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AWS for Solutions Architects

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4.3 (62)
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AWS for Solutions Architects

AWS for Solutions Architects

4.3 (62)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

The second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures. Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions. Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
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Index

AWS Compute Optimizer

AWS provides compute Optimizer recommends optimal instances for Amazon EC2 and Amazon EC2 Auto Scaling groups from 140+ instances from M, C, R, T, and X families. It uses machine learning models trained on millions of workloads to help customers optimize their compute resources for cost and performance across all workloads they run. You can take advantage of the recommendations in Compute Optimizer to reduce costs by up to 25%. The followings are the benefits of using compute Optimizer:

  • Get instance type and auto-scaling groups recommendations, making it easier for you to choose the right to compute resources for specific workloads.
  • Get a deep analysis of a workload's configuration, resource utilization, and performance data to identify dozens of defining characteristics, such as whether the workload is CPU-intensive and exhibits a daily pattern. Compute Optimizer then uses machine learning to process these characteristics to predict how the workload would...
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