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    Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

     Info
    This course uses an Amazon Redshift data warehouse as part of the data analytics solution. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will design and build data analytics solutions for data warehousing use cases. You will learn how a data warehouse can be integrated into a data lake or a modern data architecture. You will also learn to apply best practices to support security, performance, and cost optimization of Amazon Redshift.
    Listing Thumbnail

    Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

     Info

    Overview

    Course Overview

    This course uses an Amazon Redshift data warehouse as part of the data analytics solution. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will design and build data analytics solutions for data warehousing use cases. You will learn how a data warehouse can be integrated into a data lake or a modern data architecture. You will also learn to apply best practices to support security, performance, and cost optimization of Amazon Redshift.

    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 familiar with combining AWS technologies to support data lakes or other data-driven workloads will benefit from this course. We recommend that attendees of this course have:

    • Completed either AWS Technical Essentials (AWSE) or Architecting on Architecting on AWS (AWSA)
    • Completed Building Data Lakes on AWS (BDLA)

    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

    Course Content

    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

    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

    Highlights

    • This course uses an Amazon Redshift data warehouse as part of the data analytics solution. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will design and build data analytics solutions for data warehousing use cases. You will learn how a data warehouse can be integrated into a data lake or a modern data architecture.

    Details

    Delivery method

    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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