The course dives deep into the Amazon Redshift service and the current thinking in building and operating data analytics pipelines to turn data into insights. You can ask questions and get real-time feedback from expert AWS instructors to immediately apply to your data analytics solutions. You’ll build a data warehouse analytics workflow based on a real-world scenario with the content learned through tutorials, hands-on labs, discussion, demonstration, presentations, and group exercises.

Duration: 1 day

Who is it for: This course is for data warehouse engineers, data platform engineers, architects and operators who build and manage data analytics pipelines.

Layout: This course includes presentations, interactive demos, practice labs, discussions, and class exercises..

Objectives

• 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

Modules

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 architecture