Big Data Hadoop and Spark Developer in Amazon Elastic MapReduce

The Big Data Hadoop and Spark Developer course has been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies focused on AWS and Amazon EMR.

Rod Davison

Data Science | advanced | 7 hours 5 minutes | Published: Oct 2020

Discussions

Overview

13.4KSTUDENTS^*

94.3%RECOMMEND^*

This course includes:

7+ hours of on-demand video
4 modules
Core level
Direct access/chat with the instructor
100% self-paced online
Many downloadable resources
Shareable certificate of completion

The Big Data Hadoop and Spark developer course has been designed to provide in-depth knowledge of Big Data processing using Hadoop and Spark in the AWS Elastic Map Reduce (EMR) environment. The practical hands-on aspects of the course focus on Amazon Web Services (AWS) and Amazon Elastic MapReduce (EMR), where all demos take place.

Skills You Will Gain

Amazon Elastic MapReduce (EMR)

Big Data

Data Science

Handoop

Spark Developer

Learning Outcomes (At The End Of This Program, You Will Be Able To...)

Describe the different main Hadoop ecosystems such as Hadoop 3, Yarn, Pig, and Hive.
Explain the functionality and architecture of the Hadoop Distributed File System (HDFS) and YARN resource management.
Explain how Map Reduce works and how it is implemented in the Hadoop environment.
Explain what the file formats used in BIg Data – Avro, Parquet, and Orc; and how to use them in the EMR environment.
Explain the difference between traditional RDBMS and Hive tables.
Explain the architecture and functionality of Spark
Use resilient distribution datasets (RDD) for data processing in Spark.
Implement and build Spark applications.
Write a basic functional code in Scala to run a Spark application.
Explain parallel processing in Spark and Spark optimization using Catalyst and Tungsten.
Use Spark SQL, creating, transforming, and querying Data frames.
Explain the differences and use cases for Spark RDDs, DataFrames, and DataSets.
Create and deploy AWS EC2 instances, EBS, and S3 storage volumes.
Explain the pricing models for AWS storage and compute resources.
Configure and deploy an EMR Cluster in AWS.
Run Spark and Map Reduce applications in batch and interactive mode on EMR.
Create and use EMR notebooks.
Explain the differences between IaaS and PaaS resources in AWS
Create and manage a personal AWS account

Prerequisites

A basic level of conceptual understanding of data warehouses is assumed, as well as an awareness of the core functions of SQL.

Who Should Attend

Data Analysts
Data Engineers
Data Scientists
Database Architects
Database Administrators

Curriculum

Instructors

^*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.

Big Data Hadoop and Spark Developer in Amazon Elastic MapReduce

Rod Davison

Data Science | advanced | 7 hours 5 minutes | Published: Oct 2020

Discussions

Overview

13.4KSTUDENTS^*

94.3%RECOMMEND^*

This course includes:

7+ hours of on-demand video
4 modules
Core level
Direct access/chat with the instructor
100% self-paced online
Many downloadable resources
Shareable certificate of completion

Skills You Will Gain

Amazon Elastic MapReduce (EMR)

Big Data

Data Science

Handoop

Spark Developer

Learning Outcomes (At The End Of This Program, You Will Be Able To...)

Describe the different main Hadoop ecosystems such as Hadoop 3, Yarn, Pig, and Hive.
Explain the functionality and architecture of the Hadoop Distributed File System (HDFS) and YARN resource management.
Explain how Map Reduce works and how it is implemented in the Hadoop environment.
Explain what the file formats used in BIg Data – Avro, Parquet, and Orc; and how to use them in the EMR environment.
Explain the difference between traditional RDBMS and Hive tables.
Explain the architecture and functionality of Spark
Use resilient distribution datasets (RDD) for data processing in Spark.
Implement and build Spark applications.
Write a basic functional code in Scala to run a Spark application.
Explain parallel processing in Spark and Spark optimization using Catalyst and Tungsten.
Use Spark SQL, creating, transforming, and querying Data frames.
Explain the differences and use cases for Spark RDDs, DataFrames, and DataSets.
Create and deploy AWS EC2 instances, EBS, and S3 storage volumes.
Explain the pricing models for AWS storage and compute resources.
Configure and deploy an EMR Cluster in AWS.
Run Spark and Map Reduce applications in batch and interactive mode on EMR.
Create and use EMR notebooks.
Explain the differences between IaaS and PaaS resources in AWS
Create and manage a personal AWS account

Prerequisites

A basic level of conceptual understanding of data warehouses is assumed, as well as an awareness of the core functions of SQL.

Who Should Attend

Data Analysts
Data Engineers
Data Scientists
Database Architects
Database Administrators

Curriculum

1Welcome to the course!

2Program Announcements

3Labs

4Quizzes

5Module 0

6Module 1

7Module 2

8Module 3

9Recommended Further Readings

Instructors

^*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.