Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Big Data
3.
Big Data Analysis View
3.1.
Decision Oriented
3.2.
Action Oriented
4.
Characteristics of Big Data Analysis
4.1.
Velocity
4.2.
Value
4.3.
Volume
4.4.
Variety
4.5.
Veracity
4.6.
Validity
4.7.
Volatility
4.8.
Visualization
4.9.
Some other features of Big Data Analytics
5.
Frequently Asked Questions
5.1.
What are the different types of Big Data?
5.2.
What are some main components of Big Data?
5.3.
What are the advantages of Big Data?
5.4.
Write some Big Data processing techniques?
6.
Conclusion
Last Updated: Mar 27, 2024
Easy

Characteristics of Big Data Analysis

Author Anjali
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

In this article, we will be discussing a crucial concept related to Big Data. We will introduce Big Data and then look at the perspectives of viewing Big Data and the different characteristics of Big Data Analysis.

Big Data Analytics refers to collecting and analyzing a large amount of data to generate the actionable information that an organization uses to improve its various aspects. Understanding the characteristics of Big Data Analytics will also help us to understand the advanced concepts of this subject.

Big Data

Big Data is among the most burning terms in the tech sector. If you’ve been maintaining up with the industry trend, then you must have heard of Big Data. Government organizations, healthcare providers, businesses, and many other enterprises focus on using Big Data to improve their operations and drive their growth.

Big Data allows companies and organizations to use large amounts of data effectively. It enables organizations to identify trends, patterns, and associations that would be pretty challenging or nearly impossible to find with conventional data processing solutions. As a result, there is a high demand for big data professionals. Yet, if you want to pursue a career in this field, you must first familiarize yourself with the characteristics of Big Data and its fundamentals and move control to well-tested, built-in APIs.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Big Data Analysis View

Big data Analysis should be viewed from two perspectives:

Decision Oriented

Decision-oriented analytics is more akin to standard Business Intelligence. We look at particular subsets and representations of larger data sources and try to apply the results to the business decision-making process. Indeed, these decisions may result in some type of action or process change, but the goal of the analysis is to increase decision making.

Action Oriented

Action-oriented analytics is used for rapid response when a pattern emerges, or specific types of data are detected, and action is required. We discuss these types of use cases throughout the book, but this is where "the rubber meets the road." Harnessing big data through analytics and triggering proactive or reactive behaviour changes to deliver the huge potential for early adopters.

Characteristics of Big Data Analysis

The following are the main characteristics of big data. Understanding the characteristics of big data is essential to know how it works and how you can use it. There are mainly seven characteristics of big data analysis :

Velocity

Velocity refers to the speed of the data processing. High velocity is essential for the performance of any big data process. This consists of the rate of change, activity bursts, and the linking of incoming data sets.

Value

Value refers to the benefits that the organization derives from the data. Does it match the organization's goals? Does it help the organization enhance itself? It is among the most important characteristics of big data core?

Volume

Volume refers to the quantity of the data that you have. We measure the volume of our data in GigabytesZettabytes (ZB), and Yottabytes (YB). According to industry trends, the volume of data will rise substantially in the coming years.

Variety

Variety refers to the various types of big data. It is one of the most significant problems facing the big data industry as it affects performance. It is vital to manage the variety of your data correctly by organizing it. Variety is the various types of data you collect from different types of sources.

Veracity

Veracity refers to the accuracy of the data. It is one of the most critical characteristics of Big Data since low veracity can significantly damage the accuracy of its results.

Validity

Validity refers to how valid and relevant the data is to be used for the intended purpose.

Volatility

Big data is constantly changing. The data you collected from a source a day ago may be different from what you found today. This is called variability of the data and affects the homogenization of the data.

Visualization

Visualization refers to displaying your insights generated by big data through visual representations such as charts and graphs. It has recently become predominant as big data professionals regularly share their insights with non-technical audiences.

Some other features of Big Data Analytics

Now, we look at some of the additional features of big data analytics that make it different from traditional types of analytics, in addition to the features mentioned above:

It can be programmatic. One of the biggest changes in terms of analytics is that in the past, it was about data sets that could be manually loaded into an application and viewed and explored. With big data analytics, you might be faced with a situation where you might start with the raw data that often needs to be handled programmatically (using code) to manipulate or do any kind of exploration due to the scale of the data. data.

It can be based on data. While many data scientists use a hypothesis-driven approach to data analysis (developing a premise and collecting data to see if that premise is correct), you can also use data to drive analysis, especially if you've collected large amounts of data. For example, you can use a machine-learning algorithm to perform this type of analysis without assumptions.

It can use a lot of attributes. In the past, you may have been dealing with hundreds of attributes or features from that data source. You may now be dealing with hundreds of gigabytes of data consisting of thousands of attributes and millions of observations. Everything is happening now on a larger scale.

It can be iterative. More computing power means that you can iterate on your models until you get them the way you want them. Here’s an example. Assume that you’re building a model that is trying to find the predictors for certain customer behaviours associated with certain products.

You can start by extracting a reasonable sample of data or by connecting to where the data resides. You can build a model to test a hypothesis.

While in the past you may not have had as much memory to make your model work effectively, you will need a large amount of physical memory to perform the necessary iterations to train the algorithm. It may also be essential to use advanced computing techniques such as natural language processing or neural networks that automatically evolve the model based on learning as more data is collected.

You can quickly get the compute cycles you need by leveraging cloud-based infrastructure as a service. With infrastructure-as-a-service (IaaS) platforms like Amazon Cloud Services (ACS), you can quickly provision a bunch of machines to ingest large data sets and quickly analyze them.

Frequently Asked Questions

What are the different types of Big Data?

There are three types of big data:

  • Structured Data
  • Unstructured Data
  • Semi-structured Data

What are some main components of Big Data?

The main components of Big Data are:

  • Ingestion
  • Storage
  • Analysis
  • Consumption

What are the advantages of Big Data?

Some of the advantages of Big Data are:

  • Data-driven customer service
  • Efficiency Optimization
  • Enhanced decision-making
  • Real-time decision making

Write some Big Data processing techniques?

Some of the Big Data processing Techniques are:

  • Batch Processing of Big Data
  • Big Data Stream Processing
  • Map Reduce
  • Real-Time Big Data Processing

Conclusion

In this blog, we have discussed Big Data. We introduced the big data than the two perspectives to view big data. Then we discussed the different characteristics of big data analysis.
Learning never stops, and to learn more and become more skilled, head over to our practice platform Coding Ninjas Studiopractice top problems, attempt Mock Tests, and you can also consider our Data Analytics Course to give your career an edge over others. Do upvote our blog to help other ninjas grow. 

Happy Learning!

Previous article
Taxonomy and Big Data
Next article
R- environment
Live masterclass