What is Hadoop

 Hadoop is an open source, Java based framework used for storing and processing big data. The data is stored on inexpensive commodity servers that run as clusters. Its distributed file system enables concurrent processing and fault tolerance. Developed by Doug Cutting and Michael J. Cafarella, Hadoop uses the MapReduce programming model for faster storage and retrieval of data from its nodes. The framework is managed by Apache Software Foundation and is licensed under the Apache License 2.0.

For years, while the processing power of application servers has been increasing manifold, databases have lagged behind due to their limited capacity and speed. However, today, as many applications are generating big data to be processed, Hadoop plays a significant role in providing a much-needed makeover to the database world.

From a business point of view, too, there are direct and indirect benefits. By using open-source technology on inexpensive servers that are mostly in the cloud (and sometimes on-premises), organizations achieve significant cost savings. Know more hadoop training

Additionally, the ability to collect massive data, and the insights derived from crunching this data, results in better business decisions in the real-world—such as the ability to focus on the right consumer segment, weed out or fix erroneous processes, optimize floor operations, provide relevant search results, perform predictive analytics, and so on.

How Hadoop Improves on Traditional Databases

Hadoop solves two key challenges with traditional databases:

1. Capacity: Hadoop stores large volumes of data.

By using a distributed file system called an HDFS (Hadoop Distributed File System), the data is split into chunks and saved across clusters of commodity servers. As these commodity servers are built with simple hardware configurations, these are economical and easily scalable as the data grows.

2. Speed: Hadoop stores and retrieves data faster.

Hadoop uses the MapReduce functional programming model to perform parallel processing across data sets. So, when a query is sent to the database, instead of handling data sequentially, tasks are split and concurrently run across distributed servers. Finally, the output of all tasks is collated and sent back to the application, drastically improving the processing speed.Know more hadoop training

5 Benefits of Hadoop for Big Data

For big data and analytics, Hadoop is a life saver. Data gathered about people, processes, objects, tools, etc. is useful only when meaningful patterns emerge that, in-turn, result in better decisions. Hadoop helps overcome the challenge of the vastness of big data:

1. Resilience — Data stored in any node is also replicated in other nodes of the cluster. This ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.
2. Scalability — Unlike traditional systems that have a limitation on data storage, Hadoop is scalable because it operates in a distributed environment. As the need arises, the setup can be easily expanded to include more servers that can store up to multiple petabytes of data.

Low cost — As Hadoop is an open-source framework, with no license to be procured, the costs are significantly lower compared to relational database systems. The use of inexpensive commodity hardware also works in its favor to keep the solution economical. Know more hadoop training

1. Speed — Hadoop’s distributed file system, concurrent processing, and the MapReduce model enable running complex queries in a matter of seconds.
2. Data diversity — HDFS has the capability to store different data formats such as unstructured (e.g. videos), semi-structured (e.g. XML files), and structured. While storing data, it is not required to validate against a predefined schema. Rather, the data can be dumped in any format. Later, when retrieved, data is parsed and fitted into any schema as needed. This gives the flexibility to derive different insights using the same data.