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AWS for Solutions Architects - Second Edition

You're reading from  AWS for Solutions Architects - Second Edition

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781803238951
Pages 692 pages
Edition 2nd Edition
Languages
Authors (4):
Saurabh Shrivastava Saurabh Shrivastava
Profile icon Saurabh Shrivastava
Neelanjali Srivastav Neelanjali Srivastav
Profile icon Neelanjali Srivastav
Alberto Artasanchez Alberto Artasanchez
Profile icon Alberto Artasanchez
Imtiaz Sayed Imtiaz Sayed
Profile icon Imtiaz Sayed
View More author details

Table of Contents (19) Chapters

Preface 1. Understanding AWS Principles and Key Characteristics 2. Understanding the 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. Driving Efficiency with CloudOps 10. Big Data and Streaming Data Processing in AWS 11. Data Warehouses, Data Queries, and Visualization in AWS 12. Machine Learning, IoT, and Blockchain in AWS 13. Containers in AWS 14. Microservice Architectures in AWS 15. Data Lake Patterns – Integrating Your Data across the Enterprise 16. Hands-On Guide to Building an App in AWS 17. Other Books You May Enjoy
18. Index

Components of a data lake

The concept of a data lake can vary in meaning to different individuals. As previously mentioned, a data lake can consist of various components, including both structured and unstructured data, raw and transformed data, and a mix of different data types and sources. As a result, there is no one-size-fits-all approach to creating a data lake. The process of constructing a clean and secure data lake can be time-consuming and may take several months to complete, as there are numerous steps involved in the process. Let’s take a look at the components that need to be used when building a data lake:

  • Data ingestion: The process of collecting and importing data into the data lake from various sources such as databases, logs, APIs, and IoT devices. For example, a data lake may ingest data from a relational database, log files from web servers, and real-time data from IoT devices.
  • Data storage: The component that stores the raw data in its original...
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