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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
18
Index

Putting AWS analytic services together

In the previous chapter, Chapter 10, Big Data and Streaming Data Processing in AWS, you learned about AWS ETL services such as EMR and Glue. In this chapter, let’s combine that with learning how to build a data processing pipeline. The following diagram shows a data processing and analytics architecture in AWS that applies various analytics services to build an end-to-end solution:

Figure 11.6: Data analytic architecture in AWS

As shown in the preceding diagram, data is ingested from various sources such as operational systems, marketing, and other systems in S3. You want to ingest data fast without losing it, so this data is collected in a raw format first. You can clean, process, and transform this data using an ETL platform such as EMR or Glue. Using the Apache Spark framework and writing data processing code from scratch is recommended when using Glue; otherwise, you can use EMR if you have Hadoop skill sets in your team...

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