Hadoop-HDFS

Basic of Hadoop

Today in this Fast growing World the rate of data generated is growing enormously AND the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of

time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013.

This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected.

This information can be referred as a collection of large datasets that cannot be processed using traditional computing techniques abbreviated as BIG DATA.

Big Data includes huge volume, high velocity, and extensible variety of data and can be referred as three types.

•  Structured data: Relational data.

•  Semi Structured data: XML data.

•  Unstructured data: Word, PDF, Text, Media Logs.

Handle such big data and analyzing it requires to deal with certain technologies which provides more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.

BIG DATA TECHNOLOGIES

looking into the technologies that handle big data, we examine the following two classes of technology:

 Operational Big Data

This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

Analytical Big Data

This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.

MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.

Besides having these technologies This Data when Processed, leads to major challenges as followed:

•  Capturing data

• Curation

• Storage

•  Searching

• Sharing

• Transfer

• Analysis

• Presentation

To deal with the above challenges, organizations normally take the help of enterprise servers.

BIG DATA Solutions

Traditional Enterprise 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.

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.

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.