This engaging hands-on course introduces HBase, a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications as well as administrators who will manage HBase clusters.
The instructor will walk participants through HBase architecture, data modeling and application development on HBase. Other subjects covered include using MapReduce with HBase and administration topics related to performance optimization. A significant portion of class time will be dedicated to interactive labs.

Apache Kafka is a publish-subscribe messaging rethought as a distributed commit log, capable of handling hundreds of megabytes of reads and writes per second from thousands of clients on each broker. A Kafka cluster can serve as the central data backbone for a large organization.

This course teaches machine learning at scale with the popular Apache Spark framework.
No machine learning knowledge is assumed. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, covering specific use cases. This is achieved through a combination of 50 percent lecture, 50 percent lab work.
Please note that this course does not include in-depth coverage of the maths/stats behind machine learning.
This course is taught using Spark and Python.

This course will familiarize participants with Apache Spark, explaining how Spark fits into the big data ecosystem and how to use Spark for data analysis. Topics covered will include Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, machine learning and GraphX.

This course introduces students to the Solr platform. Through a combination of lecture, discussion and labs, students will gain hands-on experience configuring effective search and indexing.
The class begins with basic Solr installation and configuration then teaches the attendees the search features of Solr. Students will gain experience with faceting, indexing and search relevance among other features central to the Solr platform. The course concludes with a number of advanced topics, including spell checking, suggestions, Multicore and SolrCloud.