Book Image

Solutions Architect's Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect's Handbook - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.

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Table of Contents (22 chapters)
Preface
Preface
Who this book is for?
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
The Meaning of Solution Architecture
The Meaning of Solution Architecture
What is solution architecture?
The evolution of solution architecture
Why is solution architecture important?
The benefits of solution architecture
Solution architecture in the public cloud
Summary
2
Solution Architects in an Organization
Solution Architects in an Organization
Types of roles for a solution architect
Understanding a solution architect's responsibilities
Solution architects in an agile organization
Summary
3
Attributes of the Solution Architecture
Attributes of the Solution Architecture
Scalability and elasticity
Scaling your architecture
Static content scaling
High availability and resiliency
Fault tolerance and redundancy
Disaster recovery and business continuity
Extensibility and reusability
Usability and accessibility
Portability and interoperability
Operational excellence and maintainability
Security and compliance
Cost optimization and budget
Summary
4
Principles of Solution Architecture Design
Principles of Solution Architecture Design
Scaling workloads
Building a resilient architecture
Design for performance
Using replaceable resources
Think loose coupling
Think service not server
Using the right storage for the right requirements
Think data-driven design
Overcoming architectural constraints
Taking the minimum viable product approach
Adding security everywhere
Applying automation everywhere
Summary
5
Cloud Migration and Hybrid Cloud Architecture Design
Cloud Migration and Hybrid Cloud Architecture Design
Benefits of cloud native architecture
Popular public cloud choices
Creating a cloud migration strategy
Choosing a cloud strategy
Steps for cloud migration
Creating hybrid cloud architecture
Taking a multi-cloud approach
Designing cloud native architecture
Summary
Further reading
6
Solution Architecture Design Patterns
Solution Architecture Design Patterns
Building an n-tier layered architecture
Creating a multi-tenant SaaS-based architecture
Building stateless and stateful architecture designs
Understanding service-oriented architecture
Building a serverless architecture
Creating a microservice architecture
Building a queue-based architecture
Creating an event-driven architecture
Building a cache-based architecture
Understanding the circuit breaker pattern
Implementing the bulkheads pattern
Creating a floating IP pattern
Deploying an application with a container
Database handling in application architecture
Avoiding anti-patterns in solution architecture
Summary
7
Performance Considerations
Performance Considerations
Design principles for architecture performance
Technology selection for performance optimization
Managing performance monitoring
Summary
8
Security Considerations
Security Considerations
Designing principles for architectural security
Selecting technology for architectural security
Security and compliance certifications
The cloud's shared security responsibility model
Summary
9
Architectural Reliability Considerations
Architectural Reliability Considerations
Design principles for architectural reliability
Technology selection for architectural reliability
Improving reliability with the cloud
Summary
10
Operational Excellence Considerations
Operational Excellence Considerations
Designing principles for operational excellence
Selecting technologies for operational excellence
Achieving operational excellence in the public cloud
Summary
11
Cost Considerations
Cost Considerations
Design principles for cost optimization
Techniques for cost optimization
Cost optimization in the public cloud
Summary
12
DevOps and Solution Architecture Framework
DevOps and Solution Architecture Framework
Introducing DevOps
Understanding the benefits of DevOps
Understanding the components of DevOps
Introducing DevSecOps
Combining DevSecOps and CI/CD
Implementing a CD strategy
Implementing continuous testing in the CI/CD pipeline
Using DevOps tools for CI/CD
Implementing DevOps best practices
Building DevOps and DevSecOps in the cloud
Summary
13
Data Engineering for Solution Architecture
Data Engineering for Solution Architecture
What is big data architecture?
Designing big data processing pipelines
Data ingestion
Storing data
Processing data and performing analytics
Visualizing data
Designing big data architectures
Big data architecture best practices
Summary
14
Machine Learning Architecture
Machine Learning Architecture
What is machine learning?
Working with data science and ML
Machine learning in the cloud
Building machine learning architecture
Machine learning reference architecture
Machine learning operations
Deep learning
Summary
15
The Internet of Things Architecture
The Internet of Things Architecture
What is the Internet of Things?
Components of IoT architecture
IoT in the cloud
Building an industrial IoT solution
Summary
16
Quantum Computing
Quantum Computing
The building blocks of quantum computers
The working mechanism of quantum computers
Types of quantum computers
Quantum computing in real life
Quantum computing in the cloud
Summary
17
Rearchitecting Legacy Systems
Rearchitecting Legacy Systems
Learning the challenges of legacy systems
Benefits of system modernization
Defining a strategy for system modernization
Looking at legacy system modernization techniques
Defining a cloud migration strategy for legacy systems
Mainframe migration with the public cloud
Summary
18
Solution Architecture Document
Solution Architecture Document
Purpose of the SAD
Views of the SAD
Structure of the SAD
IT procurement documentation for a solution architecture
Summary
19
Learning Soft Skills to Become a Better Solution Architect
Learning Soft Skills to Become a Better Solution Architect
Acquiring pre-sales skills
Presenting to C-level executives
Taking ownership and accountability
Defining strategy execution with objectives and key results
Thinking big
Being flexible and adaptable
Design thinking
Being a builder by engaging in coding hands-on
Becoming better with continuous learning
Being a mentor to others
Becoming a technology evangelist and thought leader
Summary
20
Other Books You May Enjoy
Other Books You May Enjoy
21
Index
Index

Data Engineering for Solution Architecture

In the internet and digitization era, data is being generated everywhere with high velocity and volume. Getting insight from these huge amounts of data at a fast pace is challenging. We need to innovate continuously to ingest, store, and process this data to derive business outcomes.

With the convergence of cloud, mobile, and social technologies, advancements in many fields such as genomics and life sciences are growing at an ever-increasing rate. Tremendous value is found in mining this data for more insight. Modern stream processing systems need to produce continual results based on data with high input rates at low latency.

The concept of big data refers to more than just the collection and analysis of data. The actual value for organizations in their data can be used to gain insight and create competitive advantages. Not all big data solutions must end in visualization. Many solutions such as Machine Learning (ML) and other predictive...

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