FRCAWS-29AWS

Building Data Analytics Solutions Using Amazon Redshift

Name: Building Data Analytics Solutions Using Amazon Redshift
Price: 650.00 USD
Availability: InStock

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

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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