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AWS for Solutions Architects

You're reading from   AWS for Solutions Architects The definitive guide to AWS Solutions Architecture for migrating to, building, scaling, and succeeding in the cloud

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803238951
Length 692 pages
Edition 2nd Edition
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Authors (4):
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Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
Imtiaz Sayed Imtiaz Sayed
Author Profile Icon Imtiaz Sayed
Imtiaz Sayed
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Table of Contents (19) Chapters Close

AWS for Solutions Architects, Second Edition: Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services
1 Understanding AWS Principles and Key Characteristics FREE CHAPTER 2 Understanding AWS Well-Architected Framework and Getting Certified 3 Leveraging the Cloud for Digital Transformation 4 Networking in AWS 5 Storage in AWS – Choosing the Right Tool for the Job 6 Harnessing the Power of Cloud Computing 7 Selecting the Right Database Service 8 Best Practices for Application Security, Identity, and Compliance 9 Dive efficiency with Cloud Operation Automation and DevOps in AWS 10 Bigdata and streaming data processing in AWS 11 Datawarehouse, Data Query and Visualization in AWS 12 Machine Learning, IoT, and Blockchain in AWS 13 Containers in AWS 14 Microservice and Event-Driven Architectures 15 Domain-Driven Design 16 Data Lake Patterns – Integrating Your Data across the Enterprise 17 Availability, Reliability, and Scalability Patterns 18 AWS Hands-On Lab and Use Case

Putting AWS analytics services together

In the previous Chapter 10, Bigdata 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 to build a data processing pipeline. The following diagram shows a data processing and analytics architecture in AWS by applying various analytics services to build an end-to-end solution.

Figure 11.5 – Data analytics 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 these data are collected in the raw format first. You can clean, process and transform these data using an ETL platform such as EMR or Glue. Using Apache spark framework and writing data processing code from scratch is recommended to use Glue; otherwise, you can use EMR if you have a Hadoop skillset in your team. Transformed data stored...

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