Big Data Hadoop Developer Certification Training

Big Data Hadoop Developer Certification Training

Hadoop developer, Architect, Tester, Administrator, Analyst or a Data Scientist. Learn the basics, Hadoop applications, HBase data model and architecture, schema design, MapReduce and Apache Drill. This course covers the concepts of MapReduce, Yarn, Pig, Hive, HBase, Oozie, Flume and Sqoop. Our trainers give hands on experience with real-time scenario - based projects in the United States.
Start Date Duration Time (CST) Type Mode of Training Enroll
17-Mar-2019 55 Hrs 09:00 PM Online INSTRUCTOR LED TRAINING Enquiry Now


Hadoop is a revolutionary open-source framework for software programming that took the data storage and processing to next level. With its tremendous capability to store and process huge clusters of data, it unveiled opportunities to business around the world with artificial intelligence. It stores data and runs applications on clusters in commodity hardware which massively reduces the cost of installation and maintenance. It provides huge storage for any kind of data, enormous processing power and to have all kinds of analytics such as real-time analytics, predictive analytics data and so on at a click of a mouse. The volume of data being handled by organizations keeps growing exponentially with each passing day! This ever-demanding scenario calls for powerful big data handling solutions such as Hadoop for a truly data-driven decision-making approach. Students who start as Hadoop developers evolve into Hadoop Administrators by the end of a certification course and in the process guarantee a bright future. Become a certified Hadoop professional to bag a dream job offer. Acquiring proper training on Hadoop technology would definitely be a boon to professionals in terms of using Hadoop resources effectively and save huge time and effort.

Did you know?

It is noticeable that the world is revolutionized by data and Information Technology. More than ‘2.5 quintillion bytes of data is created daily across the globe. Surprisingly, the data developed in last 2 years accounts for 90% of the entire data in the world! every day this rate of data creation is increasing rapidly. Big Data professionals play a vital role in this tremendous evolution as they are responsible to handle such huge volumes of data!

Why learn and get Certified in Big Data and Hadoop?

  1. Leading multinational companies are hiring for Hadoop technology – Big Data & Hadoop market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 (Forbes).
  2.  Streaming Job Opportunities – McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts (Mckinsey Report).
  3.  Hadoop skills will boost salary packages – Average annual salary of Big Data Hadoop Developers is around $135k ( Salary Data).
  4.  Future of Big Data and Hadoop looks bright – The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data is generated. “Technology professionals should be volunteering for Big Data projects, which make them more valuable to their current employer and more marketable to other employers” – (

Course Objective

After the completion of this course, Trainee will:
  1. Expertise in writing customize Java MapReduce jobs to summarize data and helps in solving common data manipulation problems
  2. Knowledge in Debugging and implementation of workflows and common algorithms are the best practices Hadoop development
  3. Capability to assist an individual to create custom components such as input formats and writable comparables to manage difficult data types
  4. Ability to understand comprehend Advanced Hadoop API topics
  5. Expertise in Hadoop ecosystem projects like leveraging Hive, Oozie, Pig, Flume, Sqoop etc.


There are no prerequisites as such for learning Hadoop. Knowledge of Core Java and SQL skills will help, but certainly not a requirement. If you want to learn Core Java, ZaranTech offers the students a complimentary self-paced course in “Core Java” when you enroll in our Big Data Hadoop Certification course.

Who should attend this Training?

  1.  Architects, Java developers and testers who want to build effective data processing applications by querying Apache Hadoop, also who insists to learn to write code
  2. Technical managers involved in the development process also take active participation in Hadoop Developer classes
  3. Business Analysts, Database Administrators and SQL Developers
  4. Software engineers with a background in ETL/Programming and managers dealing with latest technologies and data management
  5. .NET Developers and data analysts who have to develop applications and perform big data analysis using the Hortonworks Data Platform for Windows will also find this helpful

Prepare for Certification

Our training and certification program gives you a solid understanding of the key topics covered on the certification exams. In addition to boosting your income potential, becoming certified Professional will provide you to display your ability and expertise in the relevant domain. Upon completion of course, students are encouraged to proceed to study and register for the Cloudera Certified Developer for Apache Hadoop (CCDH) Exam. Once the students successfully clears the online exam, they are eligible for CCA & CCP Certifications by Cloudera. The Developer program includes two certification tiers – Cloudera Certified Associate (CCA175) and Cloudera Certified Professional (CCP-DE575).


Unit 1: Introduction and Overview of Hadoop

  1. What is Hadoop?
  2. History of Hadoop.
  3. Building Blocks - Hadoop Eco-System.
  4. Who is behind Hadoop?
  5. What Hadoop is good for and what it is not?

Unit 2: Hadoop Distributed FileSystem (HDFS)

  1. HDFS Overview and Architecture
  2. PREVIEWHDFS Installation
  3. HDFS Use Cases
  4. Hadoop File System Shell
  5. File System Java API
  6. Hadoop Configuration

Unit 3: HBase – The Hadoop Database

  1. HBase Overview and Architecture
  2. HBase Installation
  3. HBase Shell
  4. Java Client API
  5. Java Administrative API
  6. Filters
  7. Scan Caching and Batching
  8. Key Design
  9. Table Design

Unit 4: Map/Reduce 2.0/YARN

  1. Decomposing Problems into MapReduce Workflow
  2. Using JobControl
  3. Oozie Introduction and Architecture
  4. Oozie Installation
  5. Developing, deploying, and Executing Oozie Workflows

Unit 5: Pig

  1. Pig Overview
  2. Installation
  3. Pig Latin
  4. Developing Pig Scripts
  5. Processing Big Data with Pig
  6. Joining data-sets with Pig

Unit 6: Hive

  1. Hive
  2. OverviewInstallation
  3. Hive QL

Unit 7: Sqoop

  1. Introduction
  2. Sqoop Tools
  3. Sqoop Import
  4. Sqoop Import all tables
  5. Sqoop Export
  6. Sqoop Job
  7. Sqoop metastore
  8. Sqoop Eval
  9. Sqoop Codegen
  10. Sqoop List Databases and List Tables
  11. Sqoop Create Hive Table

Unit 1: Integrating Hadoop Into The Workflow

  1. Relational Database Management Systems
  2. Storage Systems
  3. Importing Data from RDBMSs With Sqoop
  4. Hands-on exercise
  5. Importing Real-Time Data with Flume
  6. Accessing HDFS Using FuseDFS and Hoop

Unit 2: Delving Deeper Into The Hadoop API

  1. More about ToolRunner
  2. Testing with MRUnit
  3. Reducing Intermediate Data With Combiners
  4. The configure and close methods for Map/Reduce Setup and Teardown
  5. Writing Partitioners for Better Load Balancing
  6. Hands-On Exercise
  7. Directly Accessing HDFS
  8. Using the Distributed Cache

Unit 3: Common Map Reduce Algorithms

  1. Sorting and Searching
  2. Indexing
  3. Machine Learning With Mahout
  4. Term Frequency – Inverse Document Frequency
  5. Word Co-Occurrence

Unit 4: Using Hive and Pig

  1. Hive Basics
  2. Pig Basics

Unit 5: Practical Development Tips and Techniques

  1. Debugging MapReduce Code
  2. Using LocalJobRunner Mode For Easier Debugging
  3. Retrieving Job Information with Counters
  4. Logging
  5. Splittable File Formats
  6. Determining the Optimal Number of Reducers
  7. Map-Only MapReduce Jobs

Unit 6: More Advanced Map Reduce Programming

  1. Custom Writables and WritableComparables
  2. Saving Binary Data using SequenceFiles and Avro Files
  3. Creating InputFormats and OutputFormats

Unit 7: Joining Data Sets in Map Reduce

  1. Map-Side Joins
  2. The Secondary Sort
  3. Reduce-Side Joins

Unit 8: Graph Manipulation in Hadoop

  1. Introduction to graph techniques
  2. Representing graphs in Hadoop
  3. Implementing a sample algorithm: Single Source Shortest Path

Unit 9: Creating Workflows With Oozie

  1. The Motivation for Oozie
  2. Oozie’s Workflow Definition Format

About Hadoop Developer Certification

Professional certifications will help you to showcase your proficiency and expertise in the particular domain. Upon completion of course, participants are encouraged to register for the Cloudera Certified Developer for Apache Hadoop (CCDH) Exam. Once the candidate clears the online exam, he is eligible for CCA & CCP Certifications by Cloudera.

Hadoop Developer Certification Types

  1. Cloudera Certified Associate (CCA175)
  2. Cloudera Certified Professional (CCP-DE575)

CCA- Cloudera Certified Associate (CCA175) Certification

Cloudera Certified Associate (CCA175) validates foundational skills and sets forth the groundwork to achieve expertise under CCP program


For Cloudera certification exam, no prerequisites are required. CCA175 follows the same features as Cloudera developer training for Spark and Hadoop.

Exam Details

  1. Registration fee is $295
  2. Exam duration is 120 minutes
  3. There are 10-12 performance-based tasks on CHD5 cluster
  4. 70% is the passing score

CCP- Cloudera Certified Professional (CCP Data Engineer - DE575) Certification

Cloudera Certified Professional (CCP) identifies and validates candidate’s expertise in technical skills.


Candidates for CCP Data Engineer (DE575) should have in detail expertise and experience in developing data engineering solutions. 1. Registration fee is $600
  1. Exam duration is 4 hours
  2. Exam Question format – Eight customer problems with each with a unique, large data set, a 7-node high performance CDH5 cluster
+What are the benefits of Hadoop learning?
Hadoop Training FAQs Hadoop is changing the perception of handling big data especially unstructured data. It plays a vital role in handling and managing big data by enabling streamlined surplus data for any distributed processing system across clusters of computers using simple programming models.
+benefit you?
How does a Hadoop Training course could The following are the benefits of taking Hadoop training namely:  
  1. Gaining Expertise – You definitely need a professional training course to earn adequate knowledge to become Hadoop professional and to fluently work with Hadoop systems.
  2. Choice – You can enjoy the convenience of choosing the best trainer suiting your needs and requirements.
  3. Flexibility – Given that every Hadoop training course is accompanied with facilities to customize in accordance with the requests from the trainee in terms of syllabus, course duration and training modes.
  4. Certification – At the end of the Hadoop training course, you will get a course completion certificate for successfully completing the course. You can showcase this training certification in your resume to convince the recruiters on your attendance to Hadoop professional training. In addition to that, you can also avail excellent assistance to attend the expert-level certifications from providers like Cloudera, Hortonworks and MapR, etc.
  5. Employment Opportunities – Being a professional training course provider, the institute you’re enrolling here is probably facilitated with dedicated placement assistance team and tie-ups with top companies’ recruitment department to help you get employed as a Hadoop professional.
+does it need Hadoop?
If an organization doesn't do big data, Hadoop is a distributed data processing platform not just for big data. The following are the highlights of Hadoop that any organizations should make use of:  
  1. Easier Data Processing, Management, and Analysis – with the help of Hadoop, the data stored in the warehouse can be structured, processed and transformed to enable easier data management, processing and analysis.
  2. Data Archiving – Business data can be archived for years with the help of Hadoop. The data can be stored in multiple versions such as raw, native and modified data. Thanks to the inexpensive commodity hardware used by Hadoop.
  3. Easier Access to any data – Once the data is stored using Hadoop system, the access to such data stored is made much easier and effective.
+extra benefit while learning Hadoop?
I have knowledge of Linux. Do I get any

Hadoop was initially built on Linux and is the preferred method for both installing and managing Hadoop. It can also run on Windows. The solid understanding of Linux shell will also help in understanding Hadoop easily as most of the Hadoop command line parameters are in Linux.