Hadoop is an open source distributed processing framework that is commonly used to process and store big data. Not only does it store but also helps in running applications on clusters of commodity hardware.
Software solutions like Hadoop have become a necessity for businesses that store & use big data due to its ability to store & process massive amounts of data. A classic feature of Hadoop is that it can handle both structured & unstructured data thus providing enhanced flexibility to users.
Reasons To Implement Hadoop
- Massive Storage – With big data increasing rapidly, there was a need for a software solution that could handle large volumes of data. Hadoop has solved that problem with its ability to hold enormous amount of data. The data is often split into blocks and then stored in clusters.
- Scalability – You wish to have the ability to handle more data? No problem, with Hadoop you can do that easily and that too without any hassle. With little administration Hadoop allows you to add more nodes easily.
- Flexibility – Hadoop enables you to store as much data as you want whether it is structured or unstructured, formatted or encoded or in any other form. So apart from text you can store images, videos, etc. Also, with Hadoop you are not required to process data before storing it, thus making it relevant as well as simple to use.
- Computing – Its computing model helps in processing large amounts of data easily. The arithmetic is simple; more the nodes, more the processing power that you have.
- Fault Tolerance – Hadoop is fault tolerant i.e. it protects the data in case of any hardware failure. When a node gets lost or goes down, Hadoop framework redirects the task/work to another node to avoid any disruption. Also Hadoop keeps a duplicate copy of data which is then stored in different clusters.
- More Real Time – It allows you to store information into a cluster and analyze it in real time. It provides a standard approach to numerous API’s for big data analytics.
- Cost Effective – It uses commodity hardware to store data which leads to reduction in cost per terabyte of storage.
When Not To Use Hadoop?
- When you wish to modify data
- There are lots of small files
- There is low latency data access
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