Software Architecture Mindmap

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Contents

• Section 1: Software Architecture Mindmap
• Section 2: Software Architecture Reference

• Section 3: Computer Science Papers

• Section 4: Data Science (ML/NN)

• Section 5: Terminology and Comparisons
• Section 6: Citation

Software_Architecture_Mindmap.png

Three Main Pillars of Software Architecture:

• Modern Application Development
• Cloud Computing (AWS/Azure/GCP)
• Data Science (ML/NN)

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- This mind map is based on the Fresco Play curriculum (the internal learning platform of Tata Consultancy Services). © 2022

- This repository was initially created for the mind map; currently, it is used as a curated collection of software architecture references.

Software Architecture Reference

• System Design 101: 🗺️ByteByteGo. System design basics explained with diagrams
• Awesome Lists: 🗂️Lists of useful links on many topics / awesome.re / github topic
• Ecosyste.ms: Awesome: Searchable index of awesome lists
• Awesome Software Architecture (simskij): Architecture patterns, principles, and tools
• Awesome Software Architecture: Articles, videos, patterns, and principles
• Software Architecture Books: Books on architecture styles, principles, and choices
• System Design: Scaling systems to more users; interview study
• Microsoft .NET Application Architecture - Reference Apps: Example online store built with ASP.NET Core
• System Design Fight Club: Example system design discussions
• System Design - Neo Kim: Concept notes and interview prep
• Awesome System Design Resources: Links for system design interview study
• Awesome Low Level Design (LLD) / Object Oriented Design (OOD): Class and object design; interview practice
• Awesome Engineering Articles: Engineering articles from technology companies

Trend Reports

• InfoQ: News and Articles
• InfoQ Trends Reports: 📖(Downloaded PDFs 2019 and 2021 - 2025)
• Dzone: RefCards and Trend Reports
• Thoughtworks: Technology Radar 📖(archived PDFs vo.1 - vol.34)
• Microsoft Learn: 🪟Documentation and Code samples
• Trendshift: GitHub Trending repositories
• Design Gurus: Portal For Tech Interviews

Industry Trends

• Software Industry Statistics: Statista market figures
• Gartner Top Strategic Technology Trends 2024: Ten key technology trends for 2024
• MAD (ML/AI/Data) Landscape: Map of companies in AI, machine learning, and data
• Hype Cycle for Emerging Technologies 2024: New technologies and how mature each one is
• Microsoft Digital Defense Report: Cyber threat and protection trends
• Google DORA: DevOps performance research and metrics
• Postman State of the API Report: Survey on API usage and tooling
• Stanford AI Index Annual Report: Yearly data on AI research, spending, and policy
• State of Devs

Newsletter

• Substack Leaderboard: Technology newsletters on Substack
• daily.dev: Personalized developer feed
• Hacker News

Mini-Books

• 🆓 Building Evolutionary Architectures: InfoQ mini-book on guiding architecture to change over time with fitness functions
• System Design Blueprint: The Ultimate Guide: Component checklist
• 🆓 InfoQ minibooks: Architectures You’ve Always Wondered About 📖2021 / 2023 / 2024 /2025 / Cell-Based Architecture
• 🆓 Building a scalable authorization system: How to design permissions for growing systems
• 🆓 Mastering RAG: Retrieval-augmented generation guide
• 🆓 Mastering AI Agents: AI agent guide
• 🆓 Agentic Architectures for Retrieval-intensive Applications: AI agents for search-based apps
• 🆓 Programming Notes for Professionals books: Free reference books for languages and tools
• 🆓 Google SRE Handbook: Monitoring and the four golden signals

Building from scratch

• How does a request travel in ~200ms?
• Let's Build a Simple Database: SQLite-like database in C
• Let’s Build A Simple Interpreter: Interpreter in Python, step by step
• Let’s Build A Web Server: Web server in Python, step by step
• Web Browser Engineering: Small web browser, from networking to rendering
• Clone Wars: Open-source copies of well-known websites
• Curated list of project-based tutorials: Projects grouped by programming language
• Master programming by recreating your favorite technologies from scratch: Rebuild databases, shells, and more
• Build frontend applications at scale: Organizing large web applications
• Writing an Operating System in 1,000 Lines: Small OS kernel
• minimal GPT: Small GPT model in code
• PyTorch internals: How PyTorch tensors and operators work inside
• JavaScript Runtime from Scratch: JavaScript runtime in C

Cloud Architecture

• AWS to Azure services comparison: AWS services and the matching Azure services
• Google Cloud to Azure services comparison: Google Cloud services and the matching Azure services
• Compare AWS and Azure services to Google Cloud: Table that maps AWS and Azure services to Google Cloud
• Microsoft Azure Developer's Cheat Sheet: Quick reference of Azure products and features
• Azure Cloud Adoption Framework :CAF: How a company plans and starts using Azure
• Azure Well-architected Framework :WAF: Rules for building and improving cloud applications
• Azure Architecture Center :AAC: Patterns and reference architectures
• Best practices in cloud applications: Caching, retries, scaling, and similar topics
• Cloud Design Patterns: Standard solutions to common cloud problems
• Landing zone: Design guide for a proven and flexible Azure architecture
• Resources for architects and developers of multitenant solutions: Multitenant SaaS design links
• Data partitioning strategies: Splitting data across storage
• Azure FinOps Guide: Cloud cost management
• Modern Web App pattern for .NET / git: Design guide with sample .NET app
• AI agent orchestration patterns: How to make several AI agents work together

Computer Science courses

• AI learning portals: Anthropic Academy, Google AI Training, Meta AI Resources, NVIDIA CUDA, Microsoft Learn, OpenAI Academy, IBM SkillsBuild, AWS Skill Builder, DeepLearning.AI, and Hugging Face Learn
• Kaggle Solutions and Ideas: Examples from Kaggle competitions
• Best-of Machine Learning with Python: Ranked Python machine learning libraries, updated weekly
• freeCodeCamp: Free coding lessons and exercises. youtube
• Ultimate Collection of 60 YouTube Courses for 21 Programming Languages: YouTube courses by language
• Computer Science courses with video lectures: University lectures grouped by topic
• Introduction to CUDA Programming for Python Developers: Using NVIDIA GPUs from Python
• AI by Hand: Work through AI calculations by hand | AI by Hand - Solutions
• Computer Networking Fundamentals: Networking lessons with online labs

Beginner Series by Microsoft

• Generative AI for Beginners: Building apps with generative AI
• ML for Beginners: Classical machine learning
• Web Dev for Beginners: HTML, CSS, and JavaScript
• AI for Beginners: Basic AI lessons
• Data Science for Beginners: Analyzing data and showing results
• IoT for Beginners: Projects with sensors, devices, and cloud
• AI Agents for Beginners: AI agent lessons
• Cybersecurity for Beginners: Basic security lessons
• Model Context Protocol (MCP) For Beginners: Connecting AI apps to tools and data
• Generative AI for Beginners using .NET: Generative AI examples in C# and .NET
• Mastering GitHub Copilot for C#/.NET Developers: Copilot examples for C# and .NET
• Choose Your Own Copilot Adventure: Coding challenges with GitHub Copilot
• XR Development for Beginners: AR, VR, and mixed reality
• Mastering GitHub Copilot for AI Paired Programming: Using Copilot to write, test, and review code

Tools & Research

##### Algorithm & Visualization

• Algorithm Visualizer: Watch algorithms run step by step
• Netron: View layers and connections of a machine learning model
• Hello Algo: Illustrated lessons on algorithms and data structures
• Wikipedia: List of Algorithms: Algorithms listed by type

• System Design Notes
• Paperdraw: System Design Simulator
• OOP Design Patterns: Examples of common object-oriented patterns
• Dev Encyclopedia: Definitions of software terms (GitHub)
• Data Engineering Wiki: Data pipelines, storage, and platforms

• Global Research Space
• Semantic Scholar: Search research papers with AI
• Liner: Highlight and annotate research papers
• Litmaps: Map links between research papers
• Connected Papers: Find related papers in a visual graph
• Ask R Discovery: Find papers by asking a question
• scite_: See how later papers cite a study
• Moonlight: Read research papers in a browser

• Excalidraw: Draw diagrams by hand
• Eraser.io: Make diagrams from text
• PlantUML: UML diagrams from text
• GraphViz: Graph diagrams from DOT files
• Mermaid Diagram: Flowcharts and sequence diagrams in Markdown

• Google Engineering Practices Documentation
• Wise Tech Stack: Platform engineering practices across Kubernetes, delivery, data, AI, and observability
• Robin Wieruch Blog: React and TypeScript tutorials
• Best Kubernetes Tools: Kubernetes tools listed by Bluelight Consulting
• PostgreSQL Wiki: Don't Do This: Common PostgreSQL mistakes
• Power BI DAX Patterns: Ready-made DAX formulas
• How To Secure A Linux Server: Steps to make a Linux server safer
• Crontab Guru: Cron expression editor

• Assembly code visualizer: See how assembly instructions run
• explorar.dev: Browse source code in an interactive view
• Idea Instructions: Ideas explained with IKEA-style pictures
• Linux Kernel Map: Map of Linux kernel parts
• Making Software: Pictures and explanations of how software works
• Understanding the Linux Kernel, Visually: Research paper on showing Linux data structures visually

• Grokpedia: Encyclopedia articles written by AI
• DeepWiki: AI-made guides to code repositories
• Google Code Wiki: AI-made guides to code

Engineering blog

• AWS Architecture Blog
• Azure Architecture Blog
• GCP Cloud Blog
• Netflix TechBlog
• Uber Blog
• The Cloudflare Blog
• Engineering at Meta
• LinkedIn Engineering
• Stripe Blog: Engineering
• Discord Blog: Engineering & Developers
• Slack Engineering
• 79 Engineering Blogs To Level Up Your System Design Skills: Links to engineering blogs from many companies

Other Topics

• Are Ideas Getting Harder to Find?: Across industries, research effort has risen sharply while research productivity has fallen, so sustaining growth needs ever more researchers.
• How to Architect Software for a Greener Future: Design choices that reduce software energy use
• Generative Search: Practical Advice for Retrieval Augmented Generation (RAG): Talk on combining search and language models
• What are the Greenest Programming Languages?: Energy use compared across programming languages
• Frugal Architect Handbook: Designing systems with cost in mind
• .NET Developer Roadmap: Learning path for .NET skills
• AI made me doubt everything about programming - Felienne Hermans, DDD Europe 2026: Code is not neutral. The real work is explaining the theory behind code, which LLMs lack. Build for people, not just programmers.
• Celebrate 50 years of Microsoft with the company's original source code: Altair Basic: Source code of Microsoft's first product, BASIC for the Altair (1975)
• A periodic table for machine learning: 🔬Visual map of machine learning methods
• Every GPU — Data Viz: Compare GPU specs and performance across generations

Transformer Architecture (LLM Foundations)

• How Top AI Labs Are Building RL Agents in 2026: RLHF, verifiable rewards, GRPO, and LLM-as-a-judge reward signals for agent training
• LLM Architecture Gallery
• Attention Is All You Need: Paper that introduced the Transformer
• Interactive Reinforcement Learning Explorer: Try how agents learn from rewards
• LLM Visualization: 3D view of how a GPT model works
• The Illustrated Transformer: Drawings that explain attention in Transformers
• Transformer Explainer: Interactive view of how a Transformer predicts text
• Why Momentum Really Works: Interactive explanation of momentum in model training

Computer Science Books

##### General

• The Pragmatic Programmer by David Thomas and Andrew Hunt: Advice on programming and work habits
• Modern Software Engineering by David Farley: Improve software through testing, feedback, and small changes
• Code Complete by Steve McConnell: Guide to writing and organizing code
• 🆓 Software Engineering at Google by Titus Winters, Tom Manshreck, and Hyrum Wright: How Google builds and maintains software | Free online edition

• Clean Code by Uncle Bob Martin: Writing clear names and functions
• Head First Design Patterns by Eric Freeman: Design patterns explained with pictures and examples
• Refactoring by Martin Fowler: Change code structure without changing its behavior
• Design Patterns by Eric Gamma and Others: Common solutions to object-oriented design problems

• Grokking Algorithms by Aditya Bhargava: Algorithms explained with pictures
• Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein: Detailed guide to algorithms and how they work
• Cracking the Coding Interview by Gayle Laakmann McDowell: Coding interview questions and practice

• Designing Data-Intensive Applications by Martin Kleppman: How to store, copy, and process data across systems
• Learning SQL by Alan Beaulieu: Basics of querying relational databases

• Growing Object-Oriented Software, Guided by Tests by Steve Freeman: Use tests to shape an application
• Unit Testing Principles, Practices, and Patterns by Vladimir Khorikov: How to write useful unit tests
• The Art of Unit Testing by Roy Osherove: Write and maintain unit tests
• TDD by Example by Kent Beck: Test-driven development with examples

• Fundamentals Of Software Architecture by Mark Richards and Neil Ford: Architecture styles and their trade-offs
• Clean Architecture by Uncle Bob Martin: Separating business rules from tools and frameworks
• Software Architecture: The Hard Parts by Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani: Difficult choices in distributed systems
• 🆓 Domain-Driven Design Quickly by Abel Avram and Floyd Marinescu: Short introduction to domain-driven design | Free ebook
• A Philosophy of Software Design by John Ousterhout: Ways to make software easier to understand
• System Design Interview by Alex Xu: System design interview examples
• Domain-Driven Design by Eric Evans: Design software around business needs

• Understanding Distributed Systems by Roberto Vitillo: How distributed systems work and fail
• Designing Distributed Systems by Brendan Burns: Patterns for container-based systems

• DevOps Handbook by Gene Kim, Patrick Debois, John Willis, and Jez Humble: Practices for teamwork and software delivery
• Continuous Delivery by Jez Humble and David Farley: Automate builds, tests, and releases
• Accelerate by Nicole Forsgren, Jez Humble, and Gene Kim: Research on software team performance

• The Hundred-Page Machine Learning Book by Andriy Burkov: Short introduction to machine learning
• Designing Machine Learning Systems by Chip Huyen: Building and running machine learning systems
• AI Engineering by Chip Huyen / git: Building applications with large language models

• Algorithms + Data Structures = Programs by Niklaus Wirth: Classic showing that program design is inseparable from the choice of data structures and algorithms
• 🆓 Computer Networking: A Top-Down Approach: Networking taught from the application layer down to the physical layer
• 🆓 Crafting Interpreters: Build two complete interpreters for a scripting language, from scratch
• 🆓 Deep Learning: Goodfellow, Bengio, Courville's textbook covering math basics through modern deep networks
• Fundamentals of Software Engineering by Nathaniel Schutta, Dan Vega: Practical overview of the core skills and practices of a software engineer
• 🆓 Game Programming Patterns: Design patterns applied to game code, with a focus on performance and decoupling
• 🆓 Immersive Linear Algebra: Interactive, visual introduction to linear algebra
• 🆓 Operating Systems: Three Easy Pieces: OS concepts organized around virtualization, concurrency, and persistence
• 🆓 Physically Based Rendering: Literate-programming guide to building a physically based ray tracer
• 🆓 Speech and Language Processing: Jurafsky & Martin's NLP textbook, from basics to LLMs
• Thinking in Systems by Donella Meadows, Diana Wright: Primer on stocks, flows, feedback loops, and finding leverage points in complex systems

Computer Science Papers

Computer Science Papers Every Developer Should Read

• 📖Downloaded PDFs - 12 files | Study notes
• On the Criteria To Be Used in Decomposing Systems into Modules (1972): D.L. Parnas – keep each module's inner workings hidden
• An Axiomatic Basis for Computer Programming (1969): C.A.R. Hoare – ways to prove a program is correct
• Time, Clocks, and the Ordering of Events in a Distributed System (1978): L. Lamport – track which event happened before another
• Out of the Tar Pit (2006): B. Moseley, P. Marks – how changing state makes software harder to manage
• Dynamo: Amazon’s Highly Available Key-value Store (2007): G. DeCandia et al. – a key-value store that keeps working during failures
• MapReduce: Simplified Data Processing on Large Clusters (2004): J. Dean, S. Ghemawat – process large data in parallel
• A Note On Distributed Computing (1994): J. Waldo et al. – why network calls differ from local calls
• A Metrics Suite for Object-Oriented Design (1994): S.R. Chidamber – measures for object-oriented design
• A Relational Model of Data for Large Shared Data Banks (1969): E.F. Codd – the relational way to organize data
• Why Functional Programming Matters (1990): J. Hughes – build programs from small functions you can reuse
• Transmission Control Protocol (1981): J. Postel | A TCP/IP Tutorial (1991): Introductory guide to TCP/IP

70+ Distributed Systems papers

• 📖Downloaded PDFS - 72 files | Study notes
• Here's a reading list of 70+ Distributed Systems papers mostly from conferences in just last 2 years! 70+ Distributed Systems papers [Jan 2024]

25 Papers That Completely Transformed the Computer World

• 📖Downloaded PDFs - 25 files | Study notes
• Dynamo: Key-value store that stays available during failures
• Google File System: Storing large files on many low-cost computers
• Scaling Memcached at Facebook: How Facebook runs a large cache
• BigTable: Google's system for storing large tables
• Borg: How Google schedules work across its computers
• Cassandra: NoSQL database that stores data across many computers
• Attention Is All You Need: The Transformer model for language tasks
• Kafka: Storing and sending streams of events
• FoundationDB: Distributed database that keeps data correct during updates
• Amazon Aurora: Cloud database that keeps storage and compute apart
• Spanner: Google's database that keeps data the same in many regions
• MapReduce: Processing large data sets in parallel
• Shard Manager: Meta's system for assigning and moving data shards
• Dapper: How Google traces requests across services
• Flink: One engine for live and stored data
• A Comprehensive Survey on Vector Databases: Overview of vector database designs and search methods
• Zanzibar: Google's global system for access permissions
• Monarch: Google's in-memory time series database
• Thrift: Generate code for services in different languages
• Bitcoin: Digital money sent directly between users
• WTF - Who to Follow Service at Twitter: How Twitter (now X) recommends people to follow
• MyRocks: MySQL storage engine built on LSM-Tree
• GoTo Considered Harmful: Dijkstra's case for structured code over goto
• Raft Consensus Algorithm: How computers agree on one value; easier than Paxos
• Time Clocks and Ordering of Events: Ordering events across computers without a shared clock

The best NLP papers from 2015 to now

• 📖Downloaded PDFS - 100 files | Study notes
• The best NLP papers: 🔬List of important NLP papers

Data Science (ML/NN)

• Awesome_Math_Books: List of math books
• Free eBooks for ML, Data Science & AI: Newsletter list of 30 free ML books
• ML and LLM system design: Database of 800 case studies from 150+ companies

eBooks for ML, Data Science & AI

🆓 indicates a free full-text online edition, ebook, or author-provided preprint, not necessarily a free print edition. Books offered on a "read-first, buy-later" basis are not marked.

##### Machine Learning & Deep Learning

• 🆓 Deep Learning – Ian Goodfellow, Yoshua Bengio, Aaron Courville
• 🆓 Dive into Deep Learning – Aston Zhang et al.
• The Hundred-Page Machine Learning Book – Andriy Burkov
• 🆓 Machine Learning Yearning – Andrew Ng
• 🆓 Understanding Machine Learning – Shai Shalev-Shwartz, Shai Ben-David
• Machine Learning for Humans – Vishal Maini, Samer Sabri
• 🆓 Approaching (Almost) Any ML Problem – Abhishek Thakur
• 🆓 Machine Learning For Dummies – Judith Hurwitz, Daniel Kirsch
• 🆓 Hands-On Machine Learning with R – Boehmke & Greenwell
• Machine Learning Engineering – Andriy Burkov
• 🆓 Machine Learning Systems: Introduction to Machine Learning Systems – Vijay Janapa Reddi

• 🆓 Mathematics for Machine Learning – Deisenroth, Faisal, Ong
• 🆓 The Elements of Statistical Learning – Friedman, Tibshirani, Hastie
• 🆓 An Introduction to Statistical Learning – James et al.
• 🆓 Pattern Recognition and ML – Christopher Bishop
• 🆓 Information Theory, Inference, and Learning Algorithms – David J. C. MacKay
• 🆓 Algebra, Topology, Calculus & Optimization for CS/ML – Jean Gallier
• 🆓 Mathematical Methods for CV, Robotics, Graphics – Stanford
• 🆓 Math Foundations for Computer Science – Stanford CS103
• 🆓 @mathtalent Lecture Notes – Math-focused CS notes
• 🆓 Algorithms for Artificial Intelligence – Moss
• 🆓 Calculus for Mathematicians, Computer Scientists, and Physicists – Andrew D. Hwang

• 🆓 Probabilistic ML: An Introduction – Kevin P. Murphy
• 🆓 Probabilistic ML: Advanced Topics – Kevin P. Murphy
• 🆓 Applied Causal Inference – Uday Kamath et al.
• 🆓 Reinforcement Learning: An Introduction – Sutton & Barto
• 🆓 Deep Learning on Graphs – Yao Ma & Jiliang Tang
• 🆓 Natural Language Processing with Python – Bird, Klein, Loper: NLP with the NLTK library
• 🆓 Computer Vision: Models, Learning, and Inference – Simon J.D. Prince: Probabilistic approach to computer vision
• 🆓 Interpretable Machine Learning – Christoph Molnar: Explaining model predictions
• ML Interpretability – Patrick Hall & Navdeep Gill: Short primer on explainability
• 🆓 Automated Machine Learning – Frank Hutter et al.: Automating model selection and tuning
• 🆓 Feature Engineering and Selection – Max Kuhn & Kjell Johnson: Improving model inputs
• 🆓 Deep Learning Interviews – Shlomo Kashani, Amir Ivry: Interview questions
• 🆓 Boosting: Foundations and Algorithms – Schapire & Freund: Theory of boosting methods
• 🆓 A Brief Introduction to ML for Engineers – Osvaldo Simeone: Short ML overview for engineers

Github for ML, Data Science & AI

##### Foundational Learning

• 30u30: Master Ilya's 30 Foundational AI Papers in 30 Days
• Machine Learning for Beginners – Microsoft: 12-week classical ML course
• The Data Engineering Handbook: Data engineering resources and learning path
• Virgilio – Data Science Curriculum: Study plan with linked learning resources
• Open Source Data Science Masters: Self-study plan similar to a master's degree
• 🆓 Python Data Science Handbook: Jupyter notebooks for NumPy, Pandas, Matplotlib, and scikit-learn
• Data Science Python Notebooks: Notebooks on deep learning, Spark, and big data tools
• Awesome Data Science: Learning resources and career paths
• Awesome Machine Learning: Machine learning libraries by language
• Interactive Educational Data Science Python Dashboards: Interactive demos of data science concepts

• Deep Learning Book (MIT): PDF of the Goodfellow, Bengio, and Courville textbook
• 🆓 fastai Book (fastbook) | Fast.ai courses: Notebook-based deep learning course, code first
• 🆓 Mathematics for Machine Learning: Book website and materials
• labml.ai – Deep Learning Paper Implementations: Paper implementations with side-by-side notes
• Deep Learning Models by Rasbt: Model implementations in PyTorch and TensorFlow notebooks

• Machine Learning Tutorials: Tutorials and articles by topic
• Machine Learning ZoomCamp: Code for the ML Bookcamp book and course
• Applied ML – Papers & Blogs: How companies use ML in production
• Awesome Production Machine Learning: Tools for deploying and monitoring ML
• Data Science Project Template (Cookiecutter): Project template with a ready folder structure
• 365 Data Science Flashcards: Flashcards for data science terms
• openpilot – Driver Assistance System: Open-source driver assistance software for cars

• CS 229 ML Cheatsheets: Stanford CS 229 summary sheets
• ML Interview Guide: Interview preparation topics
• Data Science Interview Q\&A: Interview questions and answers

• StatQuest by Josh Starmer: Statistics and ML videos
• Machine Learning Mastery by Jason Brownlee: ML tutorials with code examples
• Papers With Code: 🔬Papers linked to code and benchmarks

Terminology and Comparisons

• See Glossary.md: an overview of key terminology, definitions, and comparisons between related concepts.

Citation

• bytebytego | techworld-with-milan | mhadidg (github) | milan_milanovic