Apache Hadoop is a Java-based, open-source data processing engine and software framework. Hadoop-based applications work on huge data sets that are distributed amongst different commodity computers. These commodity computers don’t cost too much and are easily available. They are primarily used to achieve better computational performance while keeping a check on the associated cost at the same time. So, what is a Hadoop cluster?
Everything About Hadoop Clusters and Their Benefits
What are Hadoop Clusters?
A Hadoop cluster combines a collection of computers or nodes that are connected through a network to lend computational assistance to big data sets. You may have heard about several clusters that serve different purposes; however, a Hadoop cluster is different from every one of them.
These clusters are designed to serve a very specific purpose, which is to store, process, and analyze large amounts of data, both structured and unstructured. A Hadoop cluster operates in a distributed computing environment.
What further separates Hadoop clusters from others that you may have come across are their unique architecture and structure. Hadoop clusters, as already mentioned, feature a network of master and slave nodes that are connected to each other. This network of nodes makes use of low-cost and easily available commodity hardware.
These clusters come with many capabilities that you can’t associate with any other cluster. They can add or subtract nodes and linearly scale them faster. This makes them ideal for Big Data analytics tasks that require computation of varying data sets. Hadoop clusters are also referred to as Shared Nothing systems. This name comes from the fact that different nodes in clusters share nothing else than the network through which they are interconnected.
How do Hadoop Clusters Relate to Big Data?
Big Data is essentially a huge number of data sets that significantly vary in size. Big Data can be as huge as thousands of terabytes. Its huge size makes creating, processing, manipulating, analyzing, and managing Big Data a very tough and time-consuming job. Hadoop Clusters come to the rescue! By distributing the processing power to each node or computer in the network, these clusters significantly improve the processing speed of different computation tasks that need to be performed on Big Data.
A key thing that makes Hadoop clusters suitable for Big Data computation is their scalability. If the situation demands the addition of new computers to the cluster to improve its processing power, Hadoop clusters make it very easy.
These clusters are very beneficial for applications that deal with an ever-increasing volume of data that needs to be processed or analyzed. Hadoop clusters come in handy for companies like Google and Facebook that witness huge data added to their data repository every other day.
What are the Benefits of Hadoop Clusters?
1. Flexibility: It is one of the primary benefits of Hadoop clusters. They can process any type or form of data. So, unlike other such clusters that may face a problem with different types of data, Hadoop clusters can be used to process structured, unstructured, as well as semi-structured data. This is the reason Hadoop is so popular when it comes to processing data from social media.
2. Scalability: Hadoop clusters come with limitless scalability. Unlike RDBMS that isn’t as scalable, Hadoop clusters give you the power to expand the network capacity by adding more commodity hardware. They can be used to run business applications and process data accounting to more than a few petabytes by using thousands of commodity computers in the network without encountering any problem.
3. Failure Resilient: Have you ever heard of instances of data loss in Hadoop clusters? Data loss is just a myth. These clusters work on Data Replication approach that provides backup storage. So, as long as there is no Node Failure, losing data in Hadoop is impossible.
4. Faster Processing: It takes less than a second for a Hadoop cluster to process data of the size of a few petabytes. Hadoop’s data mapping capabilities are behind this high processing speed. Tools that are responsible for processing data are present on all the servers. So, the data processing tool is there on the server where the data that needs to be processed is stored.
5. Low Cost: The setup cost of Hadoop clusters is quite less as compared to other data storage and processing units. The reason is the low cost of the commodity hardware that is part of the cluster. You don’t have to spend a fortune to set up a Hadoop cluster in your organization.
Hadoop Cluster Architecture
What exactly does Hadoop cluster architecture include? It includes a data center or a series of servers, the node that does the ultimate job, and a rack. The data center comprises racks and racks comprise nodes. A cluster that is medium to large in size will have a two or at most, a three-level architecture.
This architecture is built with servers that are mounted on racks. Every line of rack-mounted servers is connected to each other through 1GB Ethernet. In a Hadoop cluster, every switch at the rack level is connected to the switch at the cluster level. This connection is not just for one cluster as the switch at the cluster level is also connected to other similar switches for different clusters. Or it may even be linked to any other switching infrastructure.
Hadoop Cluster Components
1. Master node: In a Hadoop cluster, the master node is not only responsible for storing huge amounts of data in HDFS but also for carrying out computations on the stored data with the help of MapReduce. The master node consists of three nodes that function together to work on the given data.
These nodes are NameNode, JobTracker, and Secondary NameNode. NameNode takes care of the data storage function. It also checks the information on different files, including a file’s access time, name of the user accessing it at a given time, and other important details. Secondary NameNode backs up all the NameNode data. Lastly, JobTracker keeps a check on the processing of data.
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2. Worker or slave node: In every Hadoop cluster, worker or slave nodes perform dual responsibilities – storing data and performing computations on that data. Each slave node communicates with the master node through DataNode and TaskTracker services. DataNode and TaskTracker services are secondary to NameNode and JobTracker respectively.
3. Client node: Client node works to load all the required data into the Hadoop cluster in question. It works on Hadoop and has the necessary cluster configuration and setting to perform this job. It is also responsible for submitting jobs that are performed using MapReduce in addition to describing how the processing should be done. After the processing is done, the client node retrieves the output.
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