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BIG DATA

An Introduction to Big Data:

Big Data will be the Big thing in the IT world.

Big data burst upon the scene in the first decade of the 21st century.

Firms such as Google, eBay, LinkedIn, and Facebook were made around big data from the beginning.

Big data can bring about dramatic cost reductions, substantial improvements in the time needed to accomplish a computing task, or new product and service aids.

What is Big data?

“Big Data” is similar to “Small Data” but it will be bigger.

There are numerous approaches need for big data. They are,

  • Techniques
  • Tools
  • Architecture
  • Big data is aims to resolve new or old problems in a better way.

    Decoding the human genome originally took 10 years to be processed, and now it can be achieved in one week.

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    big data analytics

    Three characteristics of big data V3s

    VOLUME

    Data quantity

    VELOCITY

    Data speed

    VARIETY

    Data types

    1st character of Big data
    VOLUME

    A typical PC might have had 10GB of storage in 2000.

    Today, Facebook consumes500 tetrabytes of new data every day.

    2nd character of big data
    VELOCITY

    Clickstreams and ad impressions capture user behavior at millions of events per second.

    High-frequency stock trading algorithms reflect market alterationswithin microseconds.

    Machine to machine process will exchange of data between billions of devices

    Infrastructure and sensors will createhugelog data in real-time.

    Online gaming systems will support millions of concurrent users;each one will produce multiple inputs per second.

    3rd character of big data
    VARIETY

    Big data is not just numbers, dates, and strings. Big data is also geospatial data, 3D data, audio and video, and unstructured text, which includes log files and social media

    Traditional databases systems were formed to reportsmaller volumes of structured data, fewer updates, or a predictable, consistent data structure.

    types of BIG DATA ANALYSIS

    BIG DATA ANALYSIS includes various types of data They are,

    • Storing Big data
    • Selecting Big data
    • Processing big data
    what is big data
    SELECTING BIG DATA STORES

      Choosing the correct data stores on the basis of your data characteristics

      Moving code to data

      Execution of polyglot data store solutions

      Aligning business goals to the suitable data store.

    STORING BIG DATA

    Analyzing your data characteristics

    • Selection of data sources
    • Removing redundant data
    • Establishing the role of NoSQL

    Overview of Big data stores

    • Data models: Key-value, graph, document, column family.
    • Hadoop distributed file system
    • HBase
    • Hive
    PROCESSING BIG DATA

    Integrating disparate data stores

    • Mapping data to the programming outline
    • Connecting and removing data from storage
    • Transforming data for processing
    • Dividingdata in preparation for Hadoop MapReduce

    Using Hadoop MapReduce

    • Making the components of Hadoop MapReduce
    • ImplementingHadoop MapReduce jobs
    • Observingthe progress of job flows

    The structure of BIG DATA

    structure of big data

    HISTORY OF BIG DATA

  • The word 'Big Data has been in usagesince the time of 1990s. But it is unknown who first identified and utilized this term Big Data.
  • Some groups of people believe thatJohn R. Mashey has used this term first.
  • On the basis of its true principle, Big Data is not totally new, because people have been trying to utilize data analysis and analytics methodologiesfor supportingtheir decision-making process.
  • The ancient Egyptians about300BC now tried to capture all existing data in the library of Alexandria.
  • In the last two decades, the volume and speed of the data have generated and change.
  • The total number of data in the world was 4.4 zettabytes in 2013.
  • To demonstratethis development, the Big Data evolution is now divided into ‘3’ main phases. Each phase has its own features and abilities. To understand the context of big data today, it is essential to recognizehow each phase contributed to the modern meaning of big data.
  • BIG DATA PHASE

    big data phase 1.0
    BIG DATA PHASE 3.0

    Thoughweb-based unstructured content is still the main focus for many organizations in data analysis, data analytics, and big data, the current possibilities to recovervaluable information are emerging out of mobile devices.

    BIG DATA PHASE 2.0

    Since the early 2000s, the internet and website have started to provide unique data collections and data analysis chances. With the expansion of web traffic and online stores, companies such as Yahoo, Amazon, eBay started to analyze the behavior of customers by analyzing click-rates, IP-specific location data, and search logs. This opened a completely new world of opportunities.

    BIG DATA PHASE 1.0

    Data analytics and big data, both terms were originated from the longstanding domain of database management. It depends onthe storage, extraction, and optimization methodologies that are common in data that will bestored in relation database management systems.

    Applications of Big data analysis:

    smarter health care

    Used in smarter healthcare

    security

    Involves in Homeland security

    manufacturing

    Manufacturing

    traffic control

    Traffic control

    sales

    Multi-channel sales

    telecommunication

    Telecom

    analyrics

    Trading analytics

    search quality

    Search quality

    Disadvantages of Big data:

    Despite the applications and advantages of big data, it has several disadvantages as well.

    • Need for talented scientists and experts and highly paid workers in the IT field.
    • Incompatible tools are needed. Hadoop is a highly useful tool in Big data, but the standard version of Hadoop is currently available to handle real-time analysis.
    • Correlation errors- A common methodology that isutilized to analyze Big data. And that is available to draw correlations by connecting one variable to another.
    • Security and privacy concerns: Big Data analytics will allowyou to detect fraudulent attempts.