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Enjoy the best in Building Batch Data Analytics Solutions Using Amazon Redshift Training

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.
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Building Batch Data Analytics Solutions Using Amazon Redshift Training

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.

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Intended Audience

This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
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Course Objectives

In this course, you will learn to:

  • 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
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Course Outline

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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Prerequisites

Students with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have: 

  • Completed either AWS Technical Essentials or Architecting on AWS
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