Force7 Training
FRCAWS-29AWS

Building Data Analytics Solutions Using Amazon Redshift

Duration · 1 dayVirtual + In-PersonInstructor-Led

Course Description

This one-day instructor-led course teaches participants how to design, build, optimize, and manage cloud-based analytics solutions using Amazon Web Services services with a focus on Amazon Redshift. Learners gain hands-on experience with data ingestion, transformation, querying, performance tuning, security, and analytics integration.

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Audience Profile

This course is intended for:

  • Data Analysts
  • Data Engineers
  • Database Developers
  • BI Developers
  • Cloud Architects
  • Solutions Architects
  • Technical Managers

Prerequisites

Before enrolling, you should have:

  • Participants should have:
  • Basic SQL knowledge
  • Familiarity with relational databases
  • General understanding of cloud computing concepts
  • Basic familiarity with AWS services recommended

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Explain core concepts of modern cloud data warehousing
  • 2Describe the architecture and capabilities of Amazon Redshift
  • 3Load and transform data into Amazon Redshift
  • 4Query and analyze datasets using SQL
  • 5Optimize Redshift performance and cost
  • 6Implement security and governance best practices
  • 7Integrate Redshift with analytics and visualization services
  • 8Build an end-to-end analytics workflow on AWS

— Day-by-Day —

Course Outline

Module 1

Introduction to Modern Data Analytics on AWS

Topics

  • Evolution of analytics architectures
  • Data lakes, data warehouses, and lake houses
  • Overview of AWS analytics services
  • Role of Amazon Redshift in analytics platforms
  • Common analytics use cases

Lab

  • Explore the AWS analytics ecosystem
  • Review sample analytics architectures

Module 2

Amazon Redshift Architecture and Core Components

Topics

  • Amazon Redshift architecture overview
  • Clusters, nodes, and managed storage
  • RA3 nodes and scalability
  • Leader nodes and compute nodes
  • Columnar storage and massively parallel processing (MPP)
  • Redshift Serverless overview
  • Understanding workloads and concurrency

Lab

  • Launch and configure an Amazon Redshift environment
  • Explore cluster configuration settings

Module 3

Data Ingestion and Loading Strategies

Topics

  • Data ingestion patterns
  • Loading data from Amazon S3
  • COPY command fundamentals
  • Data formats: CSV JSON Parquet ORC
  • Incremental versus batch loading
  • Data validation techniques
  • Using AWS Glue with Redshift

Lab

  • Load structured and semi-structured data into Redshift
  • Validate and review loaded datasets

Module 4

Querying and Transforming Data

Topics

  • SQL fundamentals for analytics
  • Joins, aggregations, and window functions
  • Materialized views
  • Data transformations and ELT concepts
  • Working with semi-structured data
  • Query execution concepts
  • Spectrum overview for querying data in Amazon S3

Lab

  • Build analytical SQL queries
  • Create transformations and materialized views
  • Query external data using Redshift Spectrum

Module 5

Performance Optimization and Cost Management

Topics

  • Distribution styles and keys
  • Sort keys and table design
  • Query optimization techniques
  • Workload management (WLM)
  • Concurrency scaling
  • Result caching
  • Monitoring and troubleshooting queries
  • Cost optimization best practices

Lab

  • Analyze query performance
  • Optimize table and query design
  • Configure workload management settings

Module 6

Security, Governance, and Compliance

Topics

  • Authentication and authorization
  • IAM integration
  • Encryption at rest and in transit
  • Network security considerations
  • Role-based access control
  • Auditing and logging
  • Data governance best practices

Lab

  • Configure user permissions and roles
  • Implement encryption and security controls

Module 7

Analytics and Visualization Integration

Topics

  • Connecting BI tools to Amazon Redshift
  • Overview of Amazon QuickSight
  • Dashboard and reporting concepts
  • Data sharing capabilities
  • Federated query concepts
  • Real-time and near real-time analytics patterns

Lab

  • Connect a visualization tool to Redshift
  • Build a sample analytics dashboard

Module 8

End-to-End Analytics Solution Workshop

Topics

  • Designing a complete analytics pipeline
  • Data ingestion to visualization workflow
  • Operational best practices
  • Monitoring and automation concepts
  • Reviewing architectural tradeoffs
  • Capstone Lab
  • Build an end-to-end analytics solution using: Amazon S3 AWS Glue Amazon Redshift Visualization and reporting tools

— Additional Details —

What else is included

Hands-On Activities Included

  • Throughout the course, participants will:
  • Provision and configure Amazon Redshift
  • Load and transform analytics datasets
  • Execute analytical SQL workloads
  • Tune performance and optimize queries
  • Secure analytics environments
  • Build dashboards and reports
  • Implement a complete analytics workflow
  • Course Delivery Method
  • Instructor-led presentation
  • Guided demonstrations
  • Hands-on labs
  • Interactive discussions
  • Architecture reviews
  • End-of-day capstone exercise
  • Suggested Follow-On Courses
  • Architecting Data Lakes on AWS
  • Data Engineering on AWS
  • Building Streaming Data Analytics Solutions
  • Machine Learning Engineering on AWS
  • Advanced Analytics with AWS Services

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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