Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

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Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

Fast Lane Institute for Knowledge Transfer GmbH
Logo Fast Lane Institute for Knowledge Transfer GmbH
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Description

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…

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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
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