What Is Big Data? – Complete Beginner’s Guide To Big Data Analytics

What is Data?

Read Completely To Know About Big Data Anlytics

A computer can store and transmit the quantities, characters or symbols that are used in its operations. These signals may also be recorded on magnetic, optical or mechanical recording media.

Let’s now learn Big Data Introduction

What is Big Data?

Big DataThis is a large collection of data, which grows exponentially over time. This data is so complex and large that no traditional data management tool can store or process it efficiently. Big data can also be a huge data set.

big data analytics

Social Media

This statistic proves that500+terabytesNew data is ingested in the social media sites’ databasesFacebookEvery day. These data are mainly generated by photo and video uploads, messages exchanges, commenting, etc.

Different types of big data

These are the types and uses of Big Data.

  • Structured
  • Unstructured
  • Semi-structured


A’structured data’ is data that can be stored, accessed, and processed in a fixed format. Computer science talent has been able to develop techniques to work with this type of data (where it is known in advance) and derive value from it. We are now seeing problems when such data becomes large. These sizes are often in excess of multiple gigabytes.


Unstructured data is data that has no structure or form. Unstructured data is large and presents many challenges when it comes to its processing in order to extract value. Unstructured data can be described as heterogeneous data sources that contain a mix of text files, images and videos. Organizations have a wealth of data, but they don’t know how it can be used to their advantage.


Semi-structured data may contain both forms of data. Semi-structured data can be described as structured but not with e.g. A table definition in relational . An example of semi-structured information is data that has been represented in an XML format.

How does Big Data work?

Big Data is based on the principle of more data, the better you can predict the future and gain insight into the past. When you compare more data points, relationships can be discovered that were not previously visible. These relationships will allow us to learn from our mistakes and inform our decisions.

This is most commonly done by building models based on data and then running simulations. Each time, we tweak the values of data points and monitor how they impact our results. The process can be automated. Modern analytics technology can run millions of simulations and tweak all variables until it finds a pattern or insight that solves the problem.

Data is increasingly arriving in unstructured forms, which means it can’t be put into structured tables with rows or columns. Many of the data comes in the form images and videos, from satellite images to photos uploaded to Facebook and Twitter. Big Data projects often employ cutting-edge analytics that use machine learning and artificial intelligence to make sense of this data. Computers can identify the data they are storing, using image recognition or natural language processing. They are able to spot patterns faster and more reliably than human eyes.

The delivery of Big Data tools, and technology via an “as a service” platform has been a strong trend in the past few years. Third-party cloud service providers let businesses and organizations rent servers, software systems, and processing power. The work is done on the service provider’s servers and the customer pays only for what was used. This model makes Big Data-driven transformation and discovery accessible to all organizations and reduces the need for large amounts of hardware, software, premises, and technical staff.

Big Data Characteristics

These characteristics can be used to describe big data:

  • Volume
  • Variety
  • Velocity
  • Variability

(i). Volume -Big Data is a term that refers to data of enormous size. Data size plays an important role in determining the data’s value. The volume of data is also a key factor in determining whether data can be considered Big Data. Therefore,”Volume”This is an important characteristic to consider when working with Big Data.

(ii) Variety –Big Data’s next aspect is its ability to predict the future.Variety.

Variety refers both to heterogeneous sources as well as the nature of data, structured and unstructured. In the past, most applications only used spreadsheets and databases to store data. Data can now be in email, photos, videos and monitoring devices. Analytical applications also consider these data. Unstructured data presents challenges for data storage, mining, and analysis.

(iii). Velocity –The term”Velocity”This refers to how fast data is generated. The data’s true potential is determined by how fast it can be generated and processed to meet customer demands.

Big Data Velocity refers to the speed with which data flows from sources such as business processes, application logs and networks, social media sites, sensors and mobiledevices. Data flows in a continuous and massive way.

(iv). Variability -This refers to inconsistency that can sometimes be displayed by data, which may hinder the ability to manage and handle the data effectively.

Big Data Processing has many benefits

The ability to process Big Data has many benefits.

Companies can use outside intelligence to make better decisions
Organizations can use social data from search engines, sites like facebook and twitter to improve their business strategies.

Customer service improved

New systems that use Big Data technology are replacing traditional customer feedback systems. These new systems use Big Data and natural language processing technology to evaluate and read consumer responses.

If possible, early identification of potential risks to products/services

Operational efficiency improved

The use of Big Data technologies is a great way to create a landing area for new data, or a staging zone before identifying which data should be moved into the data warehouse. This integration of Big Data technologies with data warehouse allows an organization to offload less frequently accessed data.

Big Data solutions & concerns:

Big Data today offers unprecedented insight and opportunities. However, it also raises questions and concerns that need to be addressed.

  • Data privacy Big Data that we generate now contains a lot information about our private lives. We have the right to keep some of this data private. We are being asked to balance the amount of personal information we disclose and the convenience offered by Big Data-powered apps and services. Who can we give access to this data and why?
  • Data security – Can we trust someone with our data to keep it safe, even if we agree to allow them to use it for a specific purpose? Does the current legal framework have the right tools to regulate data use on this scale?
  • Data discrimination – If all information is available, can it be accepted to discriminate against people on the basis of data about their lives? Credit scoring is already used to determine who can borrow money. Insurance is heavily data-driven. You can expect to have your data analyzed in more detail. However, this shouldn’t make it harder for people with less resources or access to information.

These are all part of “Big Data.” These issues are a key part of the discussion around Big Data Analytics in academic circles. They must be addressed by all who wish to use Big Data in business. Failure to do this can make businesses vulnerable, and could lead to financial ruin as well as large fines.

Big Data Analytics was often dismissed when it first became popular. It was viewed as a trendy term that would be discussed for a while and then forgotten about until the next big thing. Although this has not been proven true, Big Data remains the driving force behind all new buzzwords. Analytics technology will be more powerful as we have more data. If Big Data can do all this today, imagine what it can do tomorrow.


  • big data analytics
  • big data technologies
  • hadoop big data
  • big data management
  • big data analytics services
  • data science and big data analytics
  • predictive big data analytics
  • big data analytics applications

Leave a Comment