AWS Solutions Architect Associate 2022: Compute & Data Analysis Services

Amazon Web Services    |    Intermediate
  • 9 videos | 49m 15s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 160 users Rating 4.5 of 160 users (160)
Matching AWS service configuration to workload requirements ensures optimal performance and tight cost control. Amazon Elastic Compute Cloud (EC2) instances can be resized. Auto scaling can add or remove instances to support workload peaks. Various serverless compute options can be leveraged to meet specific business needs. In-memory data caching using ElastiCache greatly enhances performance related to how specific data sets are used. AWS also provides an array of data analytic and ingestion tools. In this course, discover how to resize EC2 instances using the console and the CLI. Next, configure auto scaling to support an application workload. Then, explore when Amazon Fargate, EMR, and Batch should be used. Finally, examine ElastiCache, the AWS Global Accelerator, as well as AWS data analytics and ingestion services, like Amazon Athena and AWS Glue. This course can be used to prepare for exam SAA-C03, AWS Certified Solutions Architect - Associate.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Use the gui to change the elastic compute cloud (ec2) instance type to accommodate a workload
    Use the cli to change the ec2 instance type
    Describe how auto scaling increases application availability and performance
    Create an ec2 launch configuration auto scaling group
  • Recognize when amazon fargate, emr, and batch should be used
    Recognize the benefit of database caching
    Recognize when to use amazon athena, amazon quicksight, amazon kinesis, aws glue, and aws lake formation
    Summarize the key concepts covered in this course

IN THIS COURSE

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