Big Data

What is Big Data?

         Big Data refers to the large and complex sets of data that are too massive to be processed and analyzed by traditional data processing tools. The term "big data" encompasses not only the size of the data, but also its velocity, variety, and complexity. Big Data can come from various sources such as social media, internet searches, financial transactions, and more.

  Importance of Big Data:
             Big Data has become increasingly important in recent years because of the insights and opportunities it provides. By analyzing Big Data, companies can gain a better understanding of their customers, optimize their operations, and make informed business decisions. With the help of Big Data, organizations can identify trends, patterns, and correlations that would otherwise be difficult to discern.
               Big Data refers to the massive amount of data that is generated from various sources such as social media, digital devices, machines, and other forms of digital communication. This data is typically characterized by its volume, velocity, variety, and veracity.

Sources of Big Data:
        The sources of Big Data include:

  1. Social Media - Platforms such as Facebook, Twitter, and Instagram generate massive amounts of data in the form of posts, comments, and likes.
  2. Digital Devices - Smartphones, wearables, and other digital devices generate vast amounts of data in the form of location data, browsing history, and app usage.
  3. Machines - Industrial equipment, sensors, and other machines generate data that is used for monitoring, analysis, and optimization.
  4. Other forms of digital communication - Emails, chats, and other forms of digital communication generate data that can be analyzed to derive insights.
Big Data online Course

Big Data Types, Tools and Technologies:

Types of Big Data:

The types of Big Data are as follows:

  1. Structured Data
  2. Unstructured Data
  3. Semi-Structured Data

Structured Data:
                          Structured Data refers to data that is organized in a well-defined format such as rows and columns. Examples include databases, spreadsheets, and other forms of structured data. Structured data is data that is organized into a specific format such as a database.

Unstructured Data:
                    Unstructured Data refers to data that is not organized in a well-defined format. Examples include social media posts, images, videos, and audio files.  Unstructured data is data that is not organized into a specific format such as text or images.

Semi-Structured Data:
                               Semi-Structured Data refers to data that is partially structured and partially unstructured. Examples include XML and JSON data formats. Semi-structured data is a combination of structured and unstructured data.    

Big Data Tools & Technologies: 

There are a variety of tools and technologies available to help organizations manage and analyze Big Data. These include:

  • Hadoop:   
  •            Hadoop is a distributed storage and processing system that can handle massive amounts of data. It is based on the MapReduce programming model and is used for batch processing of Big Data. A popular open-source framework for distributed storage and processing of large data sets.
  • Spark:
  •            Spark is a distributed processing engine that can handle both batch and real-time processing of Big Data. It is based on the Resilient Distributed Datasets (RDD) programming model. An open-source distributed computing system that can process large data sets quickly and efficiently. 
  • NoSQL databases:
  •            NoSQL databases are non-relational databases that can handle massive amounts of unstructured data. They are often used in conjunction with Hadoop and Spark for Big Data processing. Non-relational databases that can handle large volumes of structured and unstructured data. 
  • Data warehouses: 
  •         Stream processing involves processing and analyzing data in real-time. Stream processing tools such as Apache Kafka and Apache Storm are used for real-time processing of Big Data. Centralized repositories for storing large volumes of data, which can be accessed for analysis and reporting.
  • Python and R: 
  •         Python and R are programming languages commonly used in data science and Big Data analytics. They have numerous libraries and packages that can be used for data processing, machine learning, and statistical analysis.
  • Apache Link:
  •       Apache Link is an open-source stream processing framework that can perform real-time analytics on large data sets. It is often used for processing and analyzing data from IoT devices and other real-time data sources.
Big Data Tools & Technologies
Big Data Tools & Technologies 

Characteristics Of Big Data:

The characteristics of Big Data are:

  1. Volume - Big Data is characterized by its massive volume. It is often measured in petabytes, exabytes, and even zettabytes. 
  2. Velocity - Big Data is generated at an unprecedented speed, making it challenging to process and analyze in real-time.  
  3. Variety - Big Data comes in different formats and structures, ranging from structured data such as spreadsheets to unstructured data such as social media posts.
  4. Veracity - Big Data is often plagued with inaccuracies, inconsistencies, and errors, making it challenging to extract meaningful insights from it.
Application Of Big Data: 

Big Data is being used in a variety of industries and applications. Here are some examples of Big Data applications:

  •  Healthcare: 
  • Big Data has a significant role in the healthcare industry by improving patient care, medical research, and reducing costs. By analyzing large volumes of data, healthcare organizations can identify disease trends, develop personalized treatment plans, and predict and prevent health issues.
  •  Banking and Finance: 
  • Big Data is extensively used in the banking and finance industry to analyze customer behavior, detect fraud, manage risks, and create personalized marketing strategies. Big Data analytics can help financial institutions to improve customer experience and satisfaction, reduce operational costs, and increase profits.
  •  Retail:
  •  Big Data is used in the retail industry to analyze consumer behavior, optimize inventory, create targeted marketing campaigns, and improve supply chain management. By leveraging Big Data analytics, retailers can make data-driven decisions and gain a competitive edge.
  •  Transportation: 
  • Big Data is used in the transportation industry to optimize routes, reduce traffic congestion, and improve safety. By analyzing traffic data, transportation companies can create predictive maintenance plans, optimize vehicle utilization, and reduce operational costs.
  • Social Media:
  • Big Data is used extensively in social media platforms to analyze user behavior, develop personalized content, and target advertising campaigns. Social media analytics can help businesses to understand customer preferences, sentiment, and opinions, and make informed decisions based on that data.
Features Of Big Data:
  • Predictive Analytics: 
  • Predictive analytics involves using Big Data to predict future outcomes. It enables organizations to make data-driven decisions and identify potential risks and opportunities.
  • Artificial Intelligence: 
  • Artificial Intelligence involves using algorithms and machine learning models to analyze Big Data and make intelligent decisions. It enables organizations to automate processes and make accurate predictions.
  • Machine Learning:  
  • Machine Learning involves using algorithms and statistical models to analyze Big Data and make predictions. It enables organizations to identify patterns and trends in the data and make informed decisions.
  • Internet of Things (IoT):
  •  The Internet of Things involves connecting devices and sensors to the internet to collect and analyze data. It enables organizations to monitor and optimize processes in real-time and make data-driven decisions.
Future Of Big Data
Challenges & Opportunities Of Big Data:

          The challenges in Big Data include managing and processing large volumes of data, ensuring data quality and accuracy, and extracting meaningful insights from the data. However, Big Data also presents significant opportunities for organizations to gain a competitive advantage, increase efficiency, and provide better customer service.

  • Data Management:  
  •        Data management involves the organization, storage, and retrieval of data. Big Data presents challenges in managing data due to the large volume and variety of data sources. It is important to ensure data quality and accuracy, and to have a clear understanding of the data being collected and stored.
  • Data Storage:
  •       Big Data requires large-scale storage solutions that can handle the volume and velocity of data. Traditional storage systems may not be able to handle the sheer amount of data being generated, and organizations may need to implement new storage technologies such as distributed file systems, cloud-based storage, or object-based storage.
  •  Data Processing:
  •         Big Data processing involves performing complex computations on large datasets. It requires specialized tools and techniques such as parallel processing, distributed computing, and in-memory computing. The processing of Big Data can be time-consuming and resource-intensive, requiring significant computational power and storage resources.
  • Data Analysis:
  •         Data analysis involves extracting insights and knowledge from Big Data. It requires advanced analytics tools and techniques such as machine learning, natural language processing, and predictive modeling. The analysis of Big Data can be challenging due to the complexity and volume of data, as well as the need for real-time analysis.
  • Data Visualization:
  •        Data visualization involves presenting data in a graphical format to facilitate understanding and decision-making. It requires specialized tools and techniques to handle the large volumes of data being generated. Effective data visualization can be challenging, as it requires the ability to identify patterns and insights from complex data sets and to present them in a clear and concise format.

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A career in Big Data requires a combination of technical and analytical skills. Some of the essential technical skills include data modeling, data warehousing, SQL, Hadoop, and programming languages such as Python and Java. Analytical skills such as statistical analysis, machine learning, and data visualization are also critical for a career in Big Data.

There are several job roles in Big Data, including data analyst, data scientist, data engineer, Big Data architect, and business intelligence analyst. Each of these roles requires a different set of skills and responsibilities.

Big Data is used in various industries such as healthcare, finance, retail, transportation, and social media. The use of Big Data is rapidly expanding as organizations seek to gain insights and improve decision-making.

Answer Here...Big Data is a rapidly growing field with a strong demand for skilled professionals. According to a report by IBM, demand for data scientists will increase by 28% by 2020. The report also suggests that there will be a shortage of 2.7 million data science and analytics professionals by 2020.

The salary in a Big Data career can vary depending on the job role, location, and experience. However, according to Glassdoor, the average salary for a data scientist in the US is around $120,000 per year, while a Big Data engineer can earn around $110,000 per year.

A degree in computer science, information technology, mathematics, or statistics is typically required for a career in Big Data. Many employers also prefer candidates with a master's degree in data science, analytics, or a related field.

To start a career in Big Data, one can begin by learning the necessary technical skills such as SQL, Hadoop, and programming languages. They can also pursue a degree or certification program in data science, analytics, or a related field. Joining online communities and attending industry events can also help build connections and gain valuable insights into the field.

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