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.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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

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


This course is intended for Data platform engineers, Architects and operators who build and manage data analytics pipelines.




Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.


Length: 1.0 day (8 hours)


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