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AW271: Building Data Analytics Solutions using Amazon Redshift

Training: AWS™ - Cloud

Participants receive a practical introduction to building data analytics solutions with Amazon Redshift. The course covers the components of the analytics pipeline from data ingestion to cataloging and processing, as well as integration with data lakes for analytics and machine learning workloads. It also addresses best practices for security, performance, and cost management.

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Possible course languages: German

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Agenda:

Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics

Using Amazon Redshift in the Data Analytics Pipeline
- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift

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

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

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

Security and Monitoring of Amazon Redshift Clusters
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters

Designing Data Warehouse Analytics Solutions
- Data warehouse use case review
- Activity: Designing a data warehouse analytics workflow

Developing Modern Data Architectures on AWS™
- Modern data architectures

Objectives:

In this course AW271 Building Data Analytics Solutions using Amazon Redshift, 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

Target audience:

This course AW271 Building Data Analytics Solutions using Amazon Redshift is intended for:

Data Warehouse Engineers
Data Platform Engineers
Architects and Operators who build and manage data analytics pipelines

Prerequisites:

To participate in the course AW271 Building Data Analytics Solutions using Amazon Redshift at qSkills™, you should have completed the following AWS™ trainings:

Students with a minimum one-year experience managing data warehouses will benefit from this course.

Description:

In this course AW271 Building Data Analytics Solutions using Amazon Redshift, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course AW271 Building Data Analytics Solutions using Amazon Redshift 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 AW271 Building Data Analytics Solutions using Amazon Redshift includes presentations, interactive demos, practice labs, discussions, and class exercises.