Hadoop is an open-source framework that allows to storage and process of big data in a distributed environment across clusters of computers using simple programming models. Hadoop is primarily used to support advanced analytics initiatives, including predictive analytics, data mining, and machine learning.
Hadoop is a particularly suitable fit for big data situations because of its capacity to analyze and store various sorts of data. They frequently involve large amounts of data as well as a mixture of structured transaction data, semi-structured data, and unstructured data, including clickstream records from the internet, logs from web servers and mobile applications, posts on social media, emails from customers, and sensor data from the internet of things.
What is Hadoop programming?
The management of data processing and storage for big data applications is handled by Apache Hadoop, an open-source, Java-based software platform. The platform works by dividing Hadoop’s large data and analytics operations into smaller workloads that can be handled in parallel. These workloads are then distributed among nodes in a computing cluster.
How was Hadoop invented?
Doug Cutting and Mike Cafarella founded Hadoop in 2002 while working on the Apache Nutch project. They were inspired by Google’s MapReduce programming style, which breaks an application into small portions to operate on several nodes. Hadoop was given the name Doug after his son’s toy elephant, according to a New York Times article. A few years later, Nutch and Hadoop were separated. Hadoop took on the role of distributed computing and processing whereas Nutch concentrated on the web crawler component. In 2008, Yahoo launched Hadoop as an open source project, two years after Cutting joined the company. Hadoop was released to the general public as Apache Hadoop in November 2012 by the Apache Software Foundation (ASF).
How does Hadoop work?
Hadoop receives data and applications from clients. HDFS manages the distributed file system and metadata, to put it simply. The input and output data are then processed and converted by Hadoop MapReduce. Finally, YARN distributes the tasks among the cluster members. Clients should anticipate significantly more effective use of commodity resources with Hadoop, along with high availability and an integrated point of the failure detection system. Customers can also anticipate fast response times when submitting queries to connected business systems.
Core Hadoop modules
Following Several core components make up the Hadoop ecosystem.
HDFS full form is Hadoop Distributed File System. HDFS is a Java-based system that allows large data sets to be stored across nodes. All data storage takes place in the Hadoop Distributed File System. Large data collections are managed by this component across a variety of organized and unstructured data nodes. It also keeps track of the metadata in the form of log files concurrently. The NameNode and the DataNode are HDFS’s two auxiliary parts.
YARN full form is Yet Another Resource Negotiator.YARN is used for cluster resource management, planning tasks, and scheduling jobs. Programmers can run as many applications as they need on the same cluster thanks to YARN. For optimal operational control and resource sharing for maximum flexibility, it offers a secure and reliable base. To put it simply, Hadoop YARN is a more recent and significantly improved version of MapReduce. That isn’t an entirely true picture, though. This is due to the fact that YARN is also utilized for job sequence execution, processing, and scheduling. However, YARN is the Hadoop layer for resource management, and it is here that each job that uses the data runs as a separate Java application.
MapReduce was the only execution engine available in Hadoop.MapReduce is both a programming model and big data processing engine used for the parallel processing of large data sets. MapReduce manages client-side job scheduling. Tasks requested by the user are separated into separate tasks and processes. The last step is to divide these MapReduce workloads into separate subtasks across the clusters and nodes of the commodity servers. The Map phase and the Reduce phase work together to achieve this. The data set is transformed into a new set of data that has been divided into key/value pairs during the Map step. The Reduce phase then modifies the output in accordance with the programmer’s specifications using the InputFormat class.
Advantages of Hadoop.
The advantages of Hadoop are the followings.
Hadoop is open-source and uses cost-effective commodity hardware which provides a cost-efficient model. Because it is not economical to store a large volume of data in typical relational databases, the business has begun to extract the raw data. It might not produce their business’s ideal circumstance. Hadoop offers us two key cost-saving advantages: first, it is open-source, which means it is free to use; second, it leverages cheap commodity hardware.
Hadoop is made to work well with any type of dataset, including unstructured (images and videos), semi-structured (XML, JSON), and structured (MySQL data). This makes it extremely flexible because it can process any type of data with ease, regardless of its form. This is particularly beneficial for businesses since it makes it simple to handle massive datasets. As a result, organizations can utilize Hadoop to analyze insightful data from sources like social media, email, and other sources. Hadoop can be used for log processing, data warehousing, fraud detection, etc. thanks to its versatility.
A very scalable model is Hadoop. In a cluster, a sizable volume of data is split among several affordable processors and processed in simultaneously. Depending on the needs of the business, the number of these devices or nodes may be expanded or decreased. Traditional RDBMS does not support systems that can scale to handle massive amounts of data.
Hadoop also has impressive speed, known to process terabytes of unstructured data in minutes, while processing petabytes of data in hours, based on its distribution system.HDFS is the distributed file system used by Hadoop to manage its storage. A huge file is divided into smaller file blocks and distributed among the Hadoop cluster’s nodes using the Distributed File System (DFS). Because so many file blocks are handled in parallel, Hadoop is faster than traditional database management systems and offers higher performance.
Hadoop has a default mechanism for fault tolerance. Each block of data is divided into multiple slaves which have a replica of each data chunk. Hadoop runs on inexpensive devices known as commodity hardware, which is prone to failure at any time. Hadoop offers data availability even if one of your systems crashes by replicating data across several DataNodes in a Hadoop cluster. If there is a technical problem with one machine, you can still read all the data from that one machine. Because the data is copied or replicated by default in a Hadoop cluster, it may also be read from other nodes.
In general terms, ‘throughput’ is defined as the net amount of work done per unit of time. And Hadoop is higher throughput. The distributed file system used by Hadoop allows for the assignment of different tasks to different data nodes within a cluster, which results in high throughput processing of the data. Throughput is simply the amount of work completed in a given amount of time.
Minimum Network Traffic
10Gb Ethernet network is the minimum recommended configuration for Hadoop nodes. Hadoop nodes should have a minimum of 100GB of memory and at least four physical cores. Each work in the Hadoop cluster is broken down into a number of smaller subtasks, which are then assigned to each data node that is available. Low traffic in a Hadoop cluster results from the little amount of data processed by each data node.
You may also like the following post
- STA Interview Questions
- VLSI Interview Questions
- Analog Layout Interview Questions
- VLSI Interview Questions with Answers
- Physical Design Interview Questions
- Digital Electronics Interview Question
- Memory Circuit and Layout Design Interview Question#