Course Information
Schedules
- 14 Jul 2026 (Tue) 9:30 AM - 5:00 PM
Registration period
23 Jun 2026 (Tue) - 13 Jul 2026 (Mon)
Price
HKD 3,000
(Early Bird HK3000
Standard HK6000)
(Early Bird HK3000
Standard HK6000)
Course Level
Study Mode
Duration
1 Day(s)
Language
Cantonese
Location
2/F, Centre Point, 181 Gloucester Road, Wanchai, HK
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Course Overview
Course description
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse
service. The course focuses on the data collection, ingestion, cataloging, storage, and processing
components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to
support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.
- • Course level: Intermediate
- • Duration: 1 day
Activities
- This course includes presentations, interactive demos, practice labs, discussions, and class exercises.
Course objectives
In this course, you will learn to:
• Compare the features and benefits of data warehouses, data lakes, and modern data architectures
• Design and implement a data warehouse analytics solution
• Identify and apply appropriate techniques, including compression, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store data
• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a
particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms
needed to gain actionable business insights
• Secure data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices
What You’ll Learn
Course outline
Module A: Overview of Data Analytics and the Data Pipeline
• Data analytics use cases
• Using the data pipeline for analytics
Module 1: Using Amazon Redshift in the Data Analytics Pipeline
• Why Amazon Redshift for data warehousing?
• Overview of Amazon Redshift
Module 2: Introduction to Amazon Redshift
• Amazon Redshift architecture
• Interactive Demo 1: Touring the Amazon Redshift console
• Amazon Redshift features
• Practice Lab 1: Load and query data in an Amazon Redshift cluster
Module 3: Ingestion and Storage
• Ingestion
• Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with
Data API
• Data distribution and storage
• Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
• Querying data in Amazon Redshift
• Practice Lab 2: Data analytics using Amazon Redshift Spectrum
Module 4: Processing and Optimizing Data
• Data transformation
• Advanced querying
• Practice Lab 3: Data transformation and querying in Amazon Redshift
• Resource management
• Interactive Demo 4: Applying mixed workload management on Amazon Redshift
• Automation and optimization
• Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
Module 5: Security and Monitoring of Amazon Redshift Clusters
• Securing the Amazon Redshift cluster
• Monitoring and troubleshooting Amazon Redshift clusters
Module 6: Designing Data Warehouse Analytics Solutions
• Data warehouse use case review
• Activity: Designing a data warehouse analytics workflow
Module B: Developing Modern Data Architectures on AWS
• Modern data architectures
