Book Image

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4 (2)
Book Image

AWS for Solutions Architects - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Imagine crafting cloud solutions that are secure, scalable, and optimized – not just good, but industry-leading. This updated guide throws open the doors to the AWS Well-Architected Framework, design pillars, and cloud-native design patterns empowering you to craft secure, performant, and cost-effective cloud architectures. Tame the complexities of networking, conquering edge deployments and crafting seamless hybrid cloud connections. Uncover the secrets of big data and streaming with EMR, Glue, Kinesis, and MSK, extracting valuable insights from data at speeds you never thought possible. Future-proof your cloud with game-changing insights! New chapters unveil CloudOps, machine learning, IoT, and blockchain, empowering you to build transformative solutions. Plus, unlock the secrets of storage mastery, container excellence, and data lake patterns. From simple configurations to sophisticated architectures, this guide equips you with the knowledge to solve any cloud challenge and impress even the most demanding clients. This book is your one-stop shop for architecting industry-standard AWS solutions. Stop settling for average – dive in and build like a pro!

Related Content you might be interested in

Current Title:

Small book image
AWS for Solutions Architects - Second Edition
Saurabh Shrivastava | Neelanjali Srivastav | Alberto Artasanchez | Imtiaz Sayed
Apr, 2023 | 692 pages
Small book image
Optimizing Your Modernization Journey with AWS
Mridula Grandhi
Jul, 2023 | 418 pages
Small book image
AWS Certified Cloud Practitioner Exam Guide
Rajesh Daswani
Jan, 2022 | 630 pages
Small book image
Modern Data Architecture on AWS
Behram Irani
Aug, 2023 | 420 pages
Table of Contents (19 chapters)
Preface
Preface
Who is this book for
What this book covers
To get the most out of this book
Get in touch
1
Understanding AWS Principles and Key Characteristics
Understanding AWS Principles and Key Characteristics
What is cloud computing?
What is AWS (Amazon Web Services)?
The market share, influence, and adoption of AWS
Basic cloud and AWS terminology
Why is AWS so popular?
Summary
Free Chapter
2
Understanding the AWS Well-Architected Framework and Getting Certified
Understanding the AWS Well-Architected Framework and Getting Certified
The AWS Well-Architected Framework
The six pillars of the Well-Architected Framework
AWS Well-Architected Lenses
Building credibility and getting certified
Learning tips and tricks for obtaining AWS certifications
The best way to get certified
Some frequently asked questions about the AWS certifications
Summary
3
Leveraging the Cloud for Digital Transformation
Leveraging the Cloud for Digital Transformation
Cloud computing models
Cloud migration strategy
Implementing a digital transformation program
The AWS Cloud Adoption Framework (AWS CAF)
Architectures to provide high availability, reliability, and scalability
Chaos engineering
Summary
4
Networking in AWS
Networking in AWS
Learning about the AWS Global Infrastructure
AWS networking foundations
Edge networking
Building hybrid cloud connectivity in AWS
AWS cloud network security
Summary
5
Storage in AWS – Choosing the Right Tool for the Job
Storage in AWS – Choosing the Right Tool for the Job
Understanding Amazon Elastic Block Store
Investigating Amazon Elastic File System (EFS)
Using Amazon FSx to manage file systems
Learning about Amazon Simple Storage Service (S3)
Versioning in Amazon S3
Choosing the right cloud storage type
Exploring Amazon S3 best practices
Building hybrid storage with AWS Storage Gateway
AWS Backup
Summary
6
Harnessing the Power of Cloud Computing
Harnessing the Power of Cloud Computing
Compute in AWS
Learning about Amazon EC2
Reviewing Amazon EC2 best practices
Amazon Elastic Load Balancing
Learning about serverless computing with AWS Lambda and Fargate
High-Performance Computing
Hybrid compute
Summary
7
Selecting the Right Database Service
Selecting the Right Database Service
A brief history of databases
Database consistency model
Database usage model
AWS database services
Benefits of AWS database services
Choosing the right tool for the job
Migrating databases to AWS
Summary
8
Best Practices for Application Security, Identity, and Compliance
Best Practices for Application Security, Identity, and Compliance
Understanding the importance of security, identity, and compliance in AWS
AWS security, identity, and compliance solutions
Getting familiar with identity and access management
Applying security controls
Building infrastructure protection
Building data protection
Adhering to compliances
Best practices for AWS security
Summary
9
Driving Efficiency with CloudOps
Driving Efficiency with CloudOps
What is the cloud operation (CloudOps) model, and what role does automation play?
Summary
10
Big Data and Streaming Data Processing in AWS
Big Data and Streaming Data Processing in AWS
Why use the cloud for big data analytics?
Amazon Elastic Map Reduce (EMR)
Introduction to AWS Glue
Choosing between AWS Glue and Amazon EMR
Handling streaming data in AWS
Choosing between Amazon Kinesis and Amazon MSK
Summary
11
Data Warehouses, Data Queries, and Visualization in AWS
Data Warehouses, Data Queries, and Visualization in AWS
Data warehouses in AWS with Amazon Redshift
Querying your data lake in AWS with Amazon Athena
Deep-diving into Amazon Athena
Using Amazon Athena Federated Query
Learning about Amazon Athena workgroups
Optimizing Amazon Athena
Using Amazon Athena versus Redshift Spectrum
Visualizing data with Amazon QuickSight
Putting AWS analytic services together
Summary
12
Machine Learning, IoT, and Blockchain in AWS
Machine Learning, IoT, and Blockchain in AWS
What is AI/ML?
AI/ML in AWS
Building ML best practices with MLOps
What is IoT?
Building IoT applications in AWS
Best practices to build AWS IoT applications
Blockchain in AWS
Quantum computing with AWS Braket
Generative AI
Summary
13
Containers in AWS
Containers in AWS
Understanding containerization
Virtual machines (VMs) and virtualization
Containers versus VMs
Learning about Docker
Amazon Elastic Container Service (ECS)
Learning about Kubernetes
Amazon Elastic Kubernetes Service (Amazon EKS)
Learning about AWS Fargate
Red Hat OpenShift Service on AWS (ROSA)
Choosing between AWS container services
Summary
14
Microservice Architectures in AWS
Microservice Architectures in AWS
Understanding microservices
Layered architecture
Event-driven architecture (EDA)
Microservices best practices
Domain-Driven Design (DDD)
Summary
15
Data Lake Patterns – Integrating Your Data across the Enterprise
Data Lake Patterns – Integrating Your Data across the Enterprise
Definition of a data lake
The purpose of a data lake
Components of a data lake
Data lakes in AWS with Lake Formation
Data lake best practices
Key metrics in a data lake
Lakehouse in AWS
Data mesh in AWS
Choosing between a data lake, lakehouse, and data mesh architecture
Summary
16
Hands-On Guide to Building an App in AWS
Hands-On Guide to Building an App in AWS
An introduction to the use case
Building architecture in AWS
Setting up services
Optimization with Well-Architected Review
Summary
17
Other Books You May Enjoy
Other Books You May Enjoy
18
Index
Index

Key metrics in a data lake

Now more than ever, digital transformation projects have tight deadlines and are forced to continue doing more with fewer resources. It is vital to demonstrate added value and results quickly.

Ensuring the success and longevity of a data lake implementation is crucial for a corporation, and effective communication of its value is essential. However, determining whether the implementation is adding value or not is often not a binary metric and requires a more granular analysis than a simple “green” or “red” project status.

The following list of metrics is provided as a starting point to help gauge the success of your data lake implementation. It is not intended to be an exhaustive list but rather a guide to generate metrics that are relevant to your specific implementation:

  • Size: It’s important to monitor two metrics when evaluating a lake: the overall size of the lake and the size of its trusted zone...
Previous Section
End of Section 7
Next Section