Building Data Analytics Solutions Using Amazon Redshift

Seminarinformationen

Seminar - Ziel

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift

Teilnehmer - Zielgruppe

This course is intended for:

• data warehouse engineers,
• data platform engineers
• architects and operators who build
• manage data analytics pipelines.

Kurs - Voraussetzungen

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

Seminardauer

• 1 Tag
• 09:00 Uhr bis 17:00 Uhr

Schulungsunterlagen

• nach Absprache

Seminar-Inhalt / Agenda

• 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

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