Software Architecture Mindmap

• This repository was initially created for the mind map; currently, it is used as a curated collection of software architecture references.
• This mind map is based on the Fresco Play curriculum (the internal learning platform of Tata Consultancy Services). © 2022

Contents

• Section 1: Software Architecture Mindmap
• Section 2: Software Architecture Reference
  • Industry Trends
  • Trend Reports
  • Newsletter
  • Cloud Architecture
  • Engineering blog
  • Building from scratch
  • Computer Science courses
  • Beginner Series by Microsoft
  • Mini-Books
  • Tools & Research
    • Algorithm & Visualization
    • Best Practices
    • Design Patterns & Development
    • Diagramming & Visualization Tools
    • LLM-generated wiki
    • Research & Academic Tools
    • Transformer Architecture (LLM Foundations)
    • Visual Learning
  • Other Topics
  • Computer Science Books
• Section 3: Computer Science Papers
  • Computer Science Papers Every Developer Should Read
  • 25 Papers That Completely Transformed the Computer World
  • The best NLP papers from 2015 to now
  • 70+ Distributed Systems papers
• Section 4: Data Science (ML/NN)
  • eBooks for ML, Data Science & AI
  • Github for ML, Data Science & AI
• Section 5: Terminology and Comparisons

Software terminologies and concepts, software architecture overview

Software_Architecture_Mindmap.png: Application development, Cloud Computing, and Data science.

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The mindmap can be opened and edited using mindmap-maker-clean. You can also use it to convert the JSON source into a high-resolution PNG. Verified on Chrome and Firefox.

Software Architecture Reference

• Awesome Engineering Articles: Curated high-quality engineering articles from top tech companies
• Awesome Lists: 🗂️Awesome lists about all kinds of interesting topics / awesome.re / github topic - awesome
• Awesome Low Level Design (LLD) / Object Oriented Design (OOD)
• Awesome Software Architecture: A curated list of awesome articles, videos, and other resources to learn and practice software architecture, patterns, and principles
• Awesome Software Architecture (simskij)
• Awesome System Design Resources
• Ecosyste.ms: Awesome: An open API service indexing awesome lists
• Microsoft Learn: 🪟Microsoft documentation, tutorials, and code samples
• Microsoft .NET Application Architecture - Reference Apps
• Software Architecture Books: A comprehensive list of books on Software Architecture
• Software Architecture Books
• System Design: Learn how to design systems at scale and prepare for system design interviews
• System Design - Neo Kim
• System Design 101: 🗺️ByteByteGo
• System Design Fight Club

Industry Trends

• Gartner Top Strategic Technology Trends 2024
• Google DORA: DevOps Research and Assessment (DORA)
• Hype Cycle for Emerging Technologies 2024
• MAD (ML/AI/Data) Landscape
• Microsoft Digital Defense Report
• Postman State of the API Report
• Software Industry Statistics: Statista Industry Insight
• Stanford AI Index Annual Report

Trend Reports

• Dzone: RefCards and Trend Reports
• InfoQ: News and Articles
• InfoQ Trends Reports: 📖(Downloaded PDFs 2019 and 2021 - 2025)
• Thoughtworks: Technology Radar 📖(Downloaded PDFs vo.1 - vol.34)
• Trendshift: GitHub Trending repositories

Newsletter

• daily.dev: Personalized news feed
• Hacker News
• Substack Leaderboard: Newsletter

Cloud Architecture

• AI agent orchestration patterns
• AWS to Azure services comparison
• Azure Architecture Center :AAC: architecture patterns and reference architectures
• Azure Cloud Adoption Framework :CAF: organization-wide adoption guidance
• Azure FinOps Guide
• Azure Well-architected Framework :WAF: workload-focussed design and continuous improvement guidance
• Best practices in cloud applications: Azure application architecture
• Cloud Design Patterns
• Compare AWS and Azure services to Google Cloud
• Data partitioning strategies
• Google Cloud to Azure services comparison
• Landing zone
• Microsoft Azure Developer's Cheat Sheet: Every product, feature and service in the Azure family
• Modern Web App pattern for .NET / GitHub
• Resources for architects and developers of multitenant solutions

Engineering blog

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

Building from scratch

• Build frontend applications at scale
• Clone Wars: 100+ open-source clones of popular sites
• Curated list of project-based tutorials
• JavaScript Runtime from Scratch: C programming
• Let's Build a Simple Database: Writing a sqlite clone from scratch in C
• Let’s Build A Simple Interpreter
• Let’s Build A Web Server
• Master programming by recreating your favorite technologies from scratch
• minimal GPT
• PyTorch internals
• Web Browser Engineering: Building a basic but complete web browser from scratch
• Writing an Operating System in 1,000 Lines

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
• AI by Hand
• Best-of Machine Learning with Python: A ranked list of awesome machine learning Python libraries. Updated weekly.
• Computer Networking Fundamentals For Developers, DevOps, and Platform Engineers
• Computer Science courses with video lectures
• freeCodeCamp: Learn to code for free. youtube
• Introduction to CUDA Programming for Python Developers
• Kaggle Solutions and Ideas: Collection of Kaggle Solutions and Ideas
• Ultimate Collection of 60 YouTube Courses for 21 Programming Languages

Beginner Series by Microsoft

• AI Agents for Beginners
• AI for Beginners
• Choose Your Own Copilot Adventure
• Cybersecurity for Beginners
• Data Science for Beginners
• Generative AI for Beginners
• Generative AI for Beginners using .NET
• IoT for Beginners
• Mastering GitHub Copilot for AI Paired Programming
• Mastering GitHub Copilot for C#/.NET Developers
• ML for Beginners
• Model Context Protocol (MCP) For Beginners
• Web Dev for Beginners
• XR Development for Beginners

Mini-Books

• Agentic Architectures for Retrieval-intensive Applications
• Building a scalable authorization system
• Google SRE Handbook
• InfoQ Architectures You've Always Wondered About: 📖(Downloaded PDFs 2021 - 2025)
• InfoQ Cell-Based Architecture
• Mastering AI Agents
• Mastering RAG
• Programming Notes for Professionals books
• System Design Blueprint: The Ultimate Guide

Tools & Research

Algorithm & Visualization

• Algorithm Visualizer: Interactive algorithm visualization
• Hello Algo: Algorithm tutorials
• Netron: Neural network visualizer
• Wikipedia: List of Algorithms: Comprehensive algorithm reference

Best Practices

• Best Kubernetes Tools: Curated Kubernetes tools by Bluelight Consulting
• Crontab Guru: Interactive cron schedule expression editor and reference
• Google Engineering Practices Documentation
• How To Secure A Linux Server
• PostgreSQL Wiki: Don't Do This: Common PostgreSQL pitfalls
• Power BI DAX Patterns: Patterns and best practices for Power BI DAX
• Robin Wieruch Blog: React.js, Next.js, TypeScript, JavaScript libraries, and trends
• Wise Tech Stack: Platform engineering practices across Kubernetes, delivery, data, AI, and observability

Design Patterns & Development

• Data Engineering Wiki: Data engineering resources and guides
• Dev Encyclopedia: Encyclopedia for developers (GitHub)
• OOP Design Patterns: Object-oriented design patterns explained
• Paperdraw: System Design Simulator

Diagramming & Visualization Tools

• Excalidraw: Hand-drawn style diagrams
• Eraser.io: Diagramming as code
• GraphViz: Graph visualization
• Mermaid Diagram: Diagramming and charting tool
• PlantUML: UML diagrams

LLM-generated wiki

• DeepWiki
• Google Code Wiki
• Grokpedia

Research & Academic Tools

• Ask R Discovery: AI-powered paper discovery tool
• Connected Papers: Explore connected academic papers
• Liner: AI research tool for highlighting and annotating
• Litmaps: Visualize relationships between research papers
• Moonlight: Browser extension for academic research
• scite_: Smart citation analysis and discovery
• Semantic Scholar: AI-powered academic search engine

Transformer Architecture (LLM Foundations)

• Attention Is All You Need: Transformer architecture based on self-attention
• How Top AI Labs Are Building RL Agents in 2026: RLHF, verifiable rewards, GRPO, and LLM-as-a-judge reward signals for agent training
• Interactive Reinforcement Learning Explorer
• LLM Architecture Gallery
• LLM Visualization
• The Illustrated Transformer
• Transformer Explainer
• Why Momentum Really Works

Visual Learning

• Assembly code visualizer: Visualize Your Assembly Code
• explorar.dev: Explore curated open-source repositories with an interactive code browser.
• Idea Instructions: Complex concepts explained as IKEA-style visual instructions
• Linux Kernel Map: Interactive visual map of the Linux kernel subsystems
• Making Software: Visual explanations of how software works under the hood
• Understanding the Linux Kernel, Visually

Other Topics

• .NET Developer Roadmap
• A periodic table for machine learning:🔬A visual guide to machine learning concepts and techniques
• Celebrate 50 years of Microsoft with the company's original source code: Altair Basic
• Every GPU — Data Viz: Visual comparison of GPU specs and performance across generations
• Frugal Architect Handbook
• Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
• How to Architect Software for a Greener Future
• What are the Greenest Programming Languages?

Computer Science Books

General

• Code Complete by Steve McConnell
• Modern Software Engineering by David Farley
• Software Engineering at Google by Titus Winters, Tom Manshreck, and Hyrum Wright
• The Pragmatic Programmer by David Thomas and Andrew Hunt

Good Practices

• Clean Code by Uncle Bob Martin
• Design Patterns by Eric Gamma and Others
• Head First Design Patterns by Eric Freeman
• Refactoring by Martin Fowler

Data Structures and Algorithms

• Cracking the Coding Interview by Gayle Laakmann McDowell
• Grokking Algorithms by Aditya Bhargava
• Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Data

• Designing Data-Intensive Applications by Martin Kleppman
• Learning SQL by Alan Beaulieu

Testing

• Growing Object-Oriented Software, Guided by Tests by Steve Freeman
• The Art of Unit Testing by Roy Osherove
• TDD by Example by Kent Beck
• Unit Testing Principles, Practices, and Patterns by Vladimir Khorikov

Software Architecture

• A Philosophy of Software Design by John Ousterhout
• Clean Architecture by Uncle Bob Martin
• Domain-Driven Design by Eric Evans
• Domain-Driven Design Quickly by Abel Avram and Floyd Marinescu
• Fundamentals Of Software Architecture by Mark Richards and Neil Ford
• Software Architecture: The Hard Parts by Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani
• System Design Interview by Alex Xu

Distributed Systems

• Designing Distributed Systems by Brendan Burns
• Understanding Distributed Systems by Roberto Vitillo

DevOps

• Accelerate by Nicole Forsgren, Jez Humble, and Gene Kim
• Continuous Delivery by Jez Humble and David Farley
• DevOps Handbook by Gene Kim, Patrick Debois, John Willis, and Jez Humble

Machine Learning

• AI Engineering by Chip Huyen / GitHub
• Designing Machine Learning Systems by Chip Huyen
• The Hundred-Page Machine Learning Book by Andriy Burkov

Computer Science Papers

Computer Science Papers Every Developer Should Read

• 📖Downloaded PDFs - 12 files | Study notes
• An Axiomatic Basis for Computer Programming (1969): C.A.R. Hoare
• A Metrics Suite for Object-Oriented Design (1994): S.R. Chidamber
• A Note On Distributed Computing (1994): J. Waldo, G. Wyant, A. Wollrath, S. Kendall
• A Relational Model of Data for Large Shared Data Banks (1969): E.F. Codd
• Dynamo: Amazon’s Highly Available Key-value Store (2007): G. DeCandia et al.
• MapReduce: Simplified Data Processing on Large Clusters (2004): J. Dean, S. Ghemawat
• On the Criteria To Be Used in Decomposing Systems into Modules (1972): D.L. Parnas
• Out of the Tar Pit (2006): B. Moseley, P. Marks
• Time, Clocks, and the Ordering of Events in a Distributed System (1978): L. Lamport
• Transmission Control Protocol (1981): J. Postel | A TCP/IP Tutorial (1991): a tutorial on the TCP/IP protocol
• Why Functional Programming Matters (1990): J. Hughes

25 Papers That Completely Transformed the Computer World

• 📖Downloaded PDFs - 25 files | Study notes
• Amazon Aurora: How Amazon provides high availability and performance
• Attention Is All You Need: Into a new deep learning architecture known as the transformer
• BigTable: The design principles behind a distributed storage system
• Bitcoin: The ground-breaking introduction to the peer-to-peer electronic cash system
• Borg: Large Scale Cluster Management at Google
• Cassandra: A look at the design and architecture of a distributed NoSQL database
• Dapper: Insights into Google’s distributed systems tracing infrastructure
• Dynamo: Amazon’s Highly Available Key Value Store
• Flink: A detailed look at the unified architecture of stream and batch processing
• FoundationDB: A look at how a distributed database works
• Google File System: Insights into a highly scalable file system
• GoTo Considered Harmful
• Kafka: Internals of the distributed messaging platform
• MapReduce: A detailed look at how MapReduce enables parallel processing of massive volumes of data
• Monarch: Architecture of Google’s in-memory time series database
• MyRocks: LSM-Tree Database Storage Engine
• Raft Consensus Algorithm: Learn about the more understandable consensus algorithm
• Scaling Memcached at Facebook: A look at the complexities of caching
• Shard Manager: Understanding the generic shard management framework
• Spanner: Design and architecture of Google’s globally distributed database
• Thrift: Explore the design choices behind Facebook’s code-generation tool
• Time Clocks and Ordering of Events: The extremely important paper that explains the concept of time and event ordering in a distributed system
• WTF - Who to Follow Service at Twitter: Twitter’s (now X) user recommendation system
• Zanzibar: A look at the design, implementation, and deployment of a global system for managing access control lists at Google

The best NLP papers from 2015 to now

• The best NLP papers: 🔬A curated collection of influential NLP papers
• 📖Downloaded PDFS - 100 files | Study notes

70+ Distributed Systems papers

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

Data Science (ML/NN)

• Awesome Math Books
• Free eBooks for ML, Data Science & AI
• ML and LLM system design: A database of 800 case studies from 150+ companies.

eBooks for ML, Data Science & AI

Machine Learning & Deep Learning

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

Mathematics & Statistical Foundations

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

Probabilistic, Special Topics

• A Brief Introduction to ML for Engineers – Osvaldo Simeone
• Applied Causal Inference – Uday Kamath et al.
• Automated Machine Learning – Frank Hutter et al.
• Boosting: Foundations and Algorithms – Schapire & Freund
• Computer Vision: Models, Learning, and Inference – Simon J.D. Prince
• Deep Learning on Graphs – Yao Ma & Jiliang Tang
• Deep Learning Interviews – Shlomo Kashani, Amir Ivry
• Feature Engineering and Selection – Max Kuhn & Kjell Johnson
• Interpretable Machine Learning – Christoph Molnar
• ML Interpretability – Patrick Hall & Navdeep Gill
• Natural Language Processing with Python – Bird, Klein, Loper
• Probabilistic ML: Advanced Topics – Kevin P. Murphy
• Probabilistic ML: An Introduction – Kevin P. Murphy
• Reinforcement Learning: An Introduction – Sutton & Barto
• Speech and Language Processing – Jurafsky & Martin

Github for ML, Data Science & AI

Foundational Learning

• Awesome Data Science
• Awesome Machine Learning
• Data Science Python Notebooks
• Interactive Educational Data Science Python Dashboards
• Machine Learning for Beginners – Microsoft
• Open Source Data Science Masters
• Python Data Science Handbook
• The Data Engineering Handbook
• Virgilio – Data Science Curriculum

Deep Learning & Math

• Deep Learning Book (MIT)
• Deep Learning Models by Rasbt
• fastai Book (fastbook) | Fast.ai courses
• labml.ai – Deep Learning Paper Implementations
• Mathematics for Machine Learning

Practical Skills & Production

• 365 Data Science Flashcards
• Applied ML – Papers & Blogs
• Awesome Production Machine Learning
• Data Science Project Template (Cookiecutter)
• Machine Learning Tutorials
• Machine Learning ZoomCamp
• openpilot – Driver Assistance System

Interviews & Cheatsheets

• CS 229 ML Cheatsheets
• Data Science Interview Q&A
• ML Interview Guide

Machine Learning Essentials

• Machine Learning Mastery by Jason Brownlee
• Papers With Code:🔬Research papers with code, datasets, and benchmarks
• StatQuest by Josh Starmer

Terminology and Comparisons

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

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