Building Data Analytics Solutions Using Amazon Redshift (BDASAR) – Details

Detaillierter Kursinhalt

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