Transform Your Data Science Game with Hadoop: Handle Big Data like a Pro

Hadoop is an open-source software framework which is widely used for distributed processing of large datasets. It is a popular choice for data science projects because it allows users to store and process huge amounts of data quickly and properly. Large data sets can be processed using straightforward programming paradigms by using the open-source Hadoop External software framework. Hadoop is made to expand from a single server to thousands of devices.

Hadoop is based on the MapReduce programming model, which is designed to process large datasets in parallel across a cluster of machines. It consists of two core components: the Hadoop Distributed File System (HDFS), which is used to store data, and the MapReduce engine, which is used to process data.

In data science, Hadoop is often used to store and process large datasets that are too large to  get processed on a single machine. It is well-suited for tasks such as data preparation, data cleaning, and data exploration, and it can be used with a variety of programming languages and tools, including Python, R, and SQL.

Overall, Hadoop is a powerful and flexible tool for data science that is widely used in a variety of applications, including machine learning, data analytics, and more. It is a popular choice for data scientists who need to process and analyse large amounts of data quickly and efficiently.

Hadoop evolved from Doug Cutting and Mike Cafarella’s Nutch open-source search engine. In the early days, the two tried to come up with a mechanism to distribute data and calculations among various computers so that several activities could be carried out concurrently in order to provide web search results more quickly.