Big Data Hadoop Certification Course

4 out of 5
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6 reviews

The Big Data Hadoop Certification Course is designed Big Data Industry experts to give you in-depth knowledge of the Big Data and Hadoop Ecosystem tools. You will learn HDFS, MapReduce, Hive, Pig, Spark, HBase, Oozie, Flume and Yarn with real-time industry use cases like in Healthcare, Social media, Banking, Finance and e-Commerce domains.

Why you should take Big Data Hadoop Certification Course?


  • The Big Data market to climb to $210 billion by 2020 (Arrow magazine)

  • Average Salary of Big Data Hadoop Developers is $140,000 (Indeed.com Data)

  • Big Data market revenues are projected to increase from $42 billion in 2018 to $103 billion in 2027 (CAGR)

Trainings for

Individual Classroom LearningCorporate Training Solutions
Instructor led Face 2 Face Practical Oriented TrainingFace 2 Face Interactive Practical Oriented training
State of the Art Training LabsLearn as per full day schedule with discussions and exercises.
Flexible ScheduleDoubt Clear sessions
Technical SupportCompletely Customizable course content and schedule based on your need
Use cases implementationsCertification Guidance Provided
Certification Guidance ProvidedCase studies and Use cases implementations

What next?

Since you gained Big Data Hadoop knowledge and it is time to take a next step to be acquainted with Big Data Hadoop Workshops. These workshops are Practical Oriented.

Workshop on Use case Implementation

– Social Media use case with Integration with Tableau
– Log processing use case with Tableau.

Workshop on Use case Implementation

FeaturesDevLabs AllianceOther Training Providers
Classroom SessionInteractive Classes room session with Extensive hands-onX - Instructor led, no hands-on
1-1 Training

Training ScheduleFlexibleFixed
Customized Course

Access to Recorded Videos

EMI Options

Support Post Session

Case Studies Discussion

1
Introduction to Big Data and Hadoop
  • What is Big Data?
  • Big Data in Use cases
  • Big data 3 Vs concepts
  • Big data diff data types— structured, unstructured and semi structured
  • RDBMS vs Hadoop
  • Hadoop Ecosystem
  • Various Hadoop Distributions
  • Hadoop Cluster(HDFS & MapReduce concepts)
2
HDFS: Hadoop Distributed File System:
  • HDFS Architecture
  • HDFS Writes
  • HDFS Reads
  • Rack awareness
  • Fault Tolerance
  • NameNode
  • Secondary NameNode
  • Interact with HDFS
  • HDFS Commands
  • HDFS Java API
  • HDFS REST API
  • Hands on Exercises
3
MapReduce Framework
  • MapReduce architecture
  • MapReduce Model
  • MapReduce Framework and Various phases
  • Input/output formats
  • Practitioner and combiner concepts with example
  • Map Reduce Hands On Programming with word count example run on Ubuntu
  • Distributed Cache
  • Map side join Vs Reduce Side Join
  • Managing and Scheduling Jobs,
  • types of schedulers in Hadoop,
  • Configuring the schedulers and run MapReduce jobs,
  • Hands on Exercises
4
Apache Pig
  • Pig philosophy and architecture
  • Pig installation on Ubuntu
  • Grunt shell
  • Loading data
  • Exploring Pig Latin commands
  • Pig Transformations functions
  • UDF function in java in Pig
  • Pig Loader & Storage
  • Joins in Pig
  • Hands on Exercises
5
Apache Hive
  • Hive architecture
  • Hive installation on Ubuntu
  • Hive vs RDBMS
  • HiveQL and the Hive shell
  • Data types and schemas
  • Creating tables (external vs managed)
  • Creating Partitions
  • Creating Views and Indexes
  • UDF function in java in Hive
  • Using hive create diff types of format
6
Apache HBase and NoSQL
  • Architecture and schema design
  • Hbase installation on Ununtu
  • HBase vs. Other NoSQL Options
  • HMaster and Region Servers
  • Column Families and Regions
  • Hbase installation and java api to access to HBase
  • MongoDB & Cassandra Introduction
7
Apache Sqoop
  • What is Sqoop?
  • Sqoop Usage
  • Sqoop Installation
  • Sqoop Commands
8
Apache Flume
  • Flume Concepts.
  • Installing & Configuring on Cluster.
  • Writing a Sample program using Flume Concept.
9
Oozie
  • Oozie Concepts.
  • Installing & Configuring on Cluster.
  • Writing a Sample program using Oozie Concept.
10
Apache Spark
  • Spark Introduction.
  • Installing & Configuring on Cluster.
  • Hands example on various modules e.g. Apache Spark Core, Apache Spark SQL, Apache Spark Streaming
11
New and Upcoming Features and Tools in Hadoop
  • HDFS Federation
  • Hadoop HA
  • Advanced Hadoop MapReduce (MapReduce 2) or YARN
Bangalore, Delhi, Gurgaon, Noida, Pune, London, Singapore
Your certificate has a lifetime validity.
You can email us at training@devlabsalliance.com for all your queries. We will try and respond with the solution at the earliest.
Once you complete the training program and score minimum 75{1936c8d805a432f77fd3cd83527035ce42cacdca10118399504390db87bb5ed5} marks in the qualifying exam, you will be awarded the DevOps Certified Professional Certificate.
Please inform us at the earliest. For real urgent and unforeseen situations, we will try and accommodate you in any of the upcoming training batches.
You will be eligible for ninety percent refund until one week before commencement of the course.
We will try to arrange some extra time so that you can align yourself with the ongoing class.
All our practical sessions will be performed on the AWS Cloud. We will help you set up your AWS Free Tier account once you enroll for the course. We will together create VMs, and configure it with the required setup for the hands on experience.
You will be provided desktops with the required hardware configuration, at the venue.
1 Jun 19 – 30 Jun 19Sat – Sun (5 weekends )6 PM – 10 PM IST 19,999Batch Closed
19 Jan 19 – 17 Feb 19Sat – Sun (5 weekends )6 PM – 10 PM IST 19,999Batch Closed
1 Dec 18 – 30 Dec 18Sat – Sun (5 weekends )6 PM – 10 PM IST19,999Batch Closed
1 Sep 18 – 30 Sep 18Sat – Sun (5 weekends )6 PM – 10 PM IST19,999Batch Closed
2 Jun 18 – 1 Jul 18Sat – Sun (5 weekends )6 PM – 10 PM IST19,999Batch Closed
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Enrolled: 210 students
Duration: 40 hours
Modules: 11
Level: Advanced

Working hours

Monday9:30 am - 6.00 pm
Tuesday9:30 am - 6.00 pm
Wednesday9:30 am - 6.00 pm
Thursday9:30 am - 6.00 pm
Friday9:30 am - 6.00 pm
SaturdayClosed
SundayClosed

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