What is Hadoop?

Apache Hadoop is a free and open source framework for storing and processing massive information ranging in size from gigabytes to petabytes. Hadoop enables the analysis of enormous datasets in parallel by clustering numerous computers rather than requiring a single large machine to store and analyze the data. Join Hadoop Training in Chennai at FITA Academy offers the best training with the Placement Assistance. This blog will explain about what is hadoop used for, I hope this will help you to learn about Hadoop.

Hadoop is made up of four major modules:

HDFS (Hadoop Distributed File System)- HDFS (Hadoop Distributed File System) is a distributed file system that runs on low-end or basic hardware. In terms of data performance, fault tolerance, and native support for large datasets, HDFS exceeds traditional file systems.

YARN (Yet Another Resource Negotiator)- YARN (Yet Another Resource Negotiator) is a tool for controlling and monitoring cluster nodes and resource usage. It maintains track of all of the jobs and tasks that need to be completed.

MapReduce- MapReduce is a framework that aids programmes with parallel data processing. The map task turns input data into a dataset that can be computed using key-value pairs. Reduce tasks consume the output of the map task in order to aggregate it and produce the required result.

Hadoop Common — Provides a set of shared Java libraries that may be used by all Hadoop modules.

Hadoop’s Operation

Hadoop makes it easy to make use of all of a cluster server’s storage and processing capability, as well as to run distributed operations on massive volumes of data. Hadoop provides the foundation for the development of other services and applications.

Applications that collect data in numerous forms can connect to the NameNode via an API call and place data into the Hadoop cluster. The NameNode keeps track of the file directory structure and placement of “chunks” for each file, which is repeated among DataNodes. Create a Map Reduce job consists of numerous map and reduce jobs that run on HDFS data dispersed across DataNodes in order to perform a job to query the data. Reducers operate on each node to collect and organize the final output, as well as to map tasks completed on each node to the input files provided. Join Hadoop Training in Bangalore at FITA Academy to enhance your technical skills in Hadoop domain.

Because of its extensibility, the Hadoop ecosystem has evolved tremendously over time. The Hadoop ecosystem now comprises a variety of tools and applications for collecting, storing, processing, analyzing, and managing large amounts of data.

The following are some of the most popular applications:

Spark is an open source distributed processing engine for big data applications that is widely utilised. General batch processing, streaming analytics, machine learning, graph databases, and ad hoc queries are all supported by Apache Spark, which uses in-memory caching and optimised execution for fast performance.

Presto- Presto is a distributed SQL query engine designed for ad-hoc data processing with low latency. The ANSI SQL standard supports complex queries, aggregations, joins, and window functions. Presto can handle data from a variety of sources, such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3).

Hive – Provides a SQL interface for leveraging Hadoop MapReduce, allowing for massive-scale analytics as well as distributed and fault-tolerant data warehousing.

HBase- HBase is a non-relational, versioned open source database that works on Amazon S3 (through EMRFS) or the Hadoop Distributed File System (HDFS). HBase is a massively scalable, distributed big data store designed for real-time access to tables with billions of rows and millions of columns in a random, rigorously consistent manner.

Zeppelin- Zeppelin is an interactive notebook that allows you to explore data in real time.

Hadoop on Amazon Web Services

Amazon EMR is a managed service that allows you process and analyze massive datasets on completely customized clusters utilizing the newest versions of big data processing frameworks including Apache Hadoop, Spark, HBase, and Presto.

Easy to set up: An Amazon EMR cluster may be set up in minutes. Node provisioning, cluster setup, Hadoop configuration, or cluster optimization are all taken care of for you.

Cost-effective: The Amazon EMR pricing structure is straightforward and predictable: You pay an hourly cost for each instance hour you use, and you can save even more money by using Spot Instances.

Elastic: You can provision one, hundreds, or thousands of compute instances using Amazon EMR to analyze data at any scale.

Transient: EMRFS can be used to run clusters on-demand based on HDFS data that is persistently saved in Amazon S3. You can shut down a cluster once jobs are completed and have the data preserved in Amazon S3. You just pay for the amount of time the cluster is in use.