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Data Warehousing on AWS (DWAWS)

Cloud Computing - AWS
In this course, you will learn concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. We will demonstrate how to collect, store, and prepare data for the data warehouse by using other AWS services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon Simple Storage Service (Amazon S3). We will also explore how to use business intelligence (BI) tools to perform analysis on your data.

Data Warehousing on AWS (DWAWS) Objectives

  • In this course you will learn to:
  • Evaluate the relationship between Amazon Redshift and other Big Data systems
  • Evaluate use cases for data warehousing workloads and review real-world implementation of AWS data and analytic services as part of a data warehousing solution
  • Choose an appropriate Amazon Redshift node type and size for your data needs
  • Understand which security features are appropriate for Amazon Redshift such as encryption IAM permissions and database permissions
  • Launch an Amazon Redshift cluster and use the components features and functionality to implement a data warehouse in the cloud
  • Use other AWS data and analytic services such as Amazon DynamoDB Amazon EMR Amazon Kinesis Firehose and Amazon S3 to contribute to the data warehousing solution
  • Evaluate approaches and methodologies for designing data warehouses
  • Identify data sources and assess requirements that affect the data warehouse design
  • Design the data warehouse to make effective use of compression data distribution and sort methods
  • Load and unload data and perform data maintenance tasks
  • Write queries and evaluate query plans to optimize query performance
  • Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing
  • Audit monitor and receive event notifications about activities in the data warehouse by using features and services such as Amazon Redshift database audit logging Amazon CloudTrail Amazon CloudWatch and Amazon Simple Notification Service (Amazon SNS)
  • Prepare for operational tasks such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
  • Use a BI application to perform data analysis and visualization tasks against your data

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Key Point of Training Programs

  • Data Warehousing on AWS (DWAWS) Prerequisites

    Who should attend
    This course is intended for:

    Database architects
    Database administrators
    Database developers
    Data analysts
    Data scientists
    Prerequisites
    We recommend that attendees of this course have the following prerequisites:

    AWS Technical Essentials (AWSE) or equivalent experience with AWS
    Familiarity with relational databases and database design concepts

  • Data Warehousing on AWS (DWAWS) Delivery Format

    In-Person

    Online

  • Data Warehousing on AWS (DWAWS) Outline

    Evaluate the relationship between Amazon Redshift and other Big Data systems
    Evaluate use cases for data warehousing workloads and review real-world implementation of AWS data and analytic services as part of a data warehousing solution
    Choose an appropriate Amazon Redshift node type and size for your data needs
    Understand which security features are appropriate for Amazon Redshift, such as encryption, IAM permissions, and database permissions
    Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud
    Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon S3, to contribute to the data warehousing solution
    Evaluate approaches and methodologies for designing data warehouses
    Identify data sources and assess requirements that affect the data warehouse design
    Design the data warehouse to make effective use of compression, data distribution, and sort methods
    Load and unload data and perform data maintenance tasks
    Write queries and evaluate query plans to optimize query performance
    Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing
    Audit, monitor, and receive event notifications about activities in the data warehouse by using features and services such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS)
    Prepare for operational tasks such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
    Use a BI application to perform data analysis and visualization tasks against your data

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