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Cloud Computing & DevOps

Building Data Analytics Solutions Using Amazon Redshift

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  • Overview

    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.

  • Who Should Take This Course

    AUDIENCE

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

    • Completed either AWS Technical Essentials or Architecting on AWS
    • Completed Building Data Lakes on AWS

    PREREQUISITES

    Students with a minimum one-year experience managing data warehouses will benefit from this course.

  • Why You Should Take This Course

    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
  • Schedule
  • Course 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
    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
    Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
    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
    Interactive Demo 4: Applying mixed workload management on Amazon Redshift
    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
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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