XenonStack Recommends

Big Data Engineering

What is Big Data: Characteristics, Challenges, Tools & Use Cases

Chandan Gaur | 24 October 2020

What is Big Data?

Big Data is all about large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and tools created to manage this large amount of information. However, the data array with Big prefix is so huge that it is impossible to “shovel” it with structuring and analytics. This is the reason why we need a Platform to understand a set of multiple technologies to Ingest, analyze, search, process a large amount of structured and unstructured information for Real-Time Insights and Data Visualization.

Characteristics of Big Data

To designate an array of information with the “big” prefix, it works in the following terms:

Critical 5 V's of Big Data

Most critical 5 vs of Big data

  • Value : Various types of information can have different complexity for perception and processing issues, making it complex for intelligent systems to work with. So, the information must be managed in such a way that it delivers value eventually.
  • Veracity: It stands for provenance or reliability of the information source, its context, and how significant it is to the analysis based on it.
  • Variety: Information in arrays can have heterogeneous formats that can be structured partially, completely, and accumulated. For instance, social media networks use Big Data Analysis in text, video, audio, transactions, pictures, etc.
  • Volume: First of all, the data is measured by its physical size and space it occupies on a digital storage medium. The "big" includes arrays over 150 GB each day. Moreover, you can use theData Catalog to understand your collection of information well.
  • Velocity: After that, the information is regularly updated, and real-time processing requires intelligent platforms and technology. 5 vs of Big Data

Additional V's of Big Data

Other characteristics and properties  are as follows:

  • Visualization means collecting and analyzing a huge amount of information usingReal-time analytics to make it understandable and easy to read. Without this, it is impossible to maximize and leverage the raw information.
  • Validity: It means how clean, accurate, and correct the information is to use. The benefit of analytics is only as good as its underlying information, so good data governance practices should be adopted to ensure consistent data quality, common definitions, and metadata.
  • Volatility: It means for how long the information should be kept because before Big Data, there was a tendency to store information indefinitely because of its small volume; it hardly involved expenses.
  • Vulnerability: A huge volume of data comes up with many new security concerns since there have been many big data breaches.
  • Variability: Some data streams can have peaks and seasonality, periodicity. Managing a large amount of unstructured information is difficult and requires powerful processing techniques.

Gathers your business's information and brings it into a data processing system to store, analyze, and accessStreaming Analytics.

Big Data Architecture

Big Data Architecture

It helps to design the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. This architecture consists of 6 layers, which ensure a secure flow of data.

Discover more about Big Data Architecture

Big Data Platform & Tools

Big Data Platform refers to IT solutions that combine several Analytic tools and utilities into one packaged answer. This is then used further for managing and analyzing a huge volume of critical information. The emphasis on why this approach is needed is taken care of later in the blog, but know how much information is getting created daily. This huge information, if not maintained well, enterprises are bound to lose out on customers.

Big Data Tools

To fulfill the need to achieve high-performance,  Advanced Analytics tools play a very vital role in it. Some various open-source tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

Explore Big Data Tools to learn more about the given advanced Analytic tools & Frameworks:

  • Big Data Framework
  • Data Integrations Tools
  • Data Management Tools
  • Big Data Analytics Tools
  • Data Visualization Tools
  • Data Storage Tools

Benefits of Big Data

When exploited with Business Intelligence (BI) and advanced analytics tools, there are many benefits. First, because it answers multiple questions from organizations, it contributes insight and benchmarks. Second, because it quickly explains the business hurdles that previously required much more time and resources. Simply speaking, the good use of information translates into various Data Testing benefits for the company.


Information is essential as it is the basics of correct decision-making. The correct management of a large amount of information allows us to make smart and fast decisions to benefit our business. It helps to analyze opportunity fluently even before putting any product or service on the online marketplace.


Today it is possible to analyze and predict a user's behavior on the network and know what customers think about a specific brand or a product. Moreover, today we can find out customers' real needs, the products or services they really want on-board. Analyzing these facts enable businesses to develop targeted and highly personalized marketing campaigns.


You get various monitoring options by which one can learn more about one’s audience, trends, tests, and much more. This means a higher level of personalization and customization in the product can be given by adopting and managing information.


The correct handling of information can boost the speed at which a product or service rises because we have so much information in the markets. So, the deadlines for developing a product or service are shortened with time and the costs associated with the process derived from its development.


Managing a large volume of statistics represents the challenges for infrastructure management. That is why it is convenient to consider working with them in an environment that does not set limits like the cloud. Later, this comes in handy with cost savings in hardware. Moreover, it improves the accessibility and fluidity of the company's own employees, which increases effectiveness and speed.
Get an insight into our best practices for Hadoop Storage Format

Do you know? The best brands like Netflix, Apple, Amazon, Barcelona Metro, Zara, and others are already using this technique to achieve incredible results with their collection of a high volume of useful information.

How does Big Data work?

Well, the analysis of large amounts of structured and unstructured information is performed in 3 stages:

How Big Data Works

Stage 1: Data Cleaning

Find out and fix the errors in the primary set of information—for example, Typos (manual input errors), incorrect values by measuring instruments, etc.

Stage 2: Feature Engineering

Variables to build analytical models, like education, length of service, gender, and potential buyer's age.

Stage 3: Building and Training Analytical Model

Model selection to predict the target variable. This is how the hypotheses about the dependence of target variables on predictors are tested. For instance, how many days it will take to borrow with secondary education and work experience.

Big data is a collection of information from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis. Source:

Big Data Challenges

The risks associated with it are not just related to the confidentiality of information. However, it gradually uses the opportunities they offer; both users and organizations can be shocked by the result of their far from optimal processing. Some of the biggest challenges Include:

  • When quantity doesn't convert into quality.
  • Collecting a large amount of information is not enough; it entails costs.
  • Labour resources, knowledge, and skills.
  • Managing organizational and management structure is difficult.
  • Analytical processing of a huge volume of information and identification of patterns is complex.

Also, don't forget to check some of the Big Data Challenges & Solutions to overcome the most critical challenges of Big Data.

Big Data Use Cases

A Use Case is a written description that indicates the interactions between the users and a system. This helps the user by representing them a series of tasks it contains with the features it might use to fulfill any particular user’s goal.

We offer so many use cases to download, a few of them are named below:

  • Big Data Migration
  • Solution for Healthcare and Insurance
  • Oil & Gas Industries
  • Retail
  • Manufacturing
  • Infrastructure Automation
  • Real-Time Data Analytics Infrastructure with Smack Stack

Explore our Big Data Use Cases insight to get access to the entire list of related use cases we offer.


Top Six Best Practices For Big Data Analytics

  • Be clear about your business objectives.
  • Authorize files access with predefined security policy
  • Implement Testing in Big Data
  • Implementing Big Data in a business decision.
  • Safeguard sensitive encryption of information while at rest
  • Use agile solutions

What is Big Data

To detect the range of business activities, you need to analyze customer experience to proper analytics, which is the best practice. You can learn more about this with the following related use cases/insights/Blogs:

  1. Predictive Maintenance:It is significant in different application areas, like the manufacturing industry, information, technology, heavy-machinery industry, etc. To estimate the future performance of a subsystem to make an RUL (Remaining Useful Life) estimation.
  2. Customer Experience:Customer Experience Administers users' interaction with a particular product, for business purposes, a website, applications, or the experience they have received from that interaction. A good User Experience(UX) lets your customers instantly and efficiently find information.
  3. Data Science: This is the best approach that allows delivering business insights, and automates the processes with AI, and enrich customer insights and cost optimization using Data Science and Deep Learning Solutions. Moreover, it uses Predictive Analytics, Natural language Processing, and Computer Vision.

What's Next?

Now, as you know all about its What, Why, and How, it's time to implement it to manage and analyze your business statistics, check out our Big Data Solutions and Services to transform your business information into value, thereby obtaining competing advantages.