This Building Data Analytics Solutions Using Amazon Redshift course uses an Amazon Redshift data warehouse as part of the data analytics solution. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will design and build data analytics solutions for data warehousing use cases. You will learn how a data warehouse can be integrated into a data lake or a modern data architecture. You will also learn to apply best practices to support the security, performance, and cost optimization of Amazon Redshift.
Building Data Analytics Solutions Using Amazon Redshift Delivery Methods
Building Data Analytics Solutions Using Amazon Redshift Course Information
In this Building Data Analytics Solutions Using Amazon Redshift course, you will learn how 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
Building Data Analytics Solutions Using Amazon Redshift Prerequisites
We recommend that attendees of this course have:
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
Building Data Analytics Solutions Using Amazon Redshift Course Outline
- Data analytics use cases
- Using the data pipeline for analytics
- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift
- Amazon Redshift architecture
Interactive Demo 1: Touring the Amazon Redshift console
Practice Lab 1: Setting up your data warehouse using Amazon Redshift
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
Interactive Demo 4: Applying mixed workload management on Amazon Redshift
- Automation and optimization
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
- Data warehouse use case review
Activity: Designing a data warehouse analytics workflow
