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

Gaurav / Syrovatskyi Sagar

401 pages, parution le 22/10/2022

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Résumé

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 For

Professionals 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.

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

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

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

History of cloud
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
2. Virtualization management
Containerization
1. Container architecture
2. Containers vs VMs
Infrastructure as code
Serverless compute
Cloud storage
Cloud security and Networking
1. Threats and need for security
2. Data centers and the ISPs
3. Virtual private networks and Access control lists
4. Firewalls and Load balancers
5. Identity and access management
Service quality metrics (SLAs)
Use cases
1. Configuring a virtual machine in public cloud (EC2)
2. 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
4. Key characteristics of distributed systems
5. Considerations and trade-offs
6. 1. Performance and scalability
  2. Latency and throughput
  3. Availability and consistency
Microservices
1. Communication style
  1. RESTful, RPC, Webhook and GraphQL
  API gateway and service discovery
2. API documentation
3. API measures (Latency, Availability, Robustness)
4. Use case: Building a RESTful API
Content delivery networks (CDNs)
Load balancer and Reverse proxy
Database
1. Relational database management system
2. 1. Replication
  2. Federation
  3. Denormalization and Sharding
  NoSQL systems
  1. Key-value store
  2. Document store
  3. Columnar databases
  4. Graph databases
Cache
1. Motivation
2. Types of caching (Client, CDN, server, application)
3. CDN
Asynchronism
Testing and Security
Use cases
1. Building a ticketing system (like ticketmaster)
2. Building a video streaming service (like Netflix)

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

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

Chapter 5: Artificial intelligence - Building intelligence through automatic learning

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

Chapter 6: Information security - Safeguarding resources and building trust

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

Chapter 7: Specialty technologies - Special purpose technologies gaining traction

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

Appendix

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

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.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Apress
Auteur(s)	Gaurav / Syrovatskyi Sagar
Parution	22/10/2022
Nb. de pages	401
EAN13	9781484286579

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

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