big data vs machine learning go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries while there is a lack of skilled professionals; machine learning professionals being in more demand when compared with big data analysts. When it comes to salary, both profiles enjoy similar packages and if you have skills in both of them, you are hot property in the field of analytics.

However, if you do not have the time to learn both, you can go for whichever you are interested in.

Machine Learning
Big Data
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Debarghya Das
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