Last updated on Nov 18, 2023
It is a kind of framework that permits us to store massive data in distributed environments; it provides applications that gather data in different formats into Hadoop through API operations to interact with namenode. It tracks the file directory structure and chunks placement for every file to replicate in data nodes. Its ecosystem developed with high speed over the years with its extensibility. It contains many applications and tools for collection, storing, processing, analyzing, and managing massive data. Its search results return through humans as the web develops from a few to million pages that need automation. It generates web crawlers and various research products of universities and the startups of search engines.
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The given below are some fundamental reasons which made it essential.
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Hadoop is a prevalent and powerful tool for massive data, and it offers the world-famous, reliable layer of storage. The given below are some essential features of Hadoop.
1. Hadoop is open source: it is an open-source project. Its code is available for free of price for inspection, modification and analysis, which permits organizations for code modification as per our needs.
2. Hadoop cluster is highly scalable: its collection is scalable, which permits the addition of any amount of nodes to develop node hardware capacity. It helps us to gain high computation and offers horizontal and vertical scalability for the framework of Hadoop.
3. Hadoop offers fault tolerance: it is one of the essential Hadoop features, it helps for mechanism replication to provide fault tolerance. It generates the reproduction of every block on various machines based on replication factors. When the cluster machine goes down, then the information is accessed from replication machines. It also ensures the coding replaces this mechanism of replication. It maintains the erasure coding, which offers the equal level of fault tolerance in less place. It helps in storage overhead of less than 50%.
4. Hadoop offers high availability: it is one of the essential Hadoop features that ensure high data availability when data nodes come down when the information is available for customers from various data nodes, including exact data copying. In the case of an active node failure, the passive nodes play the active node's roles and responsibilities. It offers files even after the collapse of the namenode for the customers, and it includes multiple nodes in a standby configuration. Namenode is considered an active node, and the standby node works like a passive node that reads the edit modifications of logs in the namespace.
5. Hadoop is very cost-effective: Hadoop is a cluster that includes many hardware commodity nodes. They are inexpensive to offer solutions with effective price for massive data processing and storage. It is famous as an open source product, so there is no requirement for any licenses.
6. Hadoop is faster in data processing: it uses data stored in the distribution fashion, which permits our data as the distributed process on the nodes cluster. It offers the quick lighting ability of strategy for the framework of Hadoop.
7. Hadoop is based on the concept of data locality: it is well known for the feature of data locality, which means the running logic of data computation instead of data transfer to the logic computation. This feature helps decrease the utilization of system bandwidth, and it also provides guidelines for the Hadoop configuration download.
8. Hadoop offers feasibility: Hadoop helps to process unstructured data instead of being like a traditional system. It offers feasibility for the customer's data analysis of all sizes and formats.
9. Hadoop is simple to use: it is simple to utilize, as its customers do not worry about computing distribution. It controls the framework process itself without taking any external help.
10. Hadoop makes sure of data reliability: cluster data replication helps store the reliability of machines instead of their failure on the cluster. Its framework offers the mechanism to ensure the reliability of data through the scanner of the block, volume, disk checker, and the scanner of the directory. When our machines come down, and our information is corrupted, even after those things, our data reliably is stored in the cluster from the remaining devices, which includes the data copy.
11. Hadoop distributed file system: it works as a kind of storage layer in Hadoop, always the information is stored in the data blocks form in default size of every block of 128 MB with configuration. It works on the algorithm of MapReduce, which is an architecture of master-slave. It maintains data nodes and namenode in the same form.
12. Yet another resource negotiation: it is a kind of scheduling of job and the layer of resource management in Hadoop, the information is stored on the Hadoop distributed file system and runs with the data processing engines such as processing of data, batch, etc. the entire process of Hadoop is developed by using the framework of YARN.
13. Mapreduce: it is a Hadoop layer of processing. It is a model of programming which is classified as two phases Map and reduce. It is created for the data processes in parallelly classified nodes.
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The given below are some essential advantages of Hadoop.
The following are some disadvantages of Hadoop.
Top 30 frequently asked Big Data Hadoop interview questions & answers for freshers & experienced
Hadoop is a framework of open source, which is famous for its feature of high availability and fault tolerance. It maintains scalable clusters, and its frameworks are simple to use. It makes sure our data speed is processed for its distribution. Its feature of data locality decreases the system utilization of bandwidth. Java is used to write its framework using some C codes and scripts of shell, which works in different commodity hardware for massive datasets dealing with the basic programming model. Hadoop is a data scientist skill for massive data technology, and organizations are spending a considerable amount on it and becoming a popular skill in the future and popularising in the market.
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.
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