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Building Data Analytics Solutions Using Amazon Redshift
Course Code: AW-BDAS
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Before attending this course, delegates must have:
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
After completing this course, students will be able 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
There is no associated exam with this course.
Modules
- Why Amazon Redshift for data warehousing?
- Overview of 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
- 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
- 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
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
- Data warehouse use case review
- Activity: Designing a data warehouse analytics workflow
- Data analytics use cases
- Using the data pipeline for analytics
- Modern data architecture