High Performance Big Data Analytics

Data analytics, data science and big data are a just a few of the many topical terms in business and academic research trends. This course provides an in-depth coverage of special topics in big data from data generation, storage, management, transfer, to analytics, with focuses on the state-of-the-art technologies, tools, architectures, and systems that constitute big data computing solutions in high performance networks. Real-life big data applications in various domains (particularly in sciences) are introduced as use case. It will also cover:

• Key data analytical techniques
• Relevant HPC paradigms and data infrastructures
• Case studies from academia and business

The aims of this course are:

• To discuss what data analytics and big data are.
• To describe why big and fast data analytics need high-performance infrastructures.
• To explain the high-performance paradigms for big data.

Upon the successful completion of this course, students should be able to:

• assess knowledge of the common, popular, important data analytics techniques.
• express how data analytic techniques scale and perform on HPC infrastructure.
• apply big data analytic tools.

Textbooks:

1. High-Performance Big-Data Analytics – Computing Systems and Approaches. Pethuru Raj, Anupama Raman, Dhivya Nagaraj • Siddhartha Duggirala. Springer, 2015.

References:

1. “Big Data Application Architecture Q & A: A Problem , Solution Approach” by Nitin Sawant and Himanshu Shah
2. Big Data Fundamentals: Concepts, Drivers & Techniques, Thomas Erl,‎ Wajid Khattak,‎ Paul Buhler, Prentice Hall; 1 edition, 2016
3. Big Data Analytics with R and Hadoop, Vignesh Prajapati, Packt Publishing Ltd., 2015
4. Apache Hadoop: http://hadoop.apache.org/