In 2003, Google published a paper on Google File Systems (GFS) by Ghemawat, Gobioff & Leung (2003). and a subsequent paper on the MapReduce Dean & Ghemawat (2004) model to address processing large unstructured data sets. Hadoop, an open source framework developed by the Apache Foundation, is an outgrowth of the concepts presented by Google in these papers. The Hadoop project has both a distributed file system, called HDFS (Hadoop Distributed File System) modeled on GFS and a distributed processing framework using MapReduce concepts. This course will review Big Data Analytics technologies with the Hadoop project as a backdrop.
To get a big-picture understanding of the technology innovations embodied in Hadoop, let’s start with the MapReduce programming model. The model, whose name comes from the map and reduces functions, uses a large number of computer nodes, connected via network interconnect, in a cluster fashion, to perform parallel processing across huge data sets.
Four characteristics —parallelism, fault tolerance, scalability, and data locality—are, in fact, the defining features of MapReduce systems, by White (2012):
Parallelism: Breaking the large data sets into smaller compute and storage units
makes it possible to perform analytics in parallel and therefore more efficiently.
In addition, mappers and reducers do not communicate individually, so they can run.
This course focuses on the most popularly accepted technologies. • For each of these Big Data technologies, the following subtopics are discussed: • the history and genesis of the Big Data technologies,
• problem set that this technology solves for Big Data analytics,
• the details of the technologies,
• including components,
• technical architecture,
• and theory of operations.
This is followed by technical operation and infrastructure (compute, storage, and network), design considerations, and performance benchmarks.
Finally, this course provides an integrated approach to the above-mentioned Big Data technologies.
This course provides a review and analysis of several key Big Data technologies.
Currently, there are many Big Data technologies in development and implementation; hence, a comprehensive review of all of these technologies is beyond the scope of this course.
The amount of data in the world is being collected and stored at unprecedented rates. A study by IDC Gantz & Reinsel, (2011) indicates that the world’s information is doubling every two years. Also the IDC study by Gantz & Reinsel (2011), mentions that the world created a staggering 1.8 zettabytes of information (a zettabyte is 1000 exabytes), and projections suggest that by 2020, we’ll generate will generate 50 times that amount. Big Data has been defined as, when data sets get so large, that traditional technologies techniques, and tools for extracting insights are no longer useful in a reasonable timeframe and cost-effective manner. This has spawned a new generation of technologies and corresponding considerations. Desai, Kommu & Rapp (2011) examine the cause of this explosion of Big Data, the following factors dominate: Mobility trends: Mobile devices and sensor proliferation;
• New data access: Internet, interconnected systems, and social networking;
• Open source model: Major changes in the information processing model and the availability of an open source framework What distinguishes Big Data from data in the past, however, is not just its vast volume.
The defining features of Big Data are also its variety—the sources and types of data being collected—and its velocity, the speed at which the data is flowing through the networked systems. Studies like Cisco Virtual Networking Index by Barnett, (2011) estimate that in 2016, global IP traffic will reach 1.3 zettabytes per year or 110.3 exabytes per month. Moreover, it is anticipated that there will be 19 billion networked devices by 2016. One of the most interesting aspects about Big Data is that that unstructured data is the fastest growing type of data. Unstructured data refers to information that either does not have a predefined data model or does not fit well into relational database tables. Examples of unstructured data include imagery, sensor data, telemetry data, video, documents, log files, and email files. The challenge is not only to store and manage this vast mix, but to analyze and extract meaningful value from it—and to do so in a reasonable timeframe and at a reasonable cost. Fortunately, a new generation of technologies has emerged for collecting, storing, processing, and analyzing Big Data.
1- Technologies for Big Data
2- Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce
3- Synchronizing Execution of Big Data in Distributed and Parallelized Environments
4- Parallel Data Reduction Techniques for Big Datasets
5- Techniques for Sampling Online Text-Based Data Sets
6- Big Data Warehouse Automatic Design Methodology
7- Big Data Management in the Context of Real-Time Data Warehousing
8- Big Data Sharing Among Academics
9- Scalable Data Mining, Archiving, and Big Data Management for the Next Generation Astronomical Telescopes
10- The Need to Consider Hardware Selection when Designing Big Data Applications Supported by Metadata
11- Excess Entropy in Computer Systems
12- A Review of System Benchmark Standards and a Look Ahead Towards an Industry Standard for Benchmarking Big Data Workloads
13- Accelerating Large-Scale Genome-Wide Association Studies with Graphics Processors
14- Efficient Metaheuristic Approaches for Exploration of Online Social Networks
15- Big Data at Scale for Digital Humanities: An Architecture for the HathiTrust Research Center
16- GeoBase: Indexing NetCDF Files for Large-Scale Data Analysis
17- Large-Scale Sensor Network Analysis: Applications in Structural Health Monitoring
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