However, Have you ever wondered why businesses choose Hadoop to address their big data issues?
In this post, we’ll look into the key Hadoop characteristics that have made it so popular. The article lists a number of Hadoop qualities that make it the most popular big data technology, including open provisioning, scalability, fault tolerance, and over-availability, among others.
What is Hadoop?
Hadoop is a software framework created by the Apache Software Foundation for storing and analysing huge amounts of data. Hadoop is made up of three main components:
1. HDFS (High Distributed File System)
Hadoop’s garage layer is this. In HDFS, files become corrupted in blocks of block size. NameNode and DataNode are two types of nodes in HDFS.
The NameNode store’s metadata is basically blocking the location.
At a certain period, DataNodes purchases the block and transmits block evaluations to NameNode.
Hadoop’s rendering level is MapReduce. It’s a software programming framework for creating payment-processing software.
It’s the resource management layer. YARN is in charge of task planning and resource allocation.
Let’s start with Hadoop’s capabilities.
Features of Hadoop
Apache Hadoop is the most popular and effective technology for storing and processing massive volumes of data. Hadoop provides the most reliable garage layer in the world. Many of Hadoop’s main features is there in this Hadoop Features section.
1. Hadoop is Open Source
Hadoop is an open source project, which implies that its delivery code is not subject to inspection, modification, or analysis costs, allowing businesses to customise it to their own needs.
2. Hadoop cluster is Highly Scalable
Hadoop clusters are scalable, which means we can load any number of nodes (horizontally scalable) or increase the nodes’ hardware capacity (vertically scaleable) to boost processing power. The Hadoop framework now has horizontal and vertical scalability.
3. Hadoop provides Fault Tolerance
Hadoop’s most essential feature is fault tolerance. To achieve fault tolerance, Hadoop 2 employs a replication method.
Each block is duplicated on the distinct computers that rely on the replication factor (by default it is three well). As a result, if a device in a cluster fails, the information may be retrieved from other computers that have a replica of the same data.
Erasure coding was used in Hadoop 3 to alter the replication method. Erasure Coding provides the same level of fault tolerance while taking up significantly less space. The garage overhead is not usually greater than 50% when using erase coding.
To learn more about the erasure coding algorithm, see the article Erase Coding.
4. Hadoop provides High Availability
This Hadoop feature guarantees that information is always available, even when things aren’t going well.
Because of Hadoop’s fault tolerance, if one of the DataNodes fails, the person’s data must be retrieved from a single DataNode that has a copy of the same data.
Firstly, two or more NameNodes (hot and passive) walk in a warm standby arrangement in the Hadoop over-availability cluster. Secondly, the NameNode, which is energetic, is the energetic node. Thirdly, the standby node, which reads the change data from the power NameNode and applies them to its own namespace, is the passive node.
Lastly, if a power node fails, the passive node becomes the power node’s replacement. As a result, despite the NameNode’s failure, the documents are available and ready for the consumer.
5. Hadoop is very Cost-Effective
Because the Hadoop cluster is responsible of common hardware nodes that are relatively affordable, it is a cost-effective way to store and analyse huge volumes of data. Hadoop does not require a licence because it is an open-source project.
6. Hadoop is Faster in Data Processing
Hadoop is a distributed data storage system that allows data to be processed across a group of servers. As a result, it provides Hadoop’s ultra-fast processing capabilities.
7. Hadoop is based on Data Locality concept
Hadoop is well-known for its unique sort of information localization, in which the report is converted into data rather than data being moved to the report. Moreover, these Hadoop features cut down on the amount of bandwidth consumed by a system.
Follow these installation instructions to install and configure Hadoop.
8. Hadoop provides Feasibility
Moreover, Hadoop, unlike traditional systems, can handle unstructured data. Customers should be able to explore the information of any size or codec. It is one of the Hadoop features.
9. Hadoop is Easy to use
Hadoop is simple to use since consumers do not need to know how to operate a computer. The framework carries out the processing.
10. Hadoop ensures Data Reliability
Information is reliably on the cluster computers in Hadoop owing to information replication within the cluster, despite device failures.
However, through block scanners, volume scanners, disc checkers, and directory scanners, the architecture provides a way to assure some information reliability. Furthermore, if your device fails or your data becomes worse, your data is reliably stored within the cluster and destroyed outside of the alternate device, which holds a copy of the data.
Also, learn about 10 Hadoop 3 changes that make it more accurate and faster.
In conclusion, Hadoop describes itself as an open provisioning framework. Hadoop’s fault tolerance and high availability are its most well-known features. Moreover, Clusters of Hadoop are scalable. To sum up, Hadoop is a simple platform to deal with.
Ensures that information processes quickly.
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