Hadoop Features: The Secret Sauce For Big Data Success
Hadoop is a framework developed in Java with some C and Shell Script code that utilises a variety of simple commodity hardware to process big datasets using a very fundamental level of programming. It was created by Doug Cutting and Mike Cafarella and is currently available under the Apache License 2.0. Hadoop will now be seen as a talent that every data scientist and everyone working with big data will need to learn.
It is receiving significant investment from businesses, and it will soon be a sought-after expertise. It is an open-source software framework used for storing and processing large amounts of data distributed across a cluster of commodity hardware. Some of the key features of Hadoop are:
- Scalability: Hadoop is generally designed to scale up from a single server to thousands of machines, each offering local computation and storage.
- Fault tolerance: Hadoop is designed to be fault-tolerant, meaning it can continue to operate even if one or more of the machines in the cluster fail.
- Data locality: Hadoop stores data on the machine where it is processed, which allows for faster data processing compared to moving data between machines.
- Data locality: Hadoop stores data on the machine where it is processed, which allows for faster data processing compared to moving data between machines.
- Flexibility: Hadoop allows users to store and process structured, semi-structured, and unstructured data using the same framework.
- Ecosystem: Hadoop has a large and active developer community, which has produced a wide range of tools and libraries for data processing, data analysis, and machine learning.
- Cost-effectiveness: Hadoop allows users to inexpensively store and process large amounts of data using commodity hardware, which can be more cost-effective than using specialized, high-performance systems.
- Open Source: Hadoop is free to use because it is an open-source programme. Being an open-source project, the source code is accessible online for anybody to read and modify as needed to meet industry standards.