Learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service.

In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.

Who should take this course

This course is intended for:

Data platform engineers
Architects and operators who build and manage data analytics pipelines


Module A: Overview of Data Analytics and the Data Pipeline

Data analytics use cases
Using the data pipeline for analytics
Module 1: Introduction to Amazon EMR

Using Amazon EMR in analytics solutions
Amazon EMR cluster architecture
Interactive Demo 1: Launching an Amazon EMR cluster
Cost management strategies
Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage

Storage optimization with Amazon EMR
Data ingestion techniques
Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR

Apache Spark on Amazon EMR use cases
Why Apache Spark on Amazon EMR
Spark concepts
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
Transformation, processing, and analytics
Using notebooks with Amazon EMR
Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive

Using Amazon EMR with Hive to process batch data
Transformation, processing, and analytics
Introduction to Apache HBase on Amazon EMR
Module 5: Serverless Data Processing

Serverless data processing, transformation, and analytics
Using AWS Glue with Amazon EMR workloads
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions
Module 6: Security and Monitoring of Amazon EMR Clusters

Securing EMR clusters
Interactive Demo 3: Client-side encryption with EMRFS
Monitoring and troubleshooting Amazon EMR clusters
Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions

Batch data analytics use cases
Activity: Designing a batch data analytics workflow
Module B: Developing Modern Data Architectures on AWS

Modern data architectures

Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR
Practice Lab 2: Batch data processing using Amazon EMR with Hive





Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course. We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop. We recommend that attendees of this course have: Completed either AWS Technical Essentials or Architecting on AWS Completed either Building Data Lakes on AWS or Getting Started with AWS Glue

Course Details

What You Will Learn?

In this course, you will learn to:

Compare the features and benefits of data warehouses, data lakes, and modern data architectures
Design and implement a batch data analytics solution
Identify and apply appropriate techniques, including compression, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store data
Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices