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Software Architect Mindmap

Name: Software Architect Mindmap
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🧠Mindmap of 🗺️Software Architecture, Software engineering: An Overview of Software Terminologies and Concepts.

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Software Architecture Mindmap

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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.

Section 1: Software Architecture Mindmap
Section 2: Software Architecture Reference
Section 3: Computer Science 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

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

Beginner Series by Microsoft

Industry Trends

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

Substack Leaderboard: Newsletter
daily.dev: Personalized news feed

Tools & Research

Algorithm & Visualization

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

Design Patterns & Development

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

Research & Academic Tools

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

Diagramming & Visualization Tools

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

Best Practices

Robin Wieruch Blog: React.js, Next.js, TypeScript, JavaScript libraries, and trends
Best Kubernetes Tools: Curated Kubernetes tools by Bluelight Consulting
PostgreSQL Wiki: Don't Do This: Common PostgreSQL pitfalls
Power BI DAX Patterns: Patterns and best practices for Power BI DAX
How To Secure A Linux Server
Crontab Guru: Interactive cron schedule expression editor and reference

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

LLM-generated wiki

Engineering blog

Transformer Architecture (LLM Foundations)

Computer Science Books

General

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

Good Practices

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

Data Structures and Algorithms

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

Data

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

Testing

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

Software Architecture

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

Distributed Systems

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

DevOps

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

Machine Learning

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

Computer Science Papers

Computer Science Papers Every Developer Should Read

On the Criteria To Be Used in Decomposing Systems into Modules (1972): D.L. Parnas
An Axiomatic Basis for Computer Programming (1969): C.A.R. Hoare
Time, Clocks, and the Ordering of Events in a Distributed System (1978): L. Lamport
Out of the Tar Pit (2006): B. Moseley, P. Marks
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
A Note On Distributed Computing (1994): J. Waldo, G. Wyant, A. Wollrath, S. Kendall
A Metrics Suite for Object-Oriented Design (1994): S.R. Chidamber
A Relational Model of Data for Large Shared Data Banks (1969): E.F. Codd
Why Functional Programming Matters (1990): J. Hughes 1- Transmission Control Protocol (1981): J. Postel | A TCP/IP Tutorial (1991): a tutorial on the TCP/IP protocol

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]

25 Papers That Completely Transformed the Computer World

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

The best NLP papers from 2015 to now

The best NLP papers

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

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 & Statistical Foundations

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, Special Topics

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
Speech and Language Processing – Jurafsky & Martin
Natural Language Processing with Python – Bird, Klein, Loper
Computer Vision: Models, Learning, and Inference – Simon J.D. Prince
Interpretable Machine Learning – Christoph Molnar
ML Interpretability – Patrick Hall & Navdeep Gill
Automated Machine Learning – Frank Hutter et al.
Feature Engineering and Selection – Max Kuhn & Kjell Johnson
Deep Learning Interviews – Shlomo Kashani, Amir Ivry
Boosting: Foundations and Algorithms – Schapire & Freund
A Brief Introduction to ML for Engineers – Osvaldo Simeone

Github for ML, Data Science & AI

Foundational Learning

Deep Learning & Math

Practical Skills & Production

Interviews & Cheatsheets

Machine Learning Essentials

Terminology and Comparisons

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

Citation

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

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