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23 Jun 2018

Introduction to BigData Hadoop | BigData Technologies and Challenges

Introduction to BigData Hadoop

 What is Hadoop?

Hadoop is an open-source framework. It permits to store and process big data throughout clusters of computers in distributed surroundings the usage of easy programming models. It has an ability to scale up from single servers to heaps of machines. Each one offers local computation and storage.

What is Big Data?

Big data approaches really large data. Big data is a set of huge datasets that cannot be processed by the use of conventional computing techniques. Big data isn’t always simply a record; instead, it has come to be a whole situation, which includes various tools, strategies, and frameworks.

What Comes Under Big Data?

Big statistics entails the statistics produced via special devices and applications. Below are some of the fields that come beneath the umbrella of Big Data.

  • Black Box Data:  BBD is a component of the helicopter, airplanes, and jets, etc. It captures the voices of the flight crew. It also captures recordings of microphones and earphones including performance information of the aircraft.
  • Social Media Data:  Social media includes Facebook; Twitter etc. It holds the information and the views posted by millions of people across the globe.
  • Stock Exchange Data:  The SED holds information related to ‘buy’ and ‘sell’ decisions. These decisions are made on a share of different companies made by the customers.
  • Power Grid Data:  The power grid data holds the information which is consumed by a particular node with respect to a base station.
  • Transport Data: Transport data includes model and capacity. As it is a transport data it also includes distance and availability of a vehicle.
  • Search Engine Data:  SED retrieves the lots of information from various kinds of databases.


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These are the sources that come under the BigData.
Big Data includes huge volume, high speed, and extensible style of statistics. The information existed in it will likely be of three sorts. They are

  • Structured data: Relational data.
  • Semi Structured data: XML data.
  • Unstructured data: Word, PDF, Text, Media Logs comes under unstructured data.

Big Data Technologies:

There are mainly two technologies in Big Data

  • Operational
  • Analytical

Now let’s compare operational and analytical systems.

Operational vs. Analytical Systems:

Operational Analytical
Latency 1 ms – 100 ms 1 min – 100 min
Concurrency 1000 – 100,000 1 – 10
Access Pattern Writes and Reads Reads
Queries Selective Unselective
Data Scope Operational Retrospective
End-User Customer Data Scientist
Technology NoSQL MapReduce, MPP Database


 Big Data Challenges:

Below are the major challenges associated with big data.

  • Curation
  • Capturing data
  • Storage
  • Searching
  • Sharing
  • Transfer
  • Analysis
  • Presentation



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