Amazon Redshift Training Online

8 Lesson

30 Hours

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Amazon Redshift Training Course equips learners with the skills to design, build, and manage high-performance cloud data warehouses using Amazon’s fully managed analytics service. This course covers essential concepts such as Redshift architecture, cluster management, data loading, SQL querying, optimization techniques, security, and much more. During this Amazon Redshift Course, all participants will gain practical experience through hands-on labs and real-world data engineering scenarios. This training is ideal for data engineers, analysts, and developers who want to build expertise in cloud-based data warehousing and advanced analytics within the AWS ecosystem.

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Overview

Prerequisites

Basic Knowledge of SQL
Understanding of Data Warehousing Concepts
Familiarity with AWS Basics (recommended but not mandatory)

What Will You Learn

What is Amazon Redshift - overview, key concepts, use-cases
Benefits, when and why to use Redshift (scale, speed, analytics, cloud-native warehousing)
Creating Redshift clusters - choosing node types, node count, configuration settings
Security configuration - IAM roles, access management, encryption, networking, user privileges
Designing data warehouses/schemas/tables for analytics workloads
Handling structured and semi-structured data (e.g. JSON, Parquet) and external tables/schemas
Redshift SQL: creating tables, inserting/updating data (DML), writing queries - basic to advanced
Use of features like Redshift Spectrum/external querying for data on S3; querying nested/semi-structured data
Automatic table optimization/manual optimization - ensuring efficient storage and query performance
Configuring access control: users, roles, privileges, permissions (including RBAC)

Key Features

1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
100% Job Assistance
Flexible Schedule

Course Curriculum

Lesson 1 - Introduction & Fundamentals

1. What is Amazon Redshift — overview, key concepts, use-cases.

2. Data warehouse architecture: cluster-based storage, leader & compute nodes, differences between provisioned and serverless.

3. Benefits, when and why to use Redshift (scale, speed, analytics, cloud-native warehousing).

Lesson 2 - Cluster Setup & Management

1. Creating Redshift clusters — choosing node types, nodes count, configuration settings.

2. Serverless vs provisioned clusters; managing namespaces, workgroups, compute resources (for Serverless).

3. Security configuration — IAM roles, access management, encryption, networking, user privileges.

Lesson 3 - Data Modeling, Loading & Schema Design

1. Designing data warehouses / schemas / tables for analytics workloads.

2. Data loading strategies: loading data from external sources (e.g. S3), data ingestion pipelines (ETL / ELT), integrating with data-lakes or data lakes + Redshift setups.

3. Handling structured, semi-structured data (e.g. JSON, Parquet) and external tables/ schemas.

Lesson 4 - SQL, Querying & Analytics

1. Redshift SQL: creating tables, inserting/updating data (DML), writing queries — basic to advanced.

2. Advanced SQL constructs: analytics queries, aggregation, date-time handling, complex queries.

3. Use of features like Redshift Spectrum / external querying for data on S3, querying nested/semi-structured data.

Lesson 5 - Performance Optimization & Table Optimization

1. Columnar storage, compression, data distribution styles, sort keys — to optimize storage and query speed.

2. Automatic table optimization / manual optimization — ensuring efficient storage, query performance.

3. Workload Management (WLM) / resource management / concurrency / tuning for heavy analytic workloads.

Lesson 6 - Security, Access Control & Data Governance

1. Configuring access control: users, roles, privileges, permissions (including RBAC).

2. Ensuring data security, compliance, secure storage/transport, encryption, secure integration with other services.

3. Managing sharing of data/clusters: cross-cluster data sharing, secure sharing.

Lesson 7 - Integration with Other AWS / Data Services & Ecosystem

1. Integrating Redshift with data lakes (e.g. S3), ETL/ELT pipelines (e.g. using AWS Glue), external data sources.

2. Connecting BI tools / analytics or reporting tools to Redshift for data visualization, reporting.

3. Supporting machine-learning or advanced analytics workflows using Redshift data.

Lesson 8 - Monitoring, Maintenance & Troubleshooting

1. Monitoring cluster performance, query performance, resource usage, scaling as needed.

2. Maintenance tasks, backups, data management, logging and auditing operations.

3. Troubleshooting performance bottlenecks, optimizing queries, redesigning schemas or data distribution for better throughput.

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Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 30 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 13 Jul 2026
Weekday Batch 13 Jul 2026
Weekend Batch 18 Jul 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

Amazon Redshift Certification

We provide a Course Completion Certificate to all participants who successfully finish the Amazon Redshift Training. This certificate validates the learner’s understanding of Redshift architecture, data warehousing concepts, and hands-on implementation skills.

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Amazon Redshift Training Online