Hadoop Framework -, components, and uses
You will likely come across references to the “Hadoop framework” when you’re learning about Big Data. The popularity of Hadoop and its analytics has made it very popular. Hadoop is open-source software. This means that the software can be downloaded for free and customized to meet individual needs. This allows you to customize the software to meet the needs of big data. Big data, as we all know, refers to large amounts of data that are not possible to store or process or analyze using the traditional methods. This is due to several characteristics. This is because big data is large in volume and generated at a rapid pace. Also, the data is available in many formats.
The traditional frameworks were ineffective at handling large amounts of data so new techniques needed to be developed. The Hadoop framework is here to help. The Hadoop framework is primarily based on Java and is used to deal large amounts of data.
What is Hadoop?
Hadoop is a data processing framework written in Java. Some secondary code is in shell script in C. Mike Cafarella and Doug Cutting developed it. This framework uses parallel processing and distributed storage to store and manage large data. It is the most widely used software for handling large amounts of data. Hadoop consists primarily of three components: Hadoop HDFS and Hadoop MapReduce. These components are combined to efficiently handle large data. These components are also called Hadoop modules.
Data scientists are increasingly required to have Hadoop as a core skill. Hadoop is becoming a valuable skill for professionals as companies look to invest in Big Data technology. Hadoop 3.x is the most recent version of Hadoop.
How does Hadoop work?
Hadoop’s idea is simple. Big data presents many challenges, including volume, variety, velocity, and velocity. It would be impossible to build servers that are larger and more powerful enough to handle large amounts of data. It is possible to connect multiple computers with one CPU. This would create a distributed system that operates under one system. This allows clustered computers to work in parallel towards the same goal. This would make it easier and more cost-effective to handle big data.
An example can help you better understand this concept. Imagine a carpenter making chairs and storing them in his warehouse until they are sold. There is a point when there is a demand for other products such as a table or a cupboard. Now, the same carpenter works on all three products. But, he’s exhausted and can’t keep up with the three products. He decides to hire two apprentices who will each work on a single product. They are now able to produce at a high rate, but there is a problem with storage. The carpenter can’t buy a larger warehouse to meet the increasing demand for his product. He instead takes three smaller storages to store the three different products.
This analogy shows the carpenter as the server that handles data. The server can handle too much data because of the increase in demand. The idea of one CPU being supported by multiple computers is now realized with the hiring of two apprentices to work under him. They are all working towards the same goal. To avoid storage bottlenecks, curated storage is used according to the variety. This is how Hadoop works.
Hadoop Framework’s Main Components
As mentioned previously, Hadoop has three core components. These are HDFS and MapReduce. These three components together make up the Hadoop framework architecture.
* HDFS (Hadoop Distributed File System).
It is a data storage device. It uses a distributed storage system to store large data sets. Each block contains 128 MB. It is composed of NameNode, DataNode. One NameNode can be used, but multiple DataNodes can be.
Features:
* The storage is distributed to manage a large data pool
* Data security is increased by distribution
* It can take over the failures of other blocks.
* MapReduce:
The processing unit is the MapReduce framework. All data is distributed and processed simultaneously. A MasterNode distributes data amongst SlaveNodes. The MasterNode distributes data to the MasterNodes.
Features:
* Consists two phases: Map Phase and Reduce Phase.
* Big data processing is faster when multiple nodes are working under one CPU
* YARN (yet other Resources Negotiator)
