Building Data Analytics Solutions Using Amazon Redshift

  • Home
  • /
  • Courses
  • /
  • Building Data Analytics Solutions Using Amazon Redshift
Course ID: AWS-DAREDS
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.

Why Choose Us?

  • 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%
  • Appointed Exam Centre
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 data warehouse 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 data warehouses will benefit from this course. We recommend that attendees of this course have:

  • Completed either AWS Technical Essentials or Architecting on AWS
  • Completed either Building Data Lakes on AWS
Intended Audience

This course is intended for:

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

This course includes presentations, demonstrations, 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: Using Amazon Redshift in the Data Analytics Pipeline

  • Why Amazon Redshift for data warehousing?
  • Overview of Amazon Redshift

Module 2: Introduction to Amazon Redshift

  • Amazon Redshift architecture
  • Interactive Demo 1: Touring the Amazon Redshift console
  • Amazon Redshift features
  • Practice Lab 1: Setting up your data warehouse using Amazon Redshift

Module 3: Ingestion and Storage

  • Ingestion
  • Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
  • Data distribution and storage
  • Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
  • Querying data in Amazon Redshift
  • Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Module 4: Processing and Optimizing Data

  • Data transformation
  • Advanced querying
  • Practice Lab 3: Data transformation and querying in Amazon Redshift
  • Resource management
  • Interactive Demo 4: Applying mixed workload management on Amazon Redshift
  • Automation and optimization

Module 5: Security and Monitoring of Amazon Redshift Clusters

  • Securing the Amazon Redshift cluster
  • Monitoring and troubleshooting Amazon Redshift clusters

Module 6: Designing Data Warehouse Analytics Solutions

  • Data warehouse use case review
  • Activity: Designing a data warehouse analytics workflow

Module B: Developing Modern Data Architectures on AWS

  • Modern data architectures
Certification

Search for a course