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