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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.
This course includes presentations, interactive demos, labs, discussions and exercises.
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Course Contents
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- Module A: Overview of Data Analytics and the Data Pipeline
- Module 1: Using Amazon Redshift in the Data Analytics Pipeline
- Module 2: Introduction to Amazon Redshift
- Module 3: Ingestion and Storage
- Module 4: Processing and Optimizing Data
- Module 5: Security and Monitoring of Amazon Redshift Clusters
- Module 6: Designing Data Warehouse Analytics Solutions
- Module B: Developing Modern Data Architectures on AWS
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
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Target Group
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This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
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Knowledge Prerequisites
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Students with a minimum one-year experience managing data warehouses will benefit from this course.
We recommend that attendees of this course have:
• Completed either AWS Technical Essentials or Architecting on AWS
• Completed Building Data Lakes on AWS -
Please note our overview AWS Trainings!
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 |
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Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
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Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
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Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.

-
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.
This course includes presentations, interactive demos, labs, discussions and exercises.
-
Course Contents
-
- Module A: Overview of Data Analytics and the Data Pipeline
- Module 1: Using Amazon Redshift in the Data Analytics Pipeline
- Module 2: Introduction to Amazon Redshift
- Module 3: Ingestion and Storage
- Module 4: Processing and Optimizing Data
- Module 5: Security and Monitoring of Amazon Redshift Clusters
- Module 6: Designing Data Warehouse Analytics Solutions
- Module B: Developing Modern Data Architectures on AWS
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
-
Target Group
-
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
-
Knowledge Prerequisites
-
Students with a minimum one-year experience managing data warehouses will benefit from this course.
We recommend that attendees of this course have:
• Completed either AWS Technical Essentials or Architecting on AWS
• Completed Building Data Lakes on AWS -
Please note our overview AWS Trainings!
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 |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
