Building Batch Data Analytics Solutions on AWS

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Course ID: AWS-DABATC
Duration: 1 Day
Training Fee: HK$6,000
Private in-house training
Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.

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  • The First Authorised Training Partner of AWS with full license
  • The Most Training Schedules delivered by AWS Authorized Instructors (AAI) and AAI Champion
  • Best Price Guaranteed
  • Trained over 50,000 talents in Asia
  • High Passing Rate: 90%
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Course 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
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 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
Intended Audience

This course is intended for:

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

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

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: Interactive analytics using Apache Spark on Amazon EMR
  • 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 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 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: Encrypting data at rest in Amazon EMR
  • Monitoring and troubleshooting EMR clusters
  • Demo: Reviewing Apache Spark cluster history
  • Monitoring and troubleshooting Amazon EMR clusters

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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