Open In App

Difference between Big Data and Machine Learning

Improve
Improve
Like Article
Like
Save
Share
Report

In today’s world where information is abundant, big data and machine learning have emerged as transformative forces that have revolutionized various industries and shaped the digital landscape. Although they are sometimes used interchangeably, they are distinct yet interconnected domains that have profound implications. Big data and Machine learning share a symbiotic relationship despite their distinct natures. As we move forward, the interplay between these two technologies will continue to transform our world. The ability to harness the power of Big data and machine learning will be crucial for addressing complex challenges, optimizing decision-making, and unlocking new frontiers of innovation.

Big Data  vs Machine Learning-Geeksforgeeks

Big Data vs Machine Learning

Big Data

Big Data is huge, large, or voluminous data, information, or relevant statistics acquired by large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology. 

What is Big Data?

Big data is a term for data that is too large or complex to be processed by traditional methods. It is characterized by the following four Vs:

  • Volume: Big data is characterized by its enormous volume. For example, Facebook generates over 4 petabytes of data every day.
  • Variety: Big data comes in a variety of formats, including structured, unstructured, and semi-structured data. Structured data is organized in a predefined format, such as a spreadsheet or database. Unstructured data is not organized in a predefined format, such as text, images, or video. Semi-structured data is partially organized, such as XML or JSON.
  • Velocity: Big data is constantly being generated, at a rate that is too fast for traditional methods to keep up. For example, Twitter users generate over 500 million tweets per day.
  • Veracity: Big data can be noisy and incomplete. It is important to clean and prepare big data before it can be used for analysis.
  • Value: Value refers to the usefulness of data for analysis and decision-making.

Machine Learning

Machine learning is a subset of Artificial intelligence that helps to automatically learn and improve the system without being explicitly programmed. Machine learning is applied using Algorithms to process the data and get trained for delivering future predictions without human intervention. The inputs for Machine Learning are the set of instructions data or observations.

What is Machine Learning?

Machine learning is a field of computer science that enables machines to learn from data, without being explicitly programmed. Machines can learn to identify patterns in data, make predictions, and make decisions.

There are three main types of machine learning:

  • Supervised learning: In supervised learning, the machine is trained on a dataset of labeled data. The machine learns to map the input data to the desired output.
  • Unsupervised learning: In unsupervised learning, the machine is trained on a dataset of unlabeled data. The machine learns to identify patterns in the data.
  • Reinforcement learning: In reinforcement learning, the machine learns by interacting with its environment. The machine receives rewards for taking actions that lead to desired outcomes.

How are Big Data and Machine Learning Related?

Big data and machine learning are two sides of the same coin. Big data provides the fuel that machine learning algorithms need to learn. Machine learning algorithms enable us to extract insights from big data that would be impossible to find with traditional methods.

Big Data vs Machine Learning

Difference between Big Data and Machine Learning are as follows: 

Big Data Machine Learning
Big Data is more of extraction and analysis of information from huge volumes of data. Machine Learning is more of using input data and algorithms for estimating unknown future results.
Types of Big Data are Structured, Unstructured and Semi-Structured. Types of Machine Learning Algorithms are Supervised Learning and Unsupervised Learning, Reinforcement Learning.
Big data analysis is the unique way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDB. Machine Learning is the way of analysing input datasets using various algorithms and tools like Numpy, Pandas, Scikit Learn, TensorFlow, Keras.
Big Data analytics pulls raw data and looks for patterns to help in stronger decision-making for the firms Machine Learning can learn from training data and acts like a human for making effective predictions by teaching itself using Algorithms.
It’s very difficult to extract relevant features even with latest data handling tools because of high-dimensionality of data. Machine Learning models work with limited dimensional data hence making it easier for recognizing features
Big Data Analysis requires Human Validation because of large volume of multidimensional data. Perfectly built Machine Learning Algorithms does not require human intervention.
Big Data is helpful for handling different purposes including Stock Analysis, Market Analysis, etc. Machine Learning is helpful for providing virtual assistance, Product Recommendations, Email Spam filtering, etc.
The Scope of Big Data in the near future is not just limited to handling large volumes of data but also optimizing the data storage in a structured format which enables easier analysis. The Scope of Machine Learning is to improve quality of predictive analysis, faster decision making, more robust, cognitive analysis, rise of robots and improved medical services.
Big data analytics look for emerging patterns by extracting existing information which helps in the decision making process. It teaches the machine by learning from existing data.
Problem: Dealing with large volumes of data. Problem: Overfitting.
It stores large volumes of data and finds out patterns from data. It learns from trained data and predicts future results.
It processes and transforms data to extract useful information. Machine Learning uses data for predicting output.
It deals with High-Performance Computing. It is a part of Data Science.
Volume, velocity, and variety of data Building predictive models from data
Managing and analyzing large amounts of data Making accurate predictions or decisions based on data
Descriptive and diagnostic  Predictive and prescriptive
Large volumes of structured and unstructured data Historical and real-time data
Reports, dashboards, and visualizations Predictions, classifications, and recommendations
Data storage, processing, and analysis Regression, classification, clustering, deep learning
Data cleaning, transformation, and integration Data cleaning, transformation, and feature engineering
Strong domain knowledge is often required Domain knowledge is helpful, but not always necessary
Can be used in a wide range of applications, including business, healthcare, and social science Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity

Conclusion

Big data and machine learning are both powerful tools with their own strengths and weaknesses. Big data is better for storing and analyzing large datasets, while machine learning is better for making predictions and insights from data. They are complementary technologies, and each one enhances the capabilities of the other.

Frequently Asked Questions(FAQ’s)

1. Which is better big data or machine learning?

Both big data and machine learning are powerful tools with their own strengths and weaknesses. Big data is better for storing and analyzing large datasets, while machine learning is better for making predictions and insights from data.

2. Which one should you choose to learn?

If you are interested in data analysis and want to learn how to extract insights from large datasets, then big data is a good choice. If you are interested in artificial intelligence and want to learn how to build algorithms that can learn from data, then machine learning is a good choice.

3. Is big data part of ML?

No, big data is not part of machine learning. Machine learning is a subfield of artificial intelligence that focuses on developing algorithms that can learn from data. Big data is the raw material that machine learning algorithms learn from.

4. What are the 5 V’s of big data?

The 5 V’s of big data are:

  • Volume: The amount of data
  • Variety: The different types of data
  • Velocity: The speed at which data is generated and collected
  • Veracity: The accuracy and quality of data
  • Value: The usefulness of data for analysis and decision-making

5. Can I use big data without machine learning?

Yes, but it is often difficult to extract meaningful insights from big data without the help of machine learning algorithms. Machine learning can be used to automate the process of analyzing big data and to identify patterns that would be difficult to find manually.



Last Updated : 07 Dec, 2023
Like Article
Save Article
Previous
Next
Share your thoughts in the comments
Similar Reads