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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