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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