Apache Hadoop is a an open-source software utilities collection. It facilitates with the help of a network of many computers. Therefore, how it solves problems involving massive amounts of data and computation. But Apache Hadoop provides a software framework for distributed storage and processing of big data by the Map-Reduce programming model.
What is Apache Hadoop used for?
Apache Hadoop is used to proficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly instead of using one large computer to store and process the data. Apache Hadoop makes it easier to utilize all the storage and processing capacity in cluster servers. Also to execute distributed processes against huge amounts of data. It provides the building blocks on which other applications and services can be built.
Different Modules of Apache Hadoop?
Hadoop consists of four main modules:
- Hadoop Distributed File System or HDFS. A distributed file system running on standard or low-end hardware. It provides better data amount than traditional file systems. Also offers high fault tolerance and native support of large datasets.
- Yet Another Resource Negotiator or YARN. YARN helps in managing and monitoring cluster nodes and resource usage. It schedules tasks and jobs.
- MapReduce is a framework with which programs do the parallel computation on data. The map task takes data input and converts it into a dataset that can be computed in key value pairs. The output of the map task is consumed by reduce tasks to collect output and provide the desired result.
- Hadoop Common offers common Java libraries that can be used across all modules.
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Amazon EMR is a managed service that lets users process and analyze huge datasets using the latest versions of big data processing frameworks such as Apache Hadoop, Spark, HBase, and Presto on fully customizable clusters.
- Therefore it is very easy to use. Users can launch an Amazon EMR cluster in minutes without worrying about node provisioning, cluster setup, Hadoop configuration, or cluster tuning.
- About cost :- It is low cost and Amazon EMR’s pricing is simple. It has an hourly rate for every instance hour you can use and leverage Spot Instances for greater savings.
- With Amazon EMR’s elasticity, you can provision many as much as thousands of compute instances to process data at any scale.
- You can use EMRFS to run clusters based on HDFS data stored in Amazon S3. You can shut down a cluster and have the data saved in Amazon S3 once the job is finished. You pay only for the compute time that the cluster was running.
- Security :- It is secure. Amazon EMR uses common security characteristics of AWS services such as Identity and Access Management (IAM), Encryption, Security groups, and AWS CloudTrail.
Apache Hadoop storage
The core of Apache Hadoop consists of a storage part, which is called Hadoop Distributed File System or HDFS. Then comes a processing part which is a MapReduce programming model. Apache Hadoop splits files into large blocks and distributes them across nodes in a cluster. Then it sends packaged code into nodes to process the data. This takes advantage of data locality, where nodes manipulate the data they have access to. This allows the dataset to process faster and efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking.