According to a new report from IBM, 90% of all the data in the world was generated in the last two year alone, with about 2.5 quintillion data created each day. This rate is expected to grow at an enormous rate. Therefore, to solve major issues pertaining to big data, a new architecture was required, which could facilitate the storage and processing of such data. Based on the revolutionary MapReduce methodology of processing data, Doug Cutting and Mike Cafarella were inspired to create Hadoop. The Hadoop framework is designed with a much simpler storage infrastructure facility, which offers scalability from a single node to thousands of nodes seamlessly. The Hadoop architecture, instead of storing all information on one system, distributes the data and processing to different nodes along the system along with maintaining copies. So how does this facilitate the storage and handling of big data?
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Benefits of Hadoop Architecture
The Hadoop architecture relies on additional nodes when the storage requirement increases. Adding a node to the system is a straightforward task, unlike in traditional relational database systems whose storage is mostly fixed to a pre-specified level. It makes use of multiple inexpensive servers to store extensive data sets; thereby, enabling businesses to run the application on thousands of nodes involving thousands of terabytes of data.
The only option of storing and processing data available to business previously was maintaining a traditional relational database. The cost involved has a lot to do with the infrastructure, server, hardware, and energy. Businesses had to spend a lot to set up such facilities to store their data. The cost shot up to such a level that companies had to decide which data were more valuable to them so that they could dump other data to save on costs. The problem with such a process becomes evident when businesses change their priorities or want to perform some analysis. The Hadoop architecture solves all such problems by storing the data across multiple nodes maintained in an inexpensive server. That way, companies can keep hold of all their data, even if they are deemed unhelpful at present. The savings associated with this method is significant as the cost of storing one terabyte of data can be reduced to mere hundreds of dollars instead of tens and thousands of dollars.
The Hadoop architecture’s unique storage model is based on a distributed file system. The data is divided into different part and stored across multiple nodes, which are then placed in separate racks. So instead of using a single system to store and process all data, each node can process the data they have stored simultaneously. For instance, instead of processing 4TB of data, Hadoop can distribute the data into four parts of 1TB each to four different nodes. Each node can process the data independently, thereby freeing the much-needed bandwidth and allowing for easy access to information. This architecture can process terabytes of data in just a few minutes.
Hadoop architecture grants businesses easy access to new data sources and tap into different types of structured and unstructured data to generate valuable insights. It enables enterprises to derive business insights from data sources such as email conversations, social mentions, or clickstream data. Additionally, it can also be used for recommendation systems, log processing, data warehousing, market campaign analysis and fraud detection.
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A question may arise in the mind that since a part of the data is stored in different nodes, what happens when the data in a particular node is compromised or lost. The Hadoop architecture is designed in such a way that it copies such data at different nodes. Also, some recent frameworks allow multiple copies to be created in different nodes, and one copy to be created in a node along a different rack. Such an architecture makes the system failure proof, and in the event of data loss, the lost data can be easily retrieved from a different node.