Hadoop Developer with Spark (CCA175) Certification Training Course

Hadoop Developer with Spark Certification Training Course Overview

Hadoop Developer with Spark certification will let students create robust data processing applications using Apache Hadoop. After completing this course, students will be able to comprehend workflow execution and working with APIs by executing joins and writing MapReduce code. This course will offer the most excellent practice environment for the real-world issues faced by Hadoop developers. With Big Data being the buzzword, Hadoop certification and skills are being sought by companies across the globe. Big Data Analytics is a priority for many large organizations, and it helps them improve performance. Therefore, professionals with Big Data Hadoop expertise are required by the industry at large.

Hadoop Developer with Spark are among the world's most in-demand and highly compensated technical roles. According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018.

Who should do this course?

This Hadoop training is best suited for

  • Developers
  • Engineers
  • Security Officers
  • Any professional who has programming experience with basic familiarity of SQL and Linux commands.

Course Objectives

  • The Hadoop certification will help you learn how to distribute, store, and process data in a Hadoop cluster
  • After completing this course, you can easily write, configure, and deploy Apache Spark applications on a Hadoop cluster
  • Learn how to use the Spark shell for interactive data analysis
  • Use Spark Streaming to process a live data stream
  • Find out ways to process and query structured data using Spark SQL
  • This Hadoop course will help you use Flume and Kafka to ingest data for Spark Streaming.
This course prepares you for Exam CCA175. Test your current knowledge Qubits42

Hadoop Developer with Spark (CCA175) Certification Training Course (Duration : 32 Hours) Download Course Contents

Live Virtual Classroom
Group Training 2300
02 - 05 Aug 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 09 Sep 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR) 2650
4 Hours
8 Hours
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Week End

Start Time : At any time

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12 PM

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Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
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Course Modules

Module 1: Introduction to Apache Hadoop and the Hadoop Ecosystem
  • Apache Hadoop Overview
  • Data Ingestion and Storage
  • Data Processing
  • Data Analysis and Exploration
  • Other Ecosystem Tools
  • Introduction to the Hands-On Exercises
Module 2: Apache Hadoop File Storage
  • Apache Hadoop Cluster Components
  • HDFS Architecture
  • Using HDFS
Module 4: Apache Spark Basics
  • What is Apache Spark?
  • Starting the Spark Shell
  • Using the Spark Shell
  • Getting Started with Datasets and DataFrames
  • DataFrame Operations
Module 5: Working with DataFrames and Schemas
  • Creating DataFrames from Data Sources
  • Saving DataFrames to Data Sources
  • DataFrame Schemas
  • Eager and Lazy Execution
Module 6: Analyzing Data with DataFrame Queries
  • Querying DataFrames Using Column Expressions
  • Grouping and Aggregation Queries
  • Joining DataFrames
Module 7: RDD Overview
  • RDD Overview
  • RDD Data Sources
  • Creating and Saving RDDs
  • RDD Operations
Module 8: Transforming Data with RDDs
  • Writing and Passing Transformation Functions
  • Transformation Execution
  • Converting Between RDDs and DataFrames
Module 9: Aggregating Data with Pair RDDs
  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations
Module 10: Querying Tables and Views with Apache Spark SQL
  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API
  • Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
Module 11: Working with Datasets in Scala
  • Datasets and DataFrames
  • Creating Datasets
  • Loading and Saving Datasets
  • Dataset Operations
Module 12: Writing, Configuring, and Running Apache Spark Applications
  • Writing a Spark Application
  • Building and Running an Application
  • Application Deployment Mode
  • The Spark Application Web UI
  • Configuring Application Properties
Module 13: Distributed Processing
  • Review: Apache Spark on a Cluster
  • RDD Partitions
  • Example: Partitioning in Queries
  • Stages and Tasks
  • Job Execution Planning
  • Example: Catalyst Execution Plan
  • Example: RDD Execution Plan
Module 14: Distributed Data Persistence
  • DataFrame and Dataset Persistence
  • Persistence Storage Levels
  • Viewing Persisted RDDs
Module 15: Common Patterns in Apache Spark Data Processing
  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means
Module 16: Common Patterns in Apache Spark Data Processing
  • Apache Spark Streaming Overview
  • Example: Streaming Request Count
  • DStreams
  • Developing Streaming Applications
Module 17: Apache Spark Streaming: Processing Multiple Batches
  • Multi-Batch Operations
  • Time Slicing
  • State Operations
  • Sliding Window Operations
  • Preview: Structured Streaming
Module 18: Apache Spark Streaming: Data Sources
  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
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Course Prerequisites

This course is best suited to developers and engineers who have programming experience. Knowledge of Java is strongly recommended and is required to complete the hands-on exercises.

Few of the benefits of doing a Hadoop Developer certification include:

  • Data Analytics – There is an avalanche of unorganized data that companies can decipher and leverage it to make timely business improvements. The advent of big data and cloud computing has made courses like Hadoop Developer with Spark training quite relevant considering the growing need of data analytics.
  • Seamless Integration – Apache Spark is designed to run on Hadoop Distributed File System. This seamless integration with Hadoop reduces the learning curve as anyone who is already familiar with Hadoop can quickly learn Spark.
  • Mass Adoption – A survey of 1200 professionals conducted by DNV GL reported that companies which adopted Big Data observed a 23% increase in efficiency, 16% observed better decision making and 11% reported financial savings. IT professionals who do a Hadoop Developer with Spark training certification therefore are at an advantage when it comes to job prospects.

Give an edge to your career with Cloudera certification training courses. Students can join the classes for Hadoop Developer with Spark certification training course at Koenig Campus located at New Delhi, Bengaluru, Shimla, Goa, Dehradun, Dubai & Instructor-Led Online.

FAQ's


Yes, fee excludes local taxes.
Hadoop is an open source, Java-based programming framework that supports the processing and storage of large data sets in a distributed computing environment. It can quite easily expand from single servers to thousands of machines, each providing computation and storage.
Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs. Spark can efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets. Data Scientists commonly use machine learning to decipher large and complex unorganized data and Spark can help them in their need for iterative data processing.
Rescheduling requests should come in at least 24 hours prior to your scheduled appointment. Rescheduling less than 24 hours prior to your appointment will result in forfeiture of your exam fees. All exams are non-refundable and non-transferable. All exam purchases are valid for one year from date of purchase.
Your job prospect will improve on the successful completion of the Hadoop Developer certification. There is a huge demand for Big Data and Data Analytics professionals in the industry and the remuneration they get is also good.
There are no prerequisites for taking this certification. However, this course is best suited for developers and engineers who have prior programming experience. Knowledge of Java is also strongly recommended.
A score report will be emailed to you after you take the exam, typically within a few hours of taking the exam. If you end up successfully clearing the exam, you will receive your digital certificate in three days.
If you pass the exam a certificate in PDF format will be e-mailed to you.
Candidates who are not able to pass the exam must wait for a period of thirty calendar days, beginning the day after the failed attempt, before they may retake the same exam.