The Apache Hadoop Designed to scale up from a solitary server to hundreds of machines, the Apache Hadoop allows distributed processing of vast datasets across clusters of computers using effortless programming models. Rather than depending on the hardware to deliver high availability, the Hadoop library itself is crafted to detect and handle errors at the application layer. At the heart of the Hadoop system lies a MapReduce framework which writes applications to process huge amounts of information on large collections of commodity hardware, reliably. The MapReduce programming paradigm also allows enormous scalability among numerous servers in a Hadoop cluster. Hadoop generally, is an open implementation of the MapReduce model that is developed to deal with distributed databases. The Role of Hadoop With Hadoop in the market today, CIOs are re-planning their enterprise data architecture where data, which was previously expensive to manage, can be made easily available for analysis to improve business insights. One of the elements highlighting Hadoop is its unique ability to make the described functions as a distributed system rather than centralized architectures as in the case of traditional systems. In a database that uses multiple machines, the work tends to be divided out with most of the data sitting on one or more machines, and data processing software housed on another server. While on the Hadoop cluster, the data within Hadoop Distributed File System (HDFS) and the MapReduce system are housed on every machine in the cluster. This not only adds redundancy to the system when a machine in the cluster goes down, but also maintains the data processing software in the same machine where data is stored, speeding up the information retrieval. As Hadoop has been linked to the cloud and cloud deployment, it has attributes that both align and misalign with enterprise needs and mobile applications. Also, as Hadoop's value is significant for large databases used by mobile applications, Hadoop might become an overload. The system has significant setup and processing overhead resulting in severe time consumption even if the amount of data correlated is modest. Hadoop’s inability in supporting data structures with multi-dimensional context has also added up to the misaligning facts. In addition, the system is less helpful in problems that have to be viewed iteratively or as several sequential dependent steps. Even though the aforementioned points indicate that mobile applications design should not be as a new Hadoop application, the existing Hadoop applications can be exploited to fulfill the needs of mobile applications. The Hadoop Application Strength With the introduction of Hadoop, users across industries are able to bring structured, unstructured, and semi-structured data sources together on one platform to perform precise study for improved interactions and day-to-day operations. The Hadoop system performs commendably when it comes to scheduled daily running tasks, aimed at either unstructured or semi-structured formats. Such a combination should have existed as the base of projects for the users of Hadoop or for the organizations where Hadoop is a necessity. Hadoop's supremacy lies in its unique ability to handle complicated tasks on particular data types at specific volume levels and distributability, where any of those attributes are not present, Hadoop may not pay off. Integrating Hadoop for Mobile As companies grow, they should prepare themselves to handle the enormous data movements, which the mobile applications can cause. Reports suggest that enterprises are considering support for productivity and mobility enhancement for the mobile workers with high priority application group. This reflects the fact that most companies leveraging Hadoop will probably have to amalgamate the framework with mobile applications for better productivity. The Hadoop and mobile integration process generally includes three general segments, starting with identifying Hadoop’s inbuilt boundaries in mobile usage. The second stage is the process of developing practical Hadoop application frameworks and finally troubleshooting and support Hadoop in mobile applications. While some of the Hadoop applications are not compatible, and designing mobile applications for Hadoop may not be worth, the existing Hadoop applications can be adopted to fulfill the current mobile application needs. Adopting Existing Hadoop Applications To improve the productivity and continuity for the mobile workers, adopting Hadoop can be an icebreaker. The most apparent technique for organizations is to adopt Hadoop data for mobile applications to create a front-end database, but is obviously presented in a traditional centralized form. A model of Hadoop executed on a scheduled basis, creates another database that a mobile application can query, which will not require changes to the method of its use. The changes are generally unnecessary as most of the Hadoop frameworks are in batch mode; improving the ease of use. This in addition results in a smaller database and coalition of information suitable to mobile response time requirements. A combination of software and database architects in organizations can make Hadoop compatible with mobile applications. Architects must research and analyze data from other sources to get a complete representation of mobile user needs. Without some Hadoop overlay, its generally difficult for the Hadoop real-time application users to make the data available to broad user range and also to make it an integral part of any IT operation. To maximize information from Hadoop, users considering framework for mobile applications should restructure their Hadoop to Hive project- another Apache application that convert a query language to MapReduce jobs. Hive from the Apache project automates the process of transforming queries from conventional SQL sources into Hadoop compatible queries like MapReduce. The data warehouse system creates additional indexes as well and provides tools for real-time Hadoop access. However, Hive cannot replace Hadoop for an effective database chain of command, developed to insert summaries or abstraction layers between Hadoop and real-time users. Hive cannot be a complete solution to the real-time mobile problems but can be a tool in structuring a Hadoop-centered warehouse. Hadoop is transforming businesses across industries, where they found benefitting from the processing power that Hadoop offers, becoming more data-driven, and gaining deeper insights to form viable strategies around customers and operations.
