Hadoop is an open-source data processing tool developed by the Apache Software Foundation. In this article, I will explain what Hadoop is and how it is used in data science.
Introduction to Hadoop
Hadoop is currently the go-to program for processing huge volumes and varieties of data because it was designed to make large-scale computing more affordable and flexible. With the advent of Hadoop, mass data processing was introduced for many more people and organizations.
Hadoop can offer you a great solution to manage, process, and aggregate massive streams of structured, semi-structured, and unstructured data.
By setting up and deploying Hadoop, you get a relatively affordable way to start using and pulling insights from all the data in your organization, rather than continuing to rely solely on that transactional dataset you’re in. an old data warehouse somewhere.
Why Hadoop in Data Science?
Hadoop is one of the most popular programs available for large scale computing needs. Hadoop provides a mapping and reduction layer capable of handling the data processing requirements of most big data projects.
Sometimes the data gets too big and too fast for even Hadoop to handle. In these cases, organizations turn to alternative and more customized MapReduce deployments instead.
Hadoop uses basic hardware clusters to store data. Each cluster’s hardware is connected, and that hardware is made up of core servers – low-cost, low-performance generic servers that provide powerful compute capabilities when run in parallel on a shared cluster.
These base servers are also called nodes. Standardized computing dramatically reduces the costs of managing and storing big data.
What does Hadoop Include?
Hadoop consists of the following two components:
- A distributed processing framework: Hadoop uses Hadoop MapReduce as a distributed processing framework. Again, a distributed processing framework is a powerful framework in which processing tasks are distributed across clusters of nodes so that large volumes of data can be processed very quickly throughout the system.
- A Distributed File System: Hadoop uses the Hadoop Distributed File System (HDFS) as its distributed file system.
The workloads of the applications that run on Hadoop are distributed among the nodes of the Hadoop cluster, and then the output is stored on the HDFS. The Hadoop cluster can be made up of thousands of nodes.
To reduce input/output (I / O) process costs, Hadoop MapReduce jobs are performed as close to the data as possible. This means that the job reduction processors are positioned as close as possible to the outgoing card job data that needs to be processed. This design makes it easy to share computational requirements in the big data processing.
Hadoop also supports the hierarchical organization. Some of its nodes are classified as master nodes and others are classified as slaves. The master service, called JobTracker, is designed to control multiple slave services. Slave services (also called TaskTrackers) are distributed one to each node.
The JobTracker monitors TaskTrackers and assigns them Hadoop MapReduce tasks. In a newer version of Hadoop, known as Hadoop 2, a resource manager called Hadoop YARN has been added. When it comes to MapReduce in Hadoop, YARN acts as an integrated system that performs resource management and scheduling functions.
Hadoop processes the data in a batch. Therefore, if you are working with real-time streaming data, you will not be able to use Hadoop to handle your big data issues. Having said that, it is very useful for solving many other types of big data problems.
Hope you liked this article on what Hadoop is and why it is used in data science. Please feel free to ask your valuable questions in the comments section below.