Research Article May 2017 Special Issue of International Conference on Emerging Trends in Science & Engineering (ICETSE–2017) Conference Held by IEAE India, at Coorg Institute of Technology, Ponnampet, Karnataka, India © www.ermt.net , All Rights Reserved Page | 829 International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-6, Issue-5) Big Data-based In-Memory Data Grid (IMDG) Technologies: Challenges of Implementation by Analytics Tools Abdo H. Guroob Faculty of Applied Science and Technology Hodeida University, Hodeida, Yemen Manjaiah D.H. Dept. of Computer Science, Mangalore University, Mangalore India Abstract: In recent years, the amount of data has increased in most fields, from the global economy to society administration, and from scientific researchers to national security, so big data has become the effort of researchers, pioneers of the information technology, large companies, and big institutions. When the data becomes big to the extent difficult to manage it by using traditional data management systems begin with the emergence of challenges as of how can manage this data, collect the data, store the data, analyze the data for utilizing of them. Processing-based In-memory has been newly topic lately. Dealing with Big Data needs to increase the number of transactions with minimum execution time, a lot of applications and companies changing their core systems architectures to minimize the time of execution and throughput by using the main memory RAM during implementation. This is because the time of implementation in the main memory faster and cheaper to load the entire operational dataset into memory. In this paper, we present different results of using the traditional data management system and using the In- memory data grid for big data implementation and application by Alea simulator, which is used to study advanced scheduling techniques for planning various types of jobs in Grid Environments with different mode parameters. Keyword — Big Data, Data Grid, In-Memory Data Grid (IMDG), Java Heap, Alea Simulator. I. INTRODUCTION Handling the huge volume of data is becoming a real challenge. Most the data of organizations is growing at a rate of 40 to 60 percent per year. Managing huge streams of data from various distinct sources lead to a lot of companies have difficulty recognizing the right data and defining how to best use it. Data Grid one of the approaches, which addresses problems of big data. Data Grid defined as a set of services that offer persons and organizations the ability to access to geographically distribute huge data for research determinations like the Large Hadron Collider (LHC), the Laser Information Gravitational Wave Observatory (LIGO), and the Sloan Digital Sky Survey (SDSS). In addition, Data Grid used to increase services and decrease costs by providing access to dispersed and dispersed data systems such as governments, hospitals, schools, and businesses. Big data is a collection of data sets that exceeds the processing volume of traditional database management systems. Big Data has improved the approach that we implement in exploit businesses, managements, and researches. Data- intensive science, particularly in data-intensive computing is coming into everywhere that aims to afford the tools that we need to manage the Big Data problems. To capture the value from Big Data, we need to improve new techniques, technologies for analyzing it, and no conventional techniques to powerfully handle a large volume of data within limited run times. So far, researchers have established a wide variety of techniques and technologies to hold, analyze and visualize Big Data. However, they are far away from meeting the variety of needs. Figure 1: increasing the volume and discipline of data II. IN- MEMORY DATA GRID (IMDG) An in-memory data grid (IMDG) is a particular type of data storage (keep data in main memory) and distribute it across the servers of clusters, thus offers the very high availability of data. Guaranteeing speeds, by loading data in- memory, and size by using scalability structures provided by a cluster. Thus, by loading huge volume of data (Terabytes) into memory IMDGs are capable of working with most of the Big Data processing requests today.