Description

Introduction

Amazon Redshift is a fully managed cloud data warehousing service provided by Amazon Web Services (AWS). It enables organizations to store, manage, process, and analyze large volumes of structured and semi-structured data. This training introduces Amazon Redshift architecture, core concepts, data warehousing principles, cluster fundamentals, and serverless capabilities.

Participants will learn how Amazon Redshift works, understand its architecture, explore its key components, and develop the foundational skills required to build and manage cloud-based data warehouse solutions.

Prerequisites

1. Basic knowledge of databases and SQL.

2. Understanding of relational database concepts.

3. Familiarity with data warehousing fundamentals.

4. Basic knowledge of cloud computing concepts.

5. Familiarity with AWS services is beneficial but not mandatory.

Estimated Duration

1. Beginner Level: 16–20 hours

2. Intermediate Level: 24–30 hours

3. Advanced Level: 35–40 hours

4. Enterprise Level: 45–50 hours

Training Lab Setup Specifications

1. AWS account with appropriate permissions to access Amazon Redshift.

2. Amazon Redshift provisioned cluster or Serverless environment.

3. AWS Management Console access.

4. SQL client such as Query Editor v2 or a compatible database client.

5. Amazon S3 bucket for sample datasets.

6. Sample structured and semi-structured datasets.

7. Stable internet connection and supported web browser.

8. Basic understanding of AWS Identity and Access Management (IAM).

TABLE OF CONTENTS

Module 1: Amazon Redshift Fundamentals

1.1 Introduction to Amazon Redshift

   1.1.1 Overview of Amazon Redshift
1.1.2 Features and capabilities
1.1.3 Benefits of cloud data warehousing
1.1.4 Amazon Redshift use cases

1.2 Amazon Redshift Core Concepts

   1.2.1 Database and schema fundamentals
1.2.2 Tables, columns, and data types
1.2.3 SQL processing fundamentals
1.2.4 Redshift terminology and components

1.3 Amazon Redshift Service Options

   1.3.1 Provisioned cluster fundamentals
1.3.2 Amazon Redshift Serverless overview
1.3.3 Provisioned and Serverless environments
1.3.4 Selecting the appropriate deployment model

Module 2: Amazon Redshift Architecture

2.1 Amazon Redshift Architecture Overview

   2.1.1 Massively Parallel Processing architecture
2.1.2 Leader node and compute nodes
2.1.3 Node slices and parallel execution
2.1.4 Distributed query processing

2.2 Redshift Cluster Architecture

   2.2.1 Cluster components and configuration
2.2.2 Node types and cluster structure
2.2.3 RA3 node fundamentals
2.2.4 Managed storage concepts

2.3 Amazon Redshift Storage and Processing

   2.3.1 Columnar storage architecture
2.3.2 Data compression fundamentals
2.3.3 Query execution and processing flow
2.3.4 Parallel data processing

Module 3: Data Warehousing with Amazon Redshift

3.1 Data Warehousing Fundamentals

   3.1.1 Data warehouse concepts and architecture
3.1.2 OLAP and analytical workloads
3.1.3 Data warehouse components
3.1.4 Enterprise data warehouse applications

3.2 Data Warehouse Design

   3.2.1 Database and schema organization
3.2.2 Fact and dimension tables
3.2.3 Star and snowflake schemas
3.2.4 Data warehouse design principles

3.3 Data Organization in Amazon Redshift

   3.3.1 Table creation and organization
3.3.2 Distribution style fundamentals
3.3.3 Sort key fundamentals
3.3.4 Data organization best practices

Module 4: Amazon Redshift Environment Setup

4.1 AWS Environment Preparation

   4.1.1 AWS account and service access
4.1.2 Amazon Redshift console navigation
4.1.3 IAM roles and permissions overview
4.1.4 Network and connectivity fundamentals

4.2 Redshift Cluster Setup

   4.2.1 Creating a provisioned cluster
4.2.2 Selecting node types and configurations
4.2.3 Configuring database access
4.2.4 Connecting to a Redshift cluster

4.3 Redshift Serverless Setup

   4.3.1 Creating a Serverless workgroup
4.3.2 Configuring namespaces
4.3.3 Managing compute resources
4.3.4 Connecting to Serverless databases

Module 5: Practical Implementation and Data Warehouse Deployment

5.1 Working with Redshift Databases

   5.1.1 Creating databases and schemas
5.1.2 Creating tables and defining columns
5.1.3 Inserting and querying sample data
5.1.4 Exploring database objects

5.2 Data Warehouse Implementation

   5.2.1 Building a sample data warehouse
5.2.2 Implementing fact and dimension tables
5.2.3 Organizing analytical datasets
5.2.4 Executing analytical queries

5.3 Deployment and Best Practices

   5.3.1 Selecting suitable Redshift configurations
5.3.2 Applying data warehouse best practices
5.3.3 Understanding deployment considerations
5.3.4 Reviewing implementation results

Conclusion

This training provides a strong foundation in Amazon Redshift, covering its core concepts, architecture, deployment models, and data warehousing principles. Participants will gain practical experience in setting up Redshift environments, organizing analytical data, and implementing foundational cloud data warehouse solutions.

https://LocusIT.com/learning

https://aws.amazon.com/redshift/