Big Data Analytics with Hadoop (3 credits)
In this course, students will learn cutting edge technologies and
concepts related to analysis of big data – data that is too large to process
in the main memory of one computer. The course is organized into two parts. In the first part,
students will build upon their understanding of RDBMS and SQL,
and explore the use of SQL-like queries in a big data environment
(Hadoop distributed file system – HDFS), using tools such as Sqoop, Pig and Hive.
Students will identify typical situations that warrant large data analysis,
move data between relational databases and Hadoop using Sqoop, manage data in HDFS,
and use Pig and Hive to run distributed queries on data.
In the second part of the course, students will build upon their understanding of
data mining techniques and learn to apply them to analyze large datasets.
Students will use Apache Mahout software in the Hadoop ecosystem to explore
item-based collaborative filtering, non-distributed recommenders, frequent itemset mining,
clustering, and some text mining algorithms, including Naïve Bayesian classifier.
Course includes: SQL-like querying in big data cluster; systems, classifiers,
clustering; Hadoop ecosystem overview; and deep dive into Hadoop Pig, Hive and Mahout.