Cloudera - Certified Developer for Apache Hadoop (CCDH)

Duration

Duration:

Just 3 Days

Method

Method:

Classroom / Online / Hybrid

Next date

Next date:

28/3/2022 (Monday)

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

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

  1. 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
  2. Your CCDH course is all-inclusive. One simple price covers all course materials, exams, accommodation and meals – so you can focus on learning
  3. 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
  4. 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 114.207 students saving more than one million hours since 2001

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. 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 (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 (0)8 52 50 01 66 and speak to one of our enrolment consultants, who can help you with a training plan.

Reviews

We've currently trained 114.207 students in 12 years. We asked them all to review our Accelerated Learning. Currently,
96,64% have said Firebrand exceeded their expectations:

"The course has a ton of information which has delivered succinctly."
Anonymous, AVL. (5/1/2022 (Wednesday) to 7/1/2022 (Friday))

"I have attended a number of courses now with firebrand, in class and remote. Always very well presented and knowledgeable instructors."
Caig Iveson-Dance, Kier. (6/1/2022 (Thursday) to 7/1/2022 (Friday))

"Good teacher with knowledge of the highest standard."
Fraser Anderson, N/A. (20/12/2021 (Monday) to 22/12/2021 (Wednesday))

"Firebrand provide a distraction free, one-stop shop to computing coursework and passing subsequent exams."
Carl Shadrake. (20/12/2021 (Monday) to 22/12/2021 (Wednesday))

"The training was very well structured and provided all the tools and knowledge required to become qualified. The accommodation was also fantastic."
MAS, HSBC. (20/12/2021 (Monday) to 22/12/2021 (Wednesday))

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