Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

Kursinhalt

• Module A: Overview of Data Analytics and the Data Pipeline
• Module 1: Using Amazon Redshift in the Data Analytics Pipeline
• Module 2: Introduction to Amazon Redshift
• Module 3: Ingestion and Storage
• Module 4: Processing and Optimizing Data
• Module 5: Security and Monitoring of Amazon Redshift Clusters
• Module 6: Designing Data Warehouse Analytics Solutions
• Module B: Developing Modern Data Architectures on AWS

Voraussetzungen

Sie sollten mindestens ein Jahr Erfahrung in der Verwaltung von Data Warehouses mitbringen und folgende Kurse vorher besucht haben:

• Entweder AWS Technical Essentials (AWSE) oder Architecting on AWS (AWSA)
• Building Data Lakes on AWS (BDLA)

Zielgruppe

Dieser Kurs richtet sich an Data Warehouse Engineers, Data Platform Engineers, sowie Architects und Operators, die Datenanalyse-Pipelines erstellen und verwalten.

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