Big Data: Overview, Tools and Use Cases


Big Data has emerged as a powerful new technology paradigm. To manage the massive data generated by social media, online transactions, Web logs, or sensors, Big Data incorporates innovative technologies in data management (unstructured, semi-structured and structured), processing, real time analytics, and visualization. It is also useful for reporting in circumstances where a relational database approach is not effective or too costly. This course is designed for managers, analysts, architects and developers seeking an understanding of Big Data concepts, the related technology landscape and deployment patterns.

The course starts with the evolution, characteristics and significance of Big Data. You will learn data management (acquiring, cleansing and normalizing Big Data) and discuss use cases related to log analytics, fraud detection, social media patterns, call centers and more applications in various industries. The course will introduce the concepts and methodology of NoSQL, a database management system designed to handle Big Data. You will also learn the technology infrastructure, Hadoop, storage, MapReduce and Query (SMAQ) stack, and basic Map/Reduce functionality used in Big Data. The course concludes with a review of Data Visualization Tools (DVT), analytical tools and the deployment patterns used in various industries.


The course offers an overview of the Big Data landscape, tool infrastructure and industrial applications. There will be a final project for students to work in teams and study Big Data solutions for specific industries. Students will primarily be exposed to overview of the tools. Tool usage, programming, algorithms and application development are covered in related courses.


Topics Include:



  • Big Data concepts and characteristics

  • Data management in the warehouse and in Big Data

  • Big Data industrial use cases

  • Hadoop primer

  • NoSQL functions and ways of managing data

  • NoSQL methodology and limitations

  • Tool chain, Hadoop and SMAQ (Storage, MapReduce and Query) stacks in Big Data

  • Data discovery and visualization

  • Advanced analytics

  • Team projects




Skills Needed: A fundamental understanding of databases, programming and data analytics is strongly recommended.


Prerequisites:


No prerequisites


Sections :


Section Start Date Time Location Cost Instructor Name Full Schedule Enroll
DBDA.X401.(4) 6/26/2018 06:30 PM SANTA CLARA 910 Alakh K Verma View Enroll
DBDA.X401.(5) 9/18/2018 06:30 PM SANTA CLARA 910 Alakh K Verma View Enroll