Technical Building Blocks: A Technology Reference for Real-world Product Development

ISBN-13: 9781484286579 / Angielski / Miękka / 2022 / 401 str.

Gaurav Sagar; Vitalii Syrovatskyi

Opis

Beginning-Intermediate user level

This book offers comprehensive coverage of the various technologies and techniques used to build technical products. You will learn how technical product development is collaboratively done across multiple technical teams, primarily those in software engineering, data engineering, and AI/ML engineering. You will also be introduced to the technologies these teams use to develop features and products. 
Many roles in the organization work alongside these technical product development teams and act as liaisons between them, the stakeholders, the customers, and the leadership team. The people in these roles must understand technical aspects ranging from system design to artificial intelligence, and be able to engage in technical discussions with the engineering teams to determine the pros, cons, and risks associated with the development of a technology product or feature.Technical Building Blocks will help you master these technical skills. The book has just the right level of technical details to neither overwhelm with unnecessary technical depth, nor be superficial.
From concepts to code snippets, authors Gaurav Sagar and Vitalii Syrovatskyi cover it all to give you an understanding of the engineer's mind and their work. Special emphasis on figures and charts will help you grasp complex ideas more quickly. After reading this book, you’ll be able to effectively communicate with engineering teams, provide valuable inputs in the system design review meetings of upcoming features and products, synthesize and simplify technical updates for cross-functional teams and stakeholders, and pass those dreaded technical interviews at your dream companies.What You Will Learn

• Intrinsic details of the teams and techniques used for product development 
• Concepts of cloud computing and its deployment models
• System design fundamentals required to architect features and products   
• Evolution of data pipelines and data storage solutions to support big data
• ML and deep learning algorithms to build intelligence into products
• Securing products through identity and access management using cryptography
• Role and working of blockchains, smart contracts, NFTs, and dApps in Web3  

Who This Book is ForProfessionals in roles who work with software engineering teams and want to build their technical muscle, such as product managers, program managers, business analysts, project managers and product owners. Also useful for those preparing to crack the technical interview for these roles.

Spis treści

Chapter 1: Product development - A synergy of team, techniques, and technologies  

Composition of a product team
1. The Product manager
The UX researcher and the UX Designer
2. The Product marketing manager
The Product scientist / Data Scientist 
1. Popular software development methodologies
Waterfall vs Agile
1. Scrum vs Kanban
Version control 
1. Need for version control 
Understanding Git 
2. Gitfarm and Github 
Feature development using Git
2. Overview of core software development technologies 
OSI model and the Internet 
1. Client side vs server side
Cloud 
2. Microservices
Data management
3. Artificial intelligence
Cryptography 
4. Federated Identity management
Devops and CI/CD
1. Rise of Devops
Understanding CI / CD
3. Metrics monitoring 
Tracking health - System metrics
1. Tracking success - Product metrics (A/B tests, multivariate tests, multiarmed bandit models)
   
   

Chapter 2: Cloud - On demand computing resources for scale and speed 

1. History of cloud 
2. Motivations for cloud adoption 
Cloud delivery models
1. IaaS vs PaaS vs SaaS
Cloud deployment models
1. Public / Private / Hybrid
Virtualization 
1. OS based vs Hardware based
Virtualization management
3. Containerization
Container architecture
1. Containers vs VMs
Infrastructure as code 
4. Serverless compute 
Cloud storage
5. Cloud security and Networking
Threats and need for security
1. Data centers and the ISPs
Virtual private networks and Access control lists
2. Firewalls and Load balancers
Identity and access management
6. Service quality metrics (SLAs)
Use cases
1. Configuring a virtual machine in public cloud (EC2)
Static website using object storage in public cloud (S3)

 

Chapter 3: System design: Architecting robust, scalable and modular applications 

1. Need for distributed system design
2. Monolithics and some issues
3. Vertical and horizontal scaling
Key characteristics of distributed systems 
4. Considerations and trade-offs
Performance and scalability
1. Latency and throughput
Availability and consistency
1. Microservices
Communication style
1. RESTful, RPC, Webhook and GraphQL
API gateway and service discovery 
1. API documentation 
API measures (Latency, Availability, Robustness) 
2. Use case: Building a RESTful API
Content delivery networks (CDNs)
2. Load balancer and Reverse proxy
Database
1. Relational database management system 
Replication
1. Federation
Denormalization and Sharding
2.  
NoSQL systems
1. Key-value store
Document store
2. Columnar databases 
Graph databases
3. Cache
Motivation 
1. Types of caching (Client, CDN, server, application)
CDN
4. Asynchronism
Testing and Security
5. Use cases
Building a ticketing system (like ticketmaster)
1. Building a video streaming service (like Netflix)

 

Chapter 4: Data engineering and analytics - Managing data and deriving insights 

1. Data engineering and analytics
1. Evolution of data needs 
2. Supply chain of data (from raw to actionable insights)
2. Data storage 
3. Streaming data sources
4. NoSQL databases
RDBMS
5. Data warehouse
Data lake
1. Data pipelines
Data cleaning and transformation
1. ETL
Workflow orchestration (Airflow) 
2. Big data
Data vs Big data
1. Big data formats (Parquet, ORC, Avro)
Data Analytics 
1. Streaming vs batch analytics
Popular analysis tools  
1. Hadoop and Hive
Presto and Spark 
3. Popular data analytics platform
PowerBI, Tableau, Looker
1. Offerings from public cloud providers
   

Chapter 5: Artificial intelligence - Building intelligence through automatic learning 

1. Relationship of Machine learning and Deep learning 
Learning approaches of machine learning
2. Steps to solve a machine learning problem
Overview of ML algorithms
3. Popular (shallow) ML algorithms
Uses cases - Shallow ML in action
4. Overview of deep learning algorithms
Popular deep learning algorithms
5. Use cases - Deep learning in action
When not to use deep learning
6. Rise of AI Ethics
   

Chapter 6:  Information security - Safeguarding resources and building trust

1. Need for securing digital assets
Encryption and hashing
2. Digital signatures
Public key infrastructure
3. Certificate management (TLS)
Identity Management 
1. Single sign-on 
SAML
2. Openid / Oauth
Access Management
1. RBAC
ABAC
4. Use Cases
Use of digital signatures in Docusign
1. Use of JWT for financial transactions through Stripe
   
   

Chapter 7:  Specialty technologies - Special purpose technologies gaining traction

1. Blockchain 
2. History 
3. Structure
Popular applications (Cryptocurrencies and NFTs) 
4. Use case: Building a simple block chain
Internet of things (IoT)
1. History
IoT architecture
2. IoT Applications
Challenges and criticism 
3. IoT, Edge computing and 5G
Concept and applications
1. Virtual reality
Developments over time
1. Mixed reality
Applications 
2. Concerns
Search Engines
1. Information retrieval
Importance of relevance
2. Semantic search engines
Use case: Building a search engine using elastic search 

Appendix

1. Installing VirtualBox 
1. Windows 
2. MacOS 
3. Linux (Ubuntu)
2. Linux 101
1. Linux vs Mac OS vs Windows
Directory structure of linux 
2. Basic linux management through command line
3. Installing Docker 
1. Windows 
MacOS 
2. Linux (Ubuntu)
4. Introduction to Python 
1. Variables 
Data structures (Lists, Tuples, Dictionaries and Sets)
2. Flow control: Conditional statements and loops
Functions
3. Classes
4. Modules and Packages

 

O autorze

Gaurav Sagar is a director of product management at Salesforce, Inc. and has done product management at Indeed, Amazon Web Services, and Amazon payments. He has over 11 years of experience in building both consumer and enterprise products and has deep industry knowledge of cloud computing, online advertising, ecommerce, and fintech. He has multiple patents and speaks at conferences. He is also an avid programmer and was a data scientist prior to his transition in product management. He holds a M.S. in Business Analytics and a B.S. in Computer Science. In his off hours, he loves to hike and go on short road trips, besides programming for his hobby projects. 

Vitalii Syrovatskyi is an engineering manager at Google. Previously, he was the software development manager at Amazon where he led the development of products and features for Amazon Web Services (AWS) and Amazon payment products. He has over 15 years of experience in developing technical products, managing, and building engineering teams in multiple industries, namely, search advertising, cloud computing, capital management, online payments, and computer networking. He is founder of a tech company and has firsthand experience in leading cross-functional teams and managing all end-to-end aspects of the business. He has a M.S. and a B.S. in Mathematics, and a M.S. and a B.S. in Economics. Outside of work, he enjoys exploring the beautiful Pacific Northwest.

Blurb

This book offers comprehensive coverage of the various technologies and techniques used to build technical products. You will learn how technical product development is collaboratively done across multiple technical teams, primarily those in software engineering, data engineering, and AI/ML engineering. You will also be introduced to the technologies these teams use to develop features and products. 

any roles in the organization work alongside these technical product development teams and act as liaisons between them, the stakeholders, the customers, and the leadership team. The people in these roles must understand technical aspects ranging from system design to artificial intelligence, and be able to engage in technical discussions with the engineering teams to determine the pros, cons, and risks associated with the development of a technology product or feature.

Technical Building Blocks will help you master these technical skills. The book has just the right level of technical details to neither overwhelm with unnecessary technical depth, nor be superficial.

From concepts to code snippets, authors Gaurav Sagar and Vitalii Syrovatskyi cover it all to give you an understanding of the engineer's mind and their work. Special emphasis on figures and charts will help you grasp complex ideas more quickly. After reading this book, you’ll be able to effectively communicate with engineering teams, provide valuable inputs in the system design review meetings of upcoming features and products, synthesize and simplify technical updates for cross-functional teams and stakeholders, and pass those dreaded technical interviews at your dream companies.

What You Will Learn

• Intrinsic details of the teams and techniques used for product development 
• Concepts of cloud computing and its deployment models
• System design fundamentals required to architect features and products   
• Evolution of data pipelines and data storage solutions to support big data
• ML and deep learning algorithms to build intelligence into products
• Securing products through identity and access management using cryptography
• Role and working of blockchains, smart contracts, NFTs, and dApps in Web3