Big Data Technologies: Additional Features or Replacement for Traditional Data Management Systems?

With the data volume that does not stop growing and the multitude of sources that led to diversity of structures, the classic tools of data management became unsuitable for processing and unable to offer effective tools for information retrieval and knowledge management. Thereby, a major challenge has become how to deal with the explosion of data to transform it into new useful and interesting knowledge. Despite the rapid development and change of the databases world, this data management systems diversity presents a difficulty in choosing the best solution to analyze, interpret and manage data according to the user’s needs while preserving data availability. Hence, the imposition of the Big Data in our technological landscape offers new solutions for data processing. In this work, we aim to present a brief of the current buzz research field called Big Data. Then, we provide a broad comparison of two data management technologies

Using Hadoop to Support Big Data Analysis: Design and Performance Characteristics

Today, the amount of data generated is extremely large and is growing faster than computational speeds can keep up with. Therefore, using the traditional ways or we can say using a single machine to store or process data can no longer be beneficial and can take a huge amount of time. As a result, we need a different and better way to process data such as having data distributed over large computing clusters. Hadoop is a framework that allows the distributed processing of large data sets. Hadoop is an open source application available under the Apache License. It is designed to scale up from a single server to thousands of machines, where each machine can perform computations locally and store them. The literature indicates that processing Big Data in a reasonable time frame can be a challenging task. One of the most promising platforms is a concept of Exascale computing. This paper created a testbed based on recommendations for Big Data within the Exascale architecture. This testbed featured three nodes, Hadoop distributed file system. Data from Twitter logs was stored in both the Hadoop file system as well as a traditional MySQL database. The Hadoop file system consistently outperformed the MySQL database. The further research uses larger data sets and more complex queries to truly assess the capabilities of distributed file systems. This research also addresses optimizing the number of processing nodes and the intercommunication paths in the underlying infrastructure of the distributed file system. HIVE.apache.org states that the Apache HIVE data warehouse software facilitates reading, writing, and managing large datasets residing in distributes storage using SQL. At the end, there is an explanation of how to install and launch Hadoop and HIVE, how to configure the rules in a Hadoop ecosystem and the few use cases to check the performance

Parallel programming paradigms and frameworks in big data era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.Peer ReviewedPostprint (author's final draft

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data)