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Building Batch Data Analytics Solutions on AWS (BBDAS)

Cloud Computing - AWS
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.

Building Batch Data Analytics Solutions on AWS (BBDAS) 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

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Key Point of Training Programs

  • Building Batch Data Analytics Solutions on AWS (BBDAS) Prerequisites

    Who should attend
    This course is intended for:

    Data platform engineers
    Architects and operators who build and manage data analytics pipelines
    Certifications
    This course is part of the following Certifications:

    AWS Certified Data Engineer - Associate
    Prerequisites
    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 recommend that attendees of this course have:

    Completed either Architecting on AWS (AWSA) or AWS Technical Essentials (AWSE)
    Completed either Building Data Lakes on AWS (BDLA) or Getting Started with AWS Glue

  • Building Batch Data Analytics Solutions on AWS (BBDAS) Delivery Format

    In-Person

    Online

  • Building Batch Data Analytics Solutions on AWS (BBDAS) 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
    Activity: Designing a batch data analytics workflow
    Module B: Developing Modern Data Architectures on AWS
    Modern data architectures

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