What is considered big data? Big data refers to the applications specifically designed to analyze, manage, and extract useful information from extremely large data sets. Big data has become a significant part of enterprise computing, and many companies are already using big data analytics as an integral part of their business strategy. Businesses are realizing the value in data mining, analytical processing, and pre-quergence solutions.
Distributed file system (DFS) is one of the two big data technologies on the market today. A DFS serves as a server for massive, complex storage and retrieval of data. It is typically used in large-scale data collection and analysis. With DFSs, data creation and management become significantly more manageable because there is less need to centrally manage and protect individual servers. DFSs can scale up and down as needed, making it perfect for rapidly expanding businesses or even for professionals who are not in a position to commit to a massive overhaul of their data management systems.
The second big data technology on the market today is Apache Hadoop. This open source framework is based on the Map-reduce framework and was developed by Google for use in their own Map-reduce cluster. Map-reduce is a framework for distributed application execution that makes it easier to manage the complexity of large data sets. Distributed Map-reduce therefore lets users define a function that maps key terms and phrases over a cluster of machines. The framework then generates and stores data on each node in the cluster. Map-reduce guarantees performance through multiple randomly-distributed executions on each machine.
While both Apache Hadoop and Map Reduce offer significant capabilities, they differ primarily in terms of how they will be used. In Hadoop, the user is responsible for designing the network, working out algorithms, storing the data, analyzing the data, and updating the application. The Map Reduce approach, meanwhile, is implemented entirely in the server and therefore the user is not involved in the initial design and implementation of the framework. The big difference between the two is that while Hadoop can scale up to terabytes in a single server, Map Reduce is designed to work with much smaller systems.
Apache Hadoop and Map Reduce both make use of the same core principles. Both are based on the idea of using the web’s indexing systems to deliver data to application servers and the storage devices on the network. They also make use of large scale parallel processing technologies for increasing the speed at which data is processed. Apache Hadoop also offers support for the Apache web server and the Java servlets, making it highly compatible with various open source software.
Map Reduce also offers support for the popular Hadoop Distributed Memory Architecture (HADCA), making it more useful when used in applications that require high amounts of data per server. The aim of Hadoop, after all, is to provide a system that is fast enough to be able to process large amounts of data without requiring the system to slow down. Hadoop aims to do this by providing an abstraction layer that makes database access easy and allows the application to scale up without causing significant memory strain. HDFS, the file system that drives Hadoop, is also responsible for handling large amounts of data much more efficiently than traditional file systems can.
Google and Facebook have been active participants in the cloud computing community for some time now. Their long-term approach towards using large amounts of data from their respective servers to make their online services better has been controversial. Part of the controversy has centred on the concerns over the security of data provided to external parties. However, Google’s announcement that it will be supporting Hadoop, in addition to its partnership with AWS, provides another piece of evidence that cloud computing may be on its way out.
As this emerging field continues to expand, there will be a greater emphasis being placed on what is considered big data. Large companies are already adopting different strategies to leverage big data in order to make their business decisions more efficient. The question then is how business managers will react once they learn that the biggest players in the industry today are willing to invest in developing tools that reduce the need for IT support technicians to handle such tasks. This shift will no doubt cause major changes in how IT professionals think about big data.