H for Hadoop & Humanized Big Data. (visual, empathetic, qualitative)

What is Hadoop ?

 

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.

 

 Why is Hadoop important?

• Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that's a key consideration.
  
• Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  
• Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
• Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
• Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
• Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.

 How it works?

 

Doug Cutting, Mike Cafarella and team took the solution provided by Google and started an Open Source Project called HADOOP in 2005 and Doug named it after his son's toy elephant. Now Apache Hadoop is a registered trademark of the Apache Software Foundation.

Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel on different CPU nodes. In short, Hadoop framework is capable enough to develop applications capable of running on clusters of computers and they could perform complete statistical analysis for a huge amounts of data.

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. A Hadoop frame-worked 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

Hadoop framework includes following four modules:

• Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
• Hadoop YARN: This is a framework for job scheduling and cluster resource management.
• Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
• Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.
  
  
  How Does Hadoop Work?
  

  Stage 1

  A user/application can submit a job to the Hadoop (a hadoop job client) for required process by specifying the following items:
  
• The location of the input and output files in the distributed file system.
• The java classes in the form of jar file containing the implementation of map and reduce functions.
• The job configuration by setting different parameters specific to the job.

Stage 2

The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Stage 3

The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.

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
  

References:

https://www.tutorialspoint.com/hadoop/hadoop_hdfs_overview.htm

https://www.sas.com/en_in/insights/big-data/hadoop.html

 

 

Fun Fact: "Hadoop” was the name of a yellow toy elephant owned by the son of one of its inventors.