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Overview of Course

The CCA Spark and Hadoop Developer course is designed to teach developers how to build efficient and scalable big data applications using Apache Spark and Hadoop. This course is ideal for individuals who want to learn the fundamentals of Spark and Hadoop, and how to use these technologies to process large datasets.

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Course Highlights

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Learn the basics of Hadoop and Spark

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Understand HDFS and MapReduce concepts

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Use Spark RDDs to build efficient data processing applications




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Skills You’ll Learn

#1

Basics of Hadoop and Spark

#2

HDFS and MapReduce concepts

#3

Use Spark RDDs to build efficient data processing applications

#4

Spark SQL to process structured data

#5

Spark Streaming to process real-time data

#6

Hands-on experience with big data tools

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1-on-1 Training

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  • Option Item Access to live online classes
  • Option Item Flexible schedule including weekends
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  • Option Item Session recordings and learning courseware included
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  • Option Item Book a free demo before you commit!
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  • Option Item Custom Curriculum
  • Option Item Extended access to virtual labs
  • Option Item Detailed reporting of every candidate
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Course Reviews

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Skillzcafe's training program has been an exceptional learning experience for me. The course content was comprehensive and taught by industry experts, enabling me to acquire practical skills that are highly relevant. I strongly recommend Skillzcafe to anyone looking to upskill or kickstart their career.
Marshall Kumar
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The training was a game-changer for me! The course material was comprehensive, the instructors were knowledgeable and supportive, and the hands-on approach helped me gain practical experience. I am confident and ready to tackle any challenge. Thank you, Skillzcafe!
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Talent Fulfillment Specialist at Accenture
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I was thoroughly impressed with Skillzcafe's training program. The curriculum was comprehensive and up-to-date, and the instructors were knowledgeable and helpful. The hands-on approach helped me develop practical skills to apply in my future career. Overall, a fantastic learning experience!
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Lead Financial Analyst at AT&T
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The platform was easy to use, and the trainers were knowledgeable and supportive. I learned practical skills that have helped me in my job, and the flexibility of the online training allowed me to balance work and studies.
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Curriculum

  • Apache Hadoop and Hadoop Ecosystem
  • Overview
  • Data Ingestion and Storage
  • Data Processing
  • Data Analysis and Exploration
  • Other Ecosystem Tools
  • Introduction to the Hands-On Exercises

  • Apache Hadoop Cluster Components
  • HDFS Architecture
  • Using HDFS
     

  • YARN Architecture
  • Working With YARN

  • What is Apache Spark?
  • Starting the Spark Shell
  • Using the Spark Shell
  • Getting Started with Datasets and DataFrames
  • DataFrame Operations

  • Creating DataFrames from Data Sources
  • Saving DataFrames to Data Sources
  • DataFrame Schemas
  • Eager and Lazy Execution
     

  • Querying DataFrames Using Column Expressions
  • Grouping and Aggregation Queries
  • Joining DataFrames

  • RDD Overview
  • RDD Data Sources
  • Creating and Saving RDDs
  • RDD Operations

  • Writing and Passing Transformation Functions
  • Transformation Execution
  • Converting Between RDDs and DataFrames
     

  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations

  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API
  • Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
     

  • Datasets and DataFrames
  • Creating Datasets
  • Loading and Saving Datasets
  • Dataset Operations

  • Writing a Spark Application
  • Building and Running an Application
  • Application Deployment Mode
  • The Spark Application Web UI
  • Configuring Application Properties

  • 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

  • DataFrame and Dataset Persistence
  • Persistence Storage Levels
  • Viewing Persisted RDDs

  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means

  • Apache Spark Streaming Overview
  • Example: Streaming Request Count
  • DStreams
  • Developing Streaming Applications

  • Multi-Batch Operations
  • Time Slicing
  • State Operations
  • Sliding Window Operations
  • Preview: Structured Streaming

  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
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Description

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Target Audience:

  • Software Developers
  • Big Data Engineers
  • Data Analysts
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Prerequisite:

  • Basic programming skills in Python or Java
  • Basic understanding of SQL
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Benefits of the course:

  • Learn the latest technologies and tools used in big data processing
  • Gain hands-on experience with real-world projects and applications
  • Enhance your skills and increase your job opportunities in the big data industry
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Exam details to pass the course:

  • The CCA Spark and Hadoop Developer certification exam is a hands-on, practical exam that tests your knowledge of Spark and Hadoop. The exam consists of a set of tasks that you must complete using Spark and Hadoop.
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Certification path

  • Cloudera Certified Associate (CCA) Spark and Hadoop Developer Certification
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Career options after doing the course:

  • Big Data Engineer
  • Data Scientist
  • Data Analyst
  • Hadoop Developer
  • Spark Developer

Why should you take this course from Skillzcafe:

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Why should you take this course from Skillzcafe:
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FAQs

This course covers Python and Java programming languages.

Basic understanding of big data concepts is helpful, but not required.

Yes, the Cloudera Certified Associate (CCA) Spark and Hadoop Developer Certification exam is available for this course.

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