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Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

Cloud Computing - AWS
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.

Building Data Analytics Solutions Using Amazon Redshift (BDASAR) 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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Key Point of Training Programs

  • Building Data Analytics Solutions Using Amazon Redshift (BDASAR) Prerequisites

    Who should attend
    This course is intended for:

    Data warehouse engineers
    Data platform engineers
    Architects and operators who build and manage data analytics pipelines
    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 (AWSE) or Architecting on AWS (AWSA)
    Completed Building Data Lakes on AWS (BDLA)

  • Building Data Analytics Solutions Using Amazon Redshift (BDASAR) Delivery Format

    In-Person

    Online

  • Building Data Analytics Solutions Using Amazon Redshift (BDASAR) Outline

    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: Setting up your data warehouse using Amazon Redshift
    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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