1 days.


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

    Skills Gained

    • 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

    Who Can Benefit?

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

    Outline for Building Batch Data Analytics Solutions on AWS Training

    Course Outline

    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
    02/05/2024 - 02/05/2024
    12:00 PM - 08:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00
    02/13/2024 - 02/13/2024
    09:00 AM - 05:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00
    03/27/2024 - 03/27/2024
    09:00 AM - 05:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00