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AWS Redshift Online Certificate Training
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About Course
🧾 AWS Redshift – About Course
The AWS Redshift Online Training is designed to help learners master Amazon’s fully managed, petabyte-scale data warehouse solution. This course teaches how to design, deploy, and manage Redshift clusters, enabling fast query performance and scalable data analytics. Learners gain expertise in data modeling, ETL integration, query optimization, and cost management using real-time examples.
Through hands-on labs and projects, you’ll explore how AWS Redshift integrates with services like S3, Glue, Athena, QuickSight, and Lambda to create powerful data pipelines and analytics solutions. The course provides practical knowledge to efficiently store, analyze, and visualize large datasets using Redshift’s columnar storage and MPP architecture.
This training is ideal for Data Engineers, BI Developers, Database Administrators, and Cloud Architects looking to advance their skills in cloud-based data warehousing and analytics.
⚙️ Key Learning Highlights
- Understand AWS Redshift architecture and core concepts
- Learn cluster creation, configuration, and scaling
- Optimize SQL queries and performance tuning in Redshift
- Manage security, IAM roles, and encryption
- Integrate Redshift with AWS S3, Glue, and QuickSight
- Implement ETL pipelines for data ingestion and transformation
- Monitor workloads using CloudWatch and system tables
- Learn best practices for cost optimization and maintenance
🎯 Course Benefits
- Master cloud data warehousing and analytics on AWS
- Gain real-time, hands-on project experience
- Improve skills in ETL, performance tuning, and data modeling
- Prepare for AWS Data Analytics or Database Specialty Certification
- Build a career as an AWS Redshift Developer or Data Engineer
AWS Redshift Training Course Syllabus
Introduction to Amazon Web services
- Amazon Web Services Stack
- Introduction to Amazon DATABASE
Introduction to AWS Redshift
- AWS Redshift – Data Warehouse-as-a-Service
- Features
- Pricing
AWS Redshift Architecture Overview
- Clusters
- Leader and Compute Nodes
- Node Slices
- Columnar Storage for performance
- Economics of Redshift
- Common Use cases
AWS Redshift – Hands-On
- Launch a new Redshift Cluster
- Modifying a Cluster – resize, showdown, delete, reboot.
- Security Groups.
- Parameter groups.
- Database Encryption.
- Backup and recovery – creating manual snapshots and automatic snapshots.
- Authorize access to Cluster
- Getting Information about Cluster Configuration.
- Database Audit Logging
Accessing Amazon Redshift cluster – Hands-On (AWS Redshift )
- Install and configure client SQL tools using
- Create Database, Users, user groups, permissions, and access controls.
- Connect to Redshift Cluster
- Load sample data into the cluster
- Create and test queries against the data
Monitor cluster performance – Hands-On
- Analyzing cluster Performance data
- Analyze query execution
- Creating Alarm and working with performance metrics
Designing tables – Hands-On
- DDL SQL – Creating Tables, Alter tables, Drop tables.
- LIMITATIONS and what is implemented differently.
- Selecting distribution Style and distribution keys.
- Selecting Sort Key.
- Choose best Distribution key covering various use cases
- Choosing best sort keys covering various use cases.
- Choosing a column compression type.
- Define constraints
Loading data – Hands-On
- Using Copy to Load data
- Loading data from S3
- Using a Manifest to Specify Data Files
- Loading Compressed Files
- Loading Fixed-Width Data
DML Operations
- Insert, Select, Update, Delete
- Deep Copy
- System Tables for Troubleshooting Data Loads
Unloading Data
- Unloading Data to Amazon S3
- Unloading Data in Delimited or Fixed-Width Format
- Reloading Unloaded Data
Performance Tuning – Hands-On
- Query ProcessingQuery Planning and Execution Workflow
- Reviewing Query Plan Steps
- Query Plan
- Factors Affecting Query Performance
- Analyzing and Improving Queries
- Query Analysis Workflow
- Reviewing Query Alerts
- Analyzing the Query Plan
- Analyzing the Query Summary
- Improving Query Performance
- Diagnostic Queries for Query Tuning
- Implementing Workload Management
- Defining Query Queues
- Modifying the WLM Configuration
- WLM Queue Assignment Rules
- Assigning Queries to QueuesDynamic and Static Properties
- Monitoring Workload Management
- Configuring WLM Queues to Improve Query Processing
- Troubleshooting Queries
- Building Admin queries from system tables to analyze performance.
- Migration of Existing BI Systems to Redshift
- Build custom ETL and/or ELT framework from an OLTP db to Redshift
- Limitations and Best Practices for Redshift Data warehouse implementation.
AWS Redshift Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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CHOOSE YOUR OWN COMFORTABLE LEARNING EXPERIENCE
Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
- Certification Guidance
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Self-Paced Learning
- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
Corporate Training
- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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AWS Redshift Online Training FAQ'S
What is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse service designed for fast query performance and large-scale analytics.
What are the main components of Redshift architecture?
Redshift consists of a Leader Node (manages queries and metadata) and Compute Nodes (store and process data).
How is data loaded into Redshift?
Data can be loaded using the COPY command from Amazon S3, DynamoDB, or other data sources via AWS Glue or Data Pipeline.
What is the advantage of columnar storage in Redshift?
Columnar storage improves query speed and reduces I/O by reading only the necessary columns instead of entire rows.
How do you optimize performance in Redshift?
By using distribution keys, sort keys, compression, query optimization, and workload management (WLM).
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