Table of contents
1.
Introduction
2.
TECHNIQUES FOR ANALYZING BIG DATA
2.1.
A/B testing
2.2.
Association rule learning
2.3.
Classification
2.4.
Cluster analysis
2.5.
Crowdsourcing
2.6.
Data fusion and data integration
2.7.
Data mining
2.8.
Ensemble learning
2.9.
Genetic algorithms
2.10.
Machine learning
2.11.
Neural networks
2.12.
Network analysis
2.13.
Optimization
2.14.
Sentiment analysis
2.15.
Spatial analysis
2.16.
Statistics
2.17.
Time series analysis
2.18.
Visualization
3.
BIG DATA TECHNOLOGIES
3.1.
Big Table
3.2.
Cassandra
3.3.
Cloud computing
3.4.
Data warehouse
3.5.
Distributed system
3.6.
Extract, transform, and load (ETL)
3.7.
Hadoop
3.8.
MapReduce
3.9.
Stream processing
4.
Frequently Asked Questions
4.1.
What are the Five V’s of Big Data?
4.2.
What is ETL?
4.3.
List some challenges that come with Big Data.
5.
Conclusion
Last Updated: Oct 29, 2024

Techniques for Big Data Analysis

Author vishal teotia
1 upvote
Career growth poll
Do you think IIT Guwahati certified course can help you in your career?

Introduction

Data drives the world today, and it's being analyzed every second, whether it's through your phone's Google Maps, your Netflix habits, or what's in your online shopping cart - in many ways, data can't be avoided, and it's very disruptive.

Analytics, or data analysis, refers to the examination of data sets and drawing conclusions about the information they contain, most commonly through the use of programs, software, and methods. Across commercial business industries, data analytics technologies are used at an industrial scale for the purpose of making calculated, informed business decisions.

The word 'Big Data' refers to the use of specialized techniques and technologies to process enormous amounts of data. It isn't easy to process these large and complex data sets using traditional database management tools. There are many examples, including weblogs, call logs, medical records, surveillance footage from the military, photos, and videos, and large-scale e-commerce sites.

We consider a very large dataset to be one that requires a minimum of one terabyte - if not hundreds of petabytes - of storage (1 petabyte equals 1024 terabytes). In terms of pictures and videos, Facebook alone is thought to store at least 100 petabytes.

TECHNIQUES FOR ANALYZING BIG DATA

There are several methods that can be used to analyze datasets based on disciplines such as computer science (particularly machine learning) and statistics. Our list of techniques covered in this section covers a range of industries. Please be aware that our list is by no means exhaustive. In fact, researchers are constantly developing new strategies and improving existing ones, primarily because of the need to analyze new combinations of data. There is no need for all of these techniques to be applied to big data—some can make good use of smaller datasets (for example, A/B testing, regression analysis). All of the techniques listed here can be applied to big data. In general, larger datasets with more diverse characteristics will generate more relevant results than smaller ones with fewer characteristics.  

A/B testing

A method in which a control group is compared with a variety of test groups in order to determine what changes will improve a given objective variable, e.g., marketing response rate. A/B testing is sometimes called split testing or bucket testing. You can use this to determine what type of copy, layout, image, or color will increase conversion rates to a Website for e-commerce. Big data enables a wide range of tests to be initiated and analyzed, ensuring that groups are sizeable enough to detect meaningful (i.e., statistically significant) differences between the control and treatment groups. If more than one variable is simultaneously manipulated in the treatment, the multivariate generalization of this method, which applies statistical modeling, is called "A/B/N" testing.

Association rule learning

A set of methods for finding interesting relationships, i.e., "association rules," among variables in massive databases. To generate and test these rules, different algorithms are used. An example is market basket analysis, in which a retailer determines which products are frequently purchased together and uses that information for marketing (a commonly cited example is discovering that many supermarket shoppers who buy diapers also likely tend to purchase beer). Used for data mining.

Classification

These techniques are used to identify categories in which new data points belong based on data points that have already been categorized in a training set. It can be used, for instance, to predict segment-specific customer behavior (e.g., buying decisions, churn rate, consumption rate) for which there is a clear hypothesis or objective outcome. Used for data mining.

Cluster analysis

Statistical method for classifying objects based on similarities among diverse groups of objects, but without knowing in advance what characteristics make them similar. Segmenting consumers into similar groups for targeted marketing is an example of cluster analysis. Used for data mining

Crowdsourcing

Crowdsourcing is a method for collecting data submitted by a large group of people or crowd through an open call, usually through a networked medium such as the Internet. In this case, you're utilizing Web 2.0 technology and mass collaboration.

Data fusion and data integration

The process of integrating and analyzing data from multiple sources in order to uncover insights in a more efficient and accurate manner than if they were developed from just one source.

Data mining

Statistical and machine learning approaches are combined with database management to extract patterns from large datasets. These techniques include cluster analysis, association rule learning, regression, and classification. Customer data can be analyzed to identify segments that respond most quickly to an offer, employee data can be analyzed to identify attributes of the most successful employees, or market basket analysis can be used to predict what customers will purchase.

Ensemble learning

Multiple predictive models (each constructed using statistics and/or machine learning) are used to achieve better performance than any of the constituent models.

Genetic algorithms

This optimization technique involves encoding potential solutions as "chromosomes" that can combine and mutate in the same way as a natural evolution. Every chromosome is selected for survival within the context of a modeled "environment," determining its fitness or performance. These algorithms are often described as types of "evolutionary algorithms," which are ideal for solving nonlinear problems. A few examples include optimizing the performance of an investment portfolio and improving job scheduling in manufacturing.

Machine learning

Data-driven machine learning focuses on automatically recognizing complex patterns and making intelligent decisions from them. 

Neural networks

Finding patterns in data using computational models inspired by the structure and workings of biological neural networks (such as the cells and connections found in the brain). Finding nonlinear patterns is a good application for neural networks.

Network analysis

An analysis technique for describing relationships among discrete nodes in a graph or a network. The social network analysis aims to investigate the connections among individuals in a group or organization, for example, how information moves or who has the most influence. The technology can be used, for instance, to identify key opinion leaders to target in marketing and to identify bottlenecks in information flows within enterprises.

Optimization

Numerical methods for redesigning complex systems and processes to improve their performance based on one or more objective metrics (e.g., cost, speed, or reliability). Among the applications for optimization are improving operational processes such as scheduling, routing, and floor planning, as well as formulating strategies such as product range strategy, linked investment analysis, and R&D portfolio strategy. 

Sentiment analysis

The process of extracting and identifying subjective information from a text source using natural language processing and analytic techniques. This type of analysis includes identifying the product or feature about which a sentiment is expressed, determining the type of sentiment (e.g., positive, negative, neutral), and determining the degrees and strength of sentiment. The application of sentiment analysis in social media (e.g., blogs, microblogs, and social networks) may allow companies to measure how different customer segments and stakeholders are responding to their products and actions.

Spatial analysis

An analysis of topological, geometric, or geographical properties encoded in a data set using various techniques drawn from statistics. Spatial analysis often uses geographical information systems (GIS) that capture locational information, e.g., address and longitude/latitude coordinates. Spatial data can be incorporated into spatial regressions (for instance, how is consumer willingness correlated with location?) or simulations (for example, how would a manufacturing supply chain network work with sites located in different places?).

Statistics

This includes the study of survey methods and experiments, as well as how data is collected, organized, and interpreted. It is common for statistical techniques to be used to determine what relationships between variables could have been the result of chance (the "null hypothesis") and which relationships likely result from underlying causal relationships (i.e., those that are statistically significant).

Time series analysis

Combining statistical and signal processing techniques for analyzing sets of data points, each representing a value at a different time, in order to extract properties that are meaningful. For example, a time series analysis might look at the daily price of stock market or amount of patients diagnosed with a given condition on a daily basis. Forecasting time series involves using a mathematical model to predict future values of the series based on data from past values.

Visualization

Often used to synthesize data analysis results using images, diagrams, or animations in order to communicate a message.

BIG DATA TECHNOLOGIES

Various technologies are available for aggregating, manipulating, managing, and analyzing big data. The list we have provided here contains some of the most prominent technologies, but not all, especially since more technologies continue to be developed for big data applications, some of which we have listed.

Big Table

Based on the Google File System, a proprietary distributed database system. Inspiration for HBase.

Cassandra

Database management system composed of open-source programs designed to handle large volumes of data over a distributed network. Initially developed at Facebook, this system is now managed by the Apache Software Foundation.

Cloud computing

In this paradigm, highly scalable computing resources, usually configured as distributed systems, are provided as services over the network.

Data warehouse

A database designed specifically for reporting is usually used to store large volumes of structured data. ETL (extract, transform, load) tools are used to upload data from operational data stores, and business intelligence tools are often used to generate reports.

Distributed system

Multiple computers, communicating through a network, are used to solve a common computational problem. Computers working in parallel solve each of the problems divided into multiple tasks. A distributed system provides better performance at a lower cost (i.e., because a cluster of lower-end computers can be cheaper than a single higher-end computer), as well as greater reliability (since there is no one point of failure) and scalability (since a distributed system can be made more powerful by simply adding more nodes rather than replacing a central computer).

Extract, transform, and load (ETL)

Extracting data from the outside sources, transforming it to fit operational needs, and storing it in databases or data warehouses.

Hadoop

The software framework used to process large datasets on a distributed system for particular types of problems. MapReduce framework and Google File System provide inspiration for the development of this system.

MapReduce

Google introduced a software framework for processing large datasets on certain types of problems on a distributed system.

Stream processing

Technology for processing large streams of event data in real-time. In addition to algorithmic trading, stream processing enables applications including RFID event processing applications, criminal analysis, process monitoring, and location-based services in telecommunications.

You can also consider our Data Analytics Course to give your career an edge over others.

Frequently Asked Questions

What are the Five V’s of Big Data?

The five V’s of big data are Variety, Volume, Veracity, Velocity, and Value.

What is ETL?

ETL stands for extract, transform and load.

List some challenges that come with Big Data.

Big data has many problems and challenges, such as capturing, searching, analyzing, transferring, and extracting valuable insights from big data.

Conclusion

Aside from techniques and technologies, data of any size or form is valuable. When managed correctly and effectively, it can reveal a wealth of information about a company's business, products, and markets. Where does data analysis go from here? With the rapid progress of analytics and technology, it is difficult to say, but it is evident that data innovation has revolutionized business and society as a whole.

 

 

Live masterclass