CCDH | Firebrand Training

Overview

Get the skills you need to write, maintain, and optimise Apache Hadoop on this accelerated Cloudera Certified Developer for Apache Hadoop (CCDH) course. Learn how create robust data processing applications using Apache Hadoop.

This course is 33% faster than traditional training. You’ll be prepared for the real world challenges faced by Hadoop developers and study the following topics:

The internals of MapReduce, Hadoop Distributed File System (HDFS) and how to write MapReduce code
Best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
How to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
Creating custom components such as WritableComparables and InputFormats to manage complex data types Writing and executing joins to link data sets in MapReduce
Advanced Hadoop API topics required for real-world data analysis

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Four reasons why you should sit your CCDH course with Firebrand Training

You'll be CCDH trained and certified faster. Learn more on this 3-day accelerated course. You'll get at least 12 hours a day of quality learning time in a distraction-free environment
Your CCDH course is all-inclusive. One simple price covers all course materials, exams, accommodation and meals – so you can focus on learning
Pass CCDH first time or train again for free. Your expert instructor will deliver our unique accelerated learning methods, allowing you to learn faster and be in the best possible position to pass first time. In the unlikely event that you don't, it's covered by your Certification Guarantee
Study CCDH with an award-winning training provider. We've won the Learning and Performance Institute's "Training Company of the Year" three times. Firebrand is your fastest way to learn, with 134.561 students saving more than one million hours since 2001

Benefits

Think you are ready for the course? Take a FREE practice test to assess your knowledge!

Free Practice Test

Curriculum

The Motivation for Hadoop

Problems with traditional large-scale systems
Introducing Hadoop
Hadoopable problems

Hadoop: Basic Concepts and HDFS

The Hadoop project and Hadoop components
The Hadoop Distributed File System

Introduction to MapReduce

MapReduce overview
Example: WordCount
Mappers
Reducers

Hadoop Clusters and the Hadoop Ecosystem

Hadoop cluster overview
Hadoop jobs and tasks
Other Hadoop ecosystem components

Writing a MapReduce Program in Java

Basic MapReduce API Concepts
Writing MapReduce Drivers, Mappers, and Reducers in Java
Speeding up Hadoop development by using eclipse
Differences between the old and new MapReduce APIs

Writing a MapReduce Program Using Streaming

Writing Mappers and Reducers with the streaming API

Unit Testing MapReduce Programs

Unit testing
The JUnit and MRUnit testing frameworks
Writing unit tests with MRUnit
Running unit tests

Delving Deeper into the Hadoop API

Using the ToolRunner class
Setting up and tearing down Mappers and Reducers
Decreasing the amount of intermediate data with combiners
Accessing HDFS programmatically
Using the distributed cache
Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners

Practical Development Tips and Techniques

Strategies for debugging MapReduce code
Testing MapReduce code locally by using LocalJobRunner
Writing and viewing log files
Retrieving job information with counters
Reusing objects
Creating map-only MapReduce jobs

Partitioners and Reducers

How partitioners and Reducers work together
Determining the optimal number of Reducers for a job
Writing customer partitioners

Data Input and Output

Creating custom writable and WritableComparable implementations
Saving binary data using sequenceFile and Avro data files
Issues to consider when using file compression
Implementing custom InputFormats and OutputFormats

Common MapReduce Algorithms

Sorting and searching large data sets
Indexing data
Computing term frequency — Inverse Document Frequency
Calculating word co-occurrence
Performing Secondary Sort

Joining Data Sets in MapReduce Jobs

Writing a Map-Side Join
Writing a Reduce-Side Join

Integrating Hadoop into the Enterprise Workflow

Integrating Hadoop into an existing enterprise
Loading data from an RDBMS into HDFS by using Sqoop
Managing real-time data using Flume
Accessing HDFS from legacy systems with FuseDFS and HttpFS

An Introduction to Hive, Imapala, and Pig

The motivation for Hive, Impala, and Pig
Hive overview
Impala overview
Pig overview
Choosing Between Hive, Impala, and Pig

An Introduction to Oozie

Introduction to Oozie
Creating Oozie workflows

Exam Track

As part of this accelerated course, you'll receive the following exam voucher:

Cloudera Certified Developer for Apache Hadoop (CCD-410)

The exam consists of 55 questions and must be completed within 90 minutes. You must have a passing score of at least 70% to get your certification.

This course will cover content and practical tests to cover preparation to the exam. Firebrand cannot deliver the exam at our centre. Delegates will be provided with an exam voucher to take the exam.

What's included

Included:

Official Cloudera courseware

Your accelerated course includes:

Accommodation *
Meals, unlimited snacks, beverages, tea and coffee *
On-site exams **
Exam vouchers **
Practice tests **
Certification Guarantee ***
Courseware
Up-to 12 hours of instructor-led training each day
24-hour lab access
Digital courseware **

* For residential training only. Accommodation is included from the night before the course starts. This doesn't apply for online courses.
** Some exceptions apply. Please refer to the Exam Track or speak with our experts
*** Pass first time or train again free as many times as it takes, unlimited for 1 year. Just pay for accommodation, exams, and incidental costs.

Prerequisites

This course is best suited to developers and engineers with programming experience. Knowledge of Java is strongly recommended and is required to complete the hands-on exercises. Prior knowledge of Apache Hadoop is not required.

Are you ready to get certified in record time?

We interview all applicants for the course on their technical background, degrees and certifications held, and general suitability. If you get through this screening process, it means you stand a great chance of passing.

Firebrand Training is an immersive training environment. You must be committed to the course. The above prerequisites are guidelines, but many students with less experience have other background or traits that have enabled their success in accelerated training through Firebrand Training.

If you have any doubts as to whether you meet the pre-requisites please call 78 79 16 53 and speak to one of our enrolment consultants, who can help you with a training plan.

Reviews

We've currently trained 134.561 students in 12 years. We asked them all to review our Accelerated Learning. Currently,
96,46% have said Firebrand exceeded their expectations:

"Course and material very good for remote learning, Plenty of labs and info to get you through the course. Instructor was very thorough and assisted with any issues."
James Bridges. (11/9/2023 (Monday) to 14/9/2023 (Thursday))

"The training with Firebrand is always a good experience. The hotel was very nice and accommodating and the teacher was very informative on the course"
Anonymous. (11/9/2023 (Monday) to 14/9/2023 (Thursday))

"Thank you for getting me through."
Evelyne Spanner, ERBD. (11/9/2023 (Monday) to 12/9/2023 (Tuesday))

"Our instructor was friendly, helpful, and demonstrated good knowledge of the subjects"
OA. (11/9/2023 (Monday) to 14/9/2023 (Thursday))

"Training was precise and swift!"
Anonymous, NHS Tayside. (11/9/2023 (Monday) to 14/9/2023 (Thursday))

Course Dates

Start	Finish	Status	Location
21/8/2023 (Monday)	23/8/2023 (Wednesday)	Finished - Leave feedback	-

5/2/2024 (Monday)	7/2/2024 (Wednesday)	Limited availability	Nationwide
18/3/2024 (Monday)	20/3/2024 (Wednesday)	Open	Nationwide
29/4/2024 (Monday)	1/5/2024 (Wednesday)	Open	Nationwide
10/6/2024 (Monday)	12/6/2024 (Wednesday)	Open	Nationwide

Cloudera - Certified Developer for Apache Hadoop (CCDH)