Unlock the Values from Massive Datasets.
Learn Fundamentals to Advanced Levels of Big-Data Methods BIG-DATA
Training by Certified Industry Experts.
BIG-DATA Training Online
Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages between similar organizations and result in business benefits, such as more effective marketing and increased revenue.
New methods of working with big data, such as Hadoop and Map Reduce, offer alternatives to traditional data warehousing. Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and R-Hadoop.
A powerful data analytics engine can be built and process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics.
Upskill and get certified in Big Data with our one-of-a-kind big data training course, crafted to help you become adept at Big Data Technology.
Get noticed by top companies through our professional job assistance.
35 hours of in-depth training programs with realtime scenarions.
Get trained by the highly experienced certified industry experts.
Upgraded sylabus with Industry oriented concepts for in-depth knowledge.
15+ In- demand tools & skills will help in Technical Assistance.
Every session will be live and provides you hands-on experience training.
35 contact hours
Industry case studies
Real time training
Core computer science concepts from leading industry experts.
Build an end-to-end application and test it with exciting features.
Earn an industry-recognized course completion certificate.
BIG-DATA Certification Course Contents
BIG DATA DEVELOPER WITH SPARK IN AWS ENVIRONMENT
- What is File System?
- Block, Linux commands
- How is the system working?
- File system, OS, CPU, Applications
- Shell Script basics
- How to automate Sqoop
- hive using a shell script.
Scala for Spark
INTRODUCTION TO BIG DATA-HADOOP
HBASE, PHOENIX, KAFKA, CASS
Big Data Training FAQ’S
How can I believe that Big Data has a good career scope?
How do you rate Big Data skills for a great career?
As of the latest survey, there are nearly 190,000 data scientists and 1.5 million managers are in demand in the United States. If you are interested or passionate about Big Data, it is highly recommended to take up Big Data training, which will add value to your profile.
What are the different Job roles in Big Data analytics?
Based on the analytical and practical skills of the candidates, we have five different types of job roles in Big Data analytics as follows,
- Solutions expert
- Analytics salesperson
- Expert programmer or Tools expert
- Expert Modeler
What are the various ways to learn Big Data in the most efficient and affordable way?
Two simple ways to learn Big Data are
- In-class training and
- Online training
The reason to suggest these training modes is, both the above training methods will give a comprehensive understanding and knowledge of the Big Data topic you are interested in. Yet another important thing to keep in mind is the training institute or mentors you choose to learn Big Data. You have a list of established Big Data training institutes in the United States here. Depending upon your convenience, you could either choose, In-class training or online training.
Are there any prerequisites to gain knowledge in Big Data?
Nothing makes us happier than satisfied clients. Let us share some successful client stories with you.
This is an excellent practical course, giving the opportunity to get acquainted with both theoretical and practical work in Business Analysis.
The best training professionals I found for automation testing training, faculties are highly experienced, explained every concept in-depth with real-time project scenarios.
Happy to start off my selenium career here, this is by the far the best institute I have ever got for Selenium. Very eloquent tutorial. Outstanding Training with great examples.