Book Image

AWS for Solutions Architects

By : Alberto Artasanchez
3 (1)
Book Image

AWS for Solutions Architects

3 (1)
By: Alberto Artasanchez

Overview of this book

One of the most popular cloud platforms in the world, Amazon Web Services (AWS) offers hundreds of services with thousands of features to help you build scalable cloud solutions; however, it can be overwhelming to navigate the vast number of services and decide which ones best suit your requirements. Whether you are an application architect, enterprise architect, developer, or operations engineer, this book will take you through AWS architectural patterns and guide you in selecting the most appropriate services for your projects. AWS for Solutions Architects is a comprehensive guide that covers the essential concepts that you need to know for designing well-architected AWS solutions that solve the challenges organizations face daily. You'll get to grips with AWS architectural principles and patterns by implementing best practices and recommended techniques for real-world use cases. The book will show you how to enhance operational efficiency, security, reliability, performance, and cost-effectiveness using real-world examples. By the end of this AWS book, you'll have gained a clear understanding of how to design AWS architectures using the most appropriate services to meet your organization's technological and business requirements.
Table of Contents (20 chapters)
1
Section 1: Exploring AWS
4
Section 2: AWS Service Offerings and Use Cases
11
Section 3: Applying Architectural Patterns and Reference Architectures
17
Section 4: Hands-On Labs

Characteristics of a data lake

Another important thing to analyze when setting up data lakes is the characteristics of the data lake. As we will see in a later section, these characteristics can be measured and help us gauge the success or failure of a data lake:

  • Size: This is the "volume" in the often-mentioned three Vs of big data (volume, variety, velocity) – how big is the lake?
  • Governability: How easy is it to verify and certify the data in your lake?
  • Quality: What is the quality of the data contained in the lake? Are some records and files invalid? Are there duplicates? Can you determine the source and lineage of the data in the lake?
  • Usage: How many visitors, sources, and downstream systems does the lake have? How easy is it to populate and access the data in the lake?
  • Variety: Does the data that the lake holds have many types? Are there many types of data sources that feed the lake? Can the data in the lake be extracted in different...