Course Overview

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. In this training, you will also learn to apply best practices to support security, performance, and cost optimization of Amazon Redshift.

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Level: Intermediate

Duration: 1 Day

Delivery Type: Instructor-Led Training

Course 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

Prerequisites

Required

• Students familiar with combining AWS technologies to support data lakes or other data-driven workloads will benefit from this course

Recommended

• Completed either AWS Technical Essentials or Architecting on AWS
• Completed Building Data Lakes on AWS

Who Should Go For This Training?

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

Course Outline

Day 1

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: Setting up your data warehouse using Amazon Redshift

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

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