Hadoop online training
                     HADOOP
 Online training
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HADOOP
 Online training
Hadoop is an open-source framework that allows to store and 
process big data in a distributed environment across clusters 
of computers using simple programming models. It is 
designed to scale up from single servers to thousands of 
machines, each offering local computation and storage.
This brief tutorial provides a quick introduction to Big Data, 
MapReduce algorithm, and Hadoop Distributed File System.
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HADOOP
 Online training
What is Big Data?
Big data is a collection of large datasets that cannot be processed using traditional 
computing techniques. It is not a single technique or a tool, rather it has become a 
complete subject, which involves various tools, technqiues and frameworks.
What Comes Under Big Data?
Big data involves the data produced by different devices and applications. Given below are 
some of the fields that come under the umbrella of Big Data.
Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures 
voices of the flight crew, recordings of microphones and earphones, and the performance 
information of the aircraft.
Social Media Data − Social media such as Facebook and Twitter hold information and the 
views posted by millions of people across the globe.
Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and 
‘sell’ decisions made on a share of different companies made by the customers.
Power Grid Data − The power grid data holds information consumed by a particular node 
with respect to a base station.
Transport Data − Transport data includes model, capacity, distance and availability of a 
vehicle.
Seianrfcoh@ Erhnsgoifntete Dcha.tcao m− Search engines retrieve lots of data from differen+t9 d1a 9ta35b6a9s1e3s84. 9
HADOOP
 Online training
Thus Big Data includes huge volume, high velocity, and extensible variety of 
data. The data in it will be of three types.
Structured data − Relational data.
Semi Structured data − XML data.
Unstructured data − Word, PDF, Text, Media Logs.
Benefits of Big Data:
Using the information kept in the social network like Facebook, the 
marketing agencies are learning about the response for their campaigns, 
promotions, and other advertising mediums.
Using the information in the social media like preferences and product 
perception of their consumers, product companies and retail organizations 
are planning their production.
Using the data regarding the previous medical history of patients, 
hospitals are providing better and quick service.
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Operational vs. Analytical 
Systems Analytical
Operational
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
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Traditional Approach
In this approach, an enterprise will have a computer to store and process big 
data. For storage purpose, the programmers will take the help of their choice of 
database vendors such as Oracle, IBM, etc. In this approach, the user interacts 
with the application, which in turn handles the part of data storage and analysis.
Limitation
This approach works fine with those applications that process less voluminous data 
that can be accommodated by standard database servers, or up to the limit of the 
processor that is processing the data. But when it comes to dealing with huge 
amounts of scalable data, it is a hectic task to process such data through a single 
database bottleneck.
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Google’s Solution
Google solved this problem using an algorithm called MapReduce. This algorithm 
divides the task into small parts and assigns them to many computers, and collects 
the results from them which when integrated, form the result dataset.
Hadoop
Using the solution provided by Google, Doug Cutting and his team developed 
an Open Source Project called HADOOP.
Hadoop runs applications using the MapReduce algorithm, where the data is 
processed in parallel with others. In short, Hadoop is used to develop 
applications that could perform complete statistical analysis on huge amounts of 
data.
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 Online training
Hadoop is an Apache open source framework written in java that allows distributed 
processing of large datasets across clusters of computers using simple programming 
models. The Hadoop framework framework application works in an environment that 
provides distributed storage and computation across clusters of computers. Hadoop is 
designed to scale up from single server to thousands of machines, each offering local 
computation and storage.
•Hadoop Architecture
•At its core, Hadoop has two major layers namely −
•Processing/Computation layer (MapReduce), and
•Storage layer (Hadoop Distributed File System).
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How Does Hadoop Work?
It is quite expensive to build bigger servers with heavy configurations that handle large 
scale processing, but as an alternative, you can tie together many commodity computers 
with single-CPU, as a single functional distributed system and practically, the clustered 
machines can read the dataset in parallel and provide a much higher throughput. Moreover, 
it is cheaper than one high-end server. So this is the first motivational factor behind using 
Hadoop that it runs across clustered and low-cost machines.
Hadoop runs code across a cluster of computers. This process includes the 
following core tasks that Hadoop performs −
Data is initially divided into directories and files. Files are divided into uniform sized blocks 
of 128M and 64M (preferably 128M).
These files are then distributed across various cluster nodes for further processing.
HDFS, being on top of the local file system, supervises the processing.
Blocks are replicated for handling hardware failure.
Checking that the code was executed successfully.
Performing the sort that takes place between the map and reduce stages.
Sending the sorted data to a certain computer.
Writiinnfgo @threh sdoefbtutegcghi.ncgo mlogs for each job. +91 9356913849
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Advantages of Hadoop
Hadoop framework allows the user to quickly write and test distributed 
systems. It is efficient, and it automatic distributes the data and work across 
the machines and in turn, utilizes the underlying parallelism of the CPU cores.
Hadoop does not rely on hardware to provide fault-tolerance and high 
availability (FTHA), rather Hadoop library itself has been designed to detect 
and handle failures at the application layer.
Servers can be added or removed from the cluster dynamically and Hadoop 
continues to operate without interruption.
Another big advantage of Hadoop is that apart from being open source, it is 
compatible on all the platforms since it is Java based.
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