This training provides a brief technical introduction of Apache Hadoop. It also cover HLL information about operation, architecture, concepts and utilization of HDP and Hadoop ecosystem. The course also helps to gain advanced knowledge for those professionals who are planning to attend hands-on training and Instructor Led Training.
Prerequisites for this training
The recommended prerequisite for this course is basic knowledge of Hadoop or programming learning.
Who should attend this course?
This training is intended for data integration architects, C-level executives, technical infrastructure team and Data architects. However, the target audience for this training are decision makers, Hadoop administrators who want to understand the fundamentals of Hadoop and Big Data.
What you will learn
Elaborating about HDFS architecture.
Describing the utilization cases for Hadoop.
Describing about the data ingestion selections and systems for batch along with real-time streaming.
Explaining the basic fundamentals of parallel processing.
Overseeing most known data transformation along with processing engines in action.
Detailing the features and architectures of YARN.
Describing how to secure the Hadoop.
With Microtek Learning, you’ll receive:
Certified Instructor-led training
Industry Best Trainers
Official Training Course Student Handbook
Pre and Post assessments/evaluations
Collaboration with classmates (not available for a self-paced course)
Real-world knowledge activities and scenarios
Exam scheduling support*
Learn and earn program*
Knowledge acquisition and exam-oriented
Interactive online course.
Support from an approved ITIL expert
For Government and Private pricing*
*For more details call: +1-800-961-0337 or email: firstname.lastname@example.org
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- 100,000+ professionals trained
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Many Delivery Methods
Flexible delivery methods are available here.
Resources are included for a comprehensive learning experience.
The Case for Hadoop
The Hadoop Ecosystem
Apache Hive Overview
Apache Pig Overview
Apache Spark Overview
Operational Overview with Ambari
Loading Data into HDFS
Streaming Data into HDFS
Processing with MapReduce
Data Manipulation with Hive
Risk Analysis with Pig
Risk Analysis with Spark
Securing Ranger with Hive
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