Hadoop Data Science – An Important Guide In 3 Points
Introduction
The world is moving towards a digitally prone environment each day, and the data being produced, stored and processed is also increasing exponentially. This high volume of data needs to be processed, and there are various technologies available in the market for this. One such technology is Hadoop. Hadoop is responsible for processing huge amounts of data and storing it within the necessary time. As you read further, you will learn all that there is to know about the Hadoop data science and what a Hadoop data scientist offers.
1) What is Hadoop?
Apache Hadoop is an open-source software that’ serves as a powerhouse when dealing with huge amounts of data. This software enables a network of computers to solve problems requiring computation power and massive data sets. The voluminous data which is difficult to process by traditional data processing methods, technologies and tools use Hadoop data science to their advantage. Hadoop can be programmed using different languages like Python, Perl, Ruby, and C.
There are three main components for Hadoop data science:
- Hadoop Distributed File System: This storage component of Hadoop data science is a collection of master-slave networks. In Hadoop Distributed File System, there are two nodes namely data node and name node. Name node runs on the master node that stores the addresses and locations of the chunks of data in different servers and data node runs on slave nodes which act as a backup in an event of node failure. There is no transferring of data required for the initial processing, and computation occurs when the data is stored.
- Hadoop Map – Reduce: This component of Hadoop is responsible for processing data on a higher level. This module operates on two nodes – Map & Reduce. Map workers do the job of systematically organising the data after fetching from the file system and passing it onto a buffer. Reduce worker allocate and cut down the data and lay it down in the format given by the Master node. This procedure facilitates data being converted into an easily readable and manageable form.
- Hadoop Yet Another Resource Negotiator (YARN): this component of Hadoop data science is used for job scheduling and resource management. This module allows us to manage and control resources efficiently and effectively. YARN involves three components.
- Client: their job is to forward job to resource managers
- Resource manager: supervises all activities, schedules tasks, and allocates resources and much more.
- Node manager: it monitors health, logs necessary metrics, manages resources, and is directly responsible to the resource manager.
2) Why do we need Hadoop for data science?
Hadoop is essential for data science. Data science has been evolving continuously in today’s world. It is an interdisciplinary field which uses scientific methods, algorithms, processes, and systems to insight and extracts knowledge from all kinds of data. Hadoop data scientists are trained for analysing, extracting, and generating predictions from the big data. The main purpose of Hadoop is to store the voluminous amount of data – both structured and unstructured data.
Hadoop also provides Pig and Hive for analysis data on a large scale. Knowing Hadoop data science it will enable a Hadoop data scientist to increase his expertise in data science and will make you versatile in handling the huge chunks of data. Hadoop also increases your position in the market and will serve a competitive advantage over other firms.
3) Use of Hadoop in Data Science
Hadoop is one of the most popular technologies present in the data science environment. If you’re looking to start a data science career, you must know Hadoop data science and handling large volumes of data both structured and unstructured. Hadoop is important because it not only teaches you to handle huge chunks of data but also proves to be an ideal platform for those using it. Hadoop data science also teaches you about the various extensions Mahout and Hive. Over the past years, the use of Hadoop in data science has been increasing to implement data science tools in the industries. Hadoop has impacted data scientists in four different ways:
- By data being explored through large data sets.
- Pre-processing large scale data.
- Enforcing data agility and
- Facilitating large scale data mining.
Conclusion
From the above blog, we can conclude that to take a step into the world of data science one must know about Hadoop technology. Hadoop Ecosystem is reliable and scalable. Hadoop is widely used by firms producing huge chunks of data, storing it and processing it. Hadoop has evolved to turn into a comprehensive data science platform. More and more firms are using Hadoop and one such example Marks & Spencer who are making use of Hadoop data science to analyse customer purchase patterns and manage stock. After going through the blog, we hope you have understood Hadoop and Data Science.
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