Cisco Big Data Analytics

Level
Beginner
Duration
40 hours
Course Fee
₹18000

This 15days (Weekdays – 3hrs.) OR 10-weeks (Sunday/Saturday) Cisco Big Data analytics course is an instructor-led, lab-based, hands-on course. This course introduces learners to choose and design scalable, reliable, and intelligent data center solutions using Cisco UCS Integrated Infrastructure for Big Data and Analytics.

Training Type
Classroom Online Corporate
Batch Timings

For the latest training schedule, please check the Schedules.

Weekdays
  • Early Morning
  • Morning
  • Afternoon
  • Evening
  • Fastrack
Weekdays
  • Morning
  • Afternoon
  • Evening
  • Sat / Sun
  • Sunday Only

Training is available in small groups as well as on one-to-one basis. Get in touch.

Cisco Big Data Analytics

Level
Beginner
Duration
40 hrs.
Course Fee
₹18000

This 15days (Weekdays – 3hrs.) OR 10-weeks (Sunday/Saturday) Cisco Big Data analytics course is an instructor-led, lab-based, hands-on course. This course introduces learners to choose and design scalable, reliable, and intelligent data center solutions using Cisco UCS Integrated Infrastructure for Big Data and Analytics.

Training Type
Classroom Online Corporate
Batch Timings

For the latest training schedule, please check the Schedules.

Weekdays
  • Early Morning
  • Morning
  • Afternoon
  • Evening
  • Fastrack
Weekdays
  • Morning
  • Afternoon
  • Evening
  • Sat / Sun
  • Sunday Only

Training is available in small groups as well as on one-to-one basis. Get in touch.

Course Introduction

This course provides hands on training with a technical mix of application, compute, storage and networking topics concerning the deployment of Big Data clusters. The goal of this training is to provide candidates with a better understanding of Big Data infrastructure requirements, considerations and architecture and application behavior, to be better equipped for Big Data infrastructure discussions and design exercises.

Course Highlights

The key to a high success rate is based on the program’s objectives as follows:

  • Course contents are based on CISCO Big Data Analytics
  • Dedicated Monitoring to evaluate and report candidates progress
  • Extensive hands-on lab exercises
  • Regular evaluation.
  • Industry acclaimed, experienced and certified instructors
  • Curriculum is based on CISCO BID DATA ANALYTICS.
  • The Instructor-led certified courses is designed for the
  • CISCO BID DATA ANALYTICS candidates with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises
  • Courseware includes course kits and other reference material to enable students to prepare for course.
  • Optimal balance of theory classes and practical labs every week to ensure maximum absorption of technology by participants
  • Customized tests at the end of course to be attempted by every participant
  • Stringent passing standards with progress report of each participant
  • Facility of Lab on cloud available (based on booking)
  • Repeating or lectures allowed (based on seat availability)
Course Objectives

After completing this course, you should be able to:

  • Describe a high-level overview of big data fundamentals
  • Describe big data storage, compute, and data networking architecture
  • Introduce Hadoop core components
  • Gain working knowledge of Streaming Analytics usage
  • Describe the design and sizing of the compute, network, and storage component of Cisco UCS Integrated
  • Infrastructure for Big Data
  • Identify the Cisco UCS Integrated Infrastructure for Big
  • Data, understand how Cisco Advanced Services for Big
  • Data supports the Cisco Validated Designs that fit into Cisco CPA for Big Data
  • Explain Cisco (UCS) Director Express for Big Data product features, benefits, installation, implementation, and management
Course Topics
  • Describe a high-level overview of big data fundamentals
  • Describe big data storage, compute, and data networking architecture
  • Introduce Hadoop core components
  • Gain working knowledge of Streaming Analytics usage
  • Describe the design and sizing of the compute, network, and storage component of Cisco UCS Integrated
  • Infrastructure for Big Data
  • Identify the Cisco UCS Integrated Infrastructure for Big
  • Data, understand how Cisco Advanced Services for Big
  • Data supports the Cisco Validated Designs that fit into
  • Cisco CPA for Big Data
  • Explain Cisco (UCS) Director Express for Big Data product features, benefits, installation, implementation, and management
Lab Topics

Not Available


Virtual Classroom
  • Instructor led online training is an ideal vehicle for delivering training to individuals anywhere in the world at any time.
  • This innovative approach presents live content with instructor delivering the training online.
  • Candidates will be performing labs remotely on our labs on cloud in presence of an online instructor.
  • Rstforum uses microsoft lync engine to deliver instructor led online training.
  • Advances in computer network technology, improvements in bandwidth, interactions, chat and conferencing, and realtime audio and video offers unparalleled training opportunities.
  • Instructor led online training can helps today’s busy professionals to perform their jobs and upgrade knowledge by integrating self-paced instructor led online training in their daily routines.
Miscellaneous
  • Minimum batch size required for batch is 10 participants in the this course.
  • The RST Forum reserves the right to cancel/postpone the class.
  • Course schedule will be provided before commencement of the course.
  • Certificate of participation will be awarded to participants with a minimum 90% attendance.
  • All attendees are to observe the Copyright Law on intellectual properties such as software and courseware from respective vendors.
  • The RST Forum reserves the right to include external participants in the program either for the entire course or individual courses.
  • The RST Forum reserves the right to change/alter the sequence of courses. RST FORUM published Book would be given at 50% discounted rate to the forum students.