Hadoop is open source, a Java-based programming framework that stored large data sets in a computing environment. It is a part of Apache software foundation. You can run the application on a system with thousands of hardware nodes. Hadoop can handle terabytes of data. If the nodes failed, its distributed rapid data transfer can run system even if the failure of the node. This way you can lower the risk of catastrophic system failure and massive data loss, even if a significant number of nodes become inoperative.
Hadoop was created in 2006 by computer scientists Doug Cutting and Mike Cafarella. It was actually inspired by Google’s MapReduce, a framework that broken down into numbers of small parts, which also called blocks or fragments. After many years of development with open source community, born Hadoop 1.0. And being available for public use in November 2012 under Apache project.
Following Big data challenges before Hadoop
Here is how Hadoop solves these problems:
Big investment in creating a server with high processing power: Hadoop clusters work on normal hardware and keep multiple copies to ensure the reliability of data. A maximum of 4500 machines can be connected together using Hadoop.
Time-consuming: To save time, the process is divided into small pieces and run parallel, hence saving time. A maximum of 25 Petabyte (1 PB = 1000 TB) data can be processed using Hadoop.
If you had a long query, think about if an error had occurred at the last step. You will waste so much time doing that iteration again: Hadoop creates back up data-sets at every level. It also executes a query on duplicate data-sets to avoid data loss in case of any programmatic failure. These steps make Hadoop processing more accurate and precise.
Query building difficulty: Queries in Hadoop are as simple as coding in any language. You just need to change the way of thinking about building a query to enable parallel processing.
When not to use Hadoop?
Till now we see, how Hadoop handle big data. But in some places, Hadoop implementation is not recommended. Here are some of those scenarios:
Low-cost data access: Quickly access data.
Multiple data modifications: Hadoop is better fit only if your primary requirement of data reading not writing data.
Collection of small files: Hadoop is better if you have few but large files.
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