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Bibliography

This bibliography contains all references cited throughout the book, organized by category for easy reference.

Software Architecture & Design​

Books:

  1. Newman, S. (2021). Building Microservices: Designing Fine-Grained Systems (2nd ed.). O'Reilly Media. ISBN: 978-1492034025.

  2. Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O'Reilly Media. ISBN: 978-1449373320.

  3. Evans, E. (2003). Domain-Driven Design: Tacking Complexity in the Heart of Software. Addison-Wesley Professional. ISBN: 978-0321125215.

  4. Fowler, M. (2018). Refactoring: Improving the Design of Existing Code (2nd ed.). Addison-Wesley Professional. ISBN: 978-0134757599.

  5. Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional. ISBN: 978-0201633610.

Articles:

  1. Fowler, M. (2014). Microservices. Retrieved from https://martinfowler.com/articles/microservices.html

  2. Richardson, C. (2018). Pattern: Event-driven architecture. Microservices.io. Retrieved from https://microservices.io/patterns/data/event-driven-architecture.html

Production Engineering & Site Reliability​

Books:

  1. Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016). Site Reliability Engineering: How Google Runs Production Systems. O'Reilly Media. ISBN: 978-1491929124.

  2. Beyer, B., Murphy, N. R., Rensin, D. K., Kawahara, K., & Thorne, S. (2018). The Site Reliability Workbook: Practical Ways to Implement SRE. O'Reilly Media. ISBN: 978-1492029502.

  3. Nygard, M. T. (2018). Release It!: Design and Deploy Production-Ready Software (2nd ed.). Pragmatic Bookshelf. ISBN: 978-1680502398.

  4. Majors, C., Fong-Jones, L., & Miranda, G. (2022). Observability Engineering: Achieving Production Excellence. O'Reilly Media. ISBN: 978-1492076445.

Articles:

  1. Basiri, A., Behnam, N., de Rooij, R., Hochstein, L., Kosewski, L., Reynolds, J., & Rosenthal, C. (2016). Chaos engineering. IEEE Software, 33(3), 35-41. DOI: 10.1109/MS.2016.60

  2. Hochstein, L. (2020). Circuit Breaker pattern. Netflix TechBlog. Retrieved from https://netflixtechblog.com/

DevOps & CI/CD​

Books:

  1. Kim, G., Humble, J., Debois, P., & Willis, J. (2016). The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations. IT Revolution Press. ISBN: 978-1942788003.

  2. Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press. ISBN: 978-1942788331.

  3. Morris, K. (2020). Infrastructure as Code: Dynamic Systems for the Cloud Age (2nd ed.). O'Reilly Media. ISBN: 978-1098114671.

Articles:

  1. Fowler, M., & Foemmel, M. (2006). Continuous Integration. Retrieved from https://martinfowler.com/articles/continuousIntegration.html

Testing & Quality Assurance​

Books:

  1. Beck, K. (2002). Test Driven Development: By Example. Addison-Wesley Professional. ISBN: 978-0321146533.

  2. Freeman, S., & Pryce, N. (2009). Growing Object-Oriented Software, Guided by Tests. Addison-Wesley Professional. ISBN: 978-0321503626.

Articles:

  1. Fowler, M. (2012). Test Pyramid. Retrieved from https://martinfowler.com/bliki/TestPyramid.html

  2. Fowler, M. (2014). Mocks Aren't Stubs. Retrieved from https://martinfowler.com/articles/mocksArentStubs.html

Artificial Intelligence & Machine Learning​

Books:

  1. Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. ISBN: 978-1098125974.

  2. Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications. ISBN: 978-1617296864.

  3. Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural Language Processing with Transformers. O'Reilly Media. ISBN: 978-1098103248.

Papers:

  1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. arXiv:1706.03762

  2. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. arXiv:2005.14165

  3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Retrieval-Augmented Generation (RAG)​

Papers:

  1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33, 9459-9474. arXiv:2005.11401

  2. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., ... & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.

  3. Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282.

  4. Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., ... & Zettlemoyer, L. (2023). REPLUG: Retrieval-augmented black-box language models. arXiv preprint arXiv:2301.12652.

Vector Databases & Embeddings​

Papers:

  1. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using siamese BERT-networks. arXiv preprint arXiv:1908.10084.

  2. Johnson, J., Douze, M., & Jégou, H. (2019). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535-547. arXiv:1702.08734

  3. Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence, 42(4), 824-836. arXiv:1603.09320

Documentation:

  1. Qdrant Team. (2023). Qdrant Vector Database Documentation. Retrieved from https://qdrant.tech/documentation/

  2. Facebook AI Research. (2023). FAISS: A library for efficient similarity search. Retrieved from https://github.com/facebookresearch/faiss

Knowledge Graphs & Graph Databases​

Books:

  1. Robinson, I., Webber, J., & Eifrem, E. (2015). Graph Databases (2nd ed.). O'Reilly Media. ISBN: 978-1491930892.

  2. Needham, M., & Hodler, A. E. (2019). Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O'Reilly Media. ISBN: 978-1492047681.

Papers:

  1. Angles, R., & Gutierrez, C. (2008). Survey of graph database models. ACM Computing Surveys (CSUR), 40(1), 1-39. DOI: 10.1145/1322432.1322433

Documentation:

  1. Neo4j Inc. (2023). Neo4j Graph Database Documentation. Retrieved from https://neo4j.com/docs/

  2. NetworkX Developers. (2023). NetworkX Documentation. Retrieved from https://networkx.org/documentation/

Large Language Models & Prompt Engineering​

Papers:

  1. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837. arXiv:2201.11903

  2. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., ... & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.

  3. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910.

  4. Chen, L., Zaharia, M., & Zou, J. (2023). FrugalGPT: How to use large language models while reducing cost and improving performance. arXiv preprint arXiv:2305.05176.

Multi-Agent Systems​

Papers:

  1. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., ... & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155.

  2. Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., ... & Zhou, J. (2023). MetaGPT: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352.

  3. Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442.

Documentation:

  1. LangChain AI. (2023). LangGraph Documentation. Retrieved from https://python.langchain.com/docs/langgraph

  2. LlamaIndex Team. (2023). LlamaIndex Documentation. Retrieved from https://docs.llamaindex.ai/

Event-Driven Architecture & Message Streaming​

Books:

  1. Stopford, B. (2018). Designing Event-Driven Systems. O'Reilly Media. ISBN: 978-1492038221.

  2. Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: The Definitive Guide. O'Reilly Media. ISBN: 978-1491936160.

Documentation:

  1. Apache Software Foundation. (2023). Apache Kafka Documentation. Retrieved from https://kafka.apache.org/documentation/

  2. Apache Software Foundation. (2023). Apache Airflow Documentation. Retrieved from https://airflow.apache.org/docs/

Frontend Development​

Books:

  1. Banks, A., & Porcello, E. (2020). Learning React (2nd ed.). O'Reilly Media. ISBN: 978-1492051725.

  2. Larsen, R., & Orlikowski, M. (2022). Full Stack React, TypeScript, and Node. Packt Publishing. ISBN: 978-1801073707.

Articles:

  1. Frost, B. (2016). Atomic Design. Retrieved from https://atomicdesign.bradfrost.com/

  2. Nielsen, J. (2020). 10 Usability Heuristics for User Interface Design. Nielsen Norman Group. Retrieved from https://www.nngroup.com/articles/ten-usability-heuristics/

Documentation:

  1. React Team. (2023). React Documentation. Retrieved from https://react.dev/

  2. Vite Team. (2023). Vite Documentation. Retrieved from https://vitejs.dev/

  3. Tailwind Labs. (2023). Tailwind CSS Documentation. Retrieved from https://tailwindcss.com/docs

  4. Framer. (2023). Framer Motion Documentation. Retrieved from https://www.framer.com/motion/

Docker & Containerization​

Books:

  1. Matthias, K., & Kane, S. P. (2018). Docker: Up & Running (2nd ed.). O'Reilly Media. ISBN: 978-1492036739.

  2. Poulton, N. (2020). Docker Deep Dive. Independently published. ISBN: 978-1521822807.

Documentation:

  1. Docker Inc. (2023). Docker Documentation. Retrieved from https://docs.docker.com/

  2. Docker Inc. (2023). Docker Compose Documentation. Retrieved from https://docs.docker.com/compose/

Python & Software Development​

Books:

  1. Ramalho, L. (2021). Fluent Python (2nd ed.). O'Reilly Media. ISBN: 978-1492056355.

  2. Martin, R. C. (2008). Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall. ISBN: 978-0132350884.

  3. McConnell, S. (2004). Code Complete (2nd ed.). Microsoft Press. ISBN: 978-0735619678.

Documentation:

  1. Python Software Foundation. (2023). Python Documentation. Retrieved from https://docs.python.org/3/

  2. FastAPI Team. (2023). FastAPI Documentation. Retrieved from https://fastapi.tiangolo.com/

  3. Pydantic Team. (2023). Pydantic Documentation. Retrieved from https://docs.pydantic.dev/

Ethics & Responsible AI​

Books:

  1. O'Neil, C. (2016). Weapons of Math Destruction. Crown. ISBN: 978-0553418811.

  2. Noble, S. U. (2018). Algorithms of Oppression. NYU Press. ISBN: 978-1479837243.

  3. Crawford, K. (2021). Atlas of AI. Yale University Press. ISBN: 978-0300209570.

Papers:

  1. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). DOI: 10.1162/99608f92.8cd550d1

  2. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). DOI: 10.1145/3442188.3445922

  3. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

Academic Paper Sources​

Databases & Repositories:

  1. arXiv.org. (2023). arXiv: Open access to 2.3M+ papers. Cornell University. Retrieved from https://arxiv.org/

  2. Semantic Scholar. (2023). AI-powered research tool. Allen Institute for AI. Retrieved from https://www.semanticscholar.org/

  3. PubMed. (2023). Biomedical literature database. National Center for Biotechnology Information. Retrieved from https://pubmed.ncbi.nlm.nih.gov/

  4. Zenodo. (2023). Research sharing repository. CERN. Retrieved from https://zenodo.org/

  5. HuggingFace. (2023). AI model and dataset repository. Retrieved from https://huggingface.co/

Tools & Frameworks Referenced​

Version Control & CI/CD:

  1. GitHub Inc. (2023). GitHub Actions Documentation. Retrieved from https://docs.github.com/en/actions

  2. Codecov. (2023). Code Coverage Tool. Retrieved from https://about.codecov.io/

Monitoring & Observability:

  1. Grafana Labs. (2023). Grafana Documentation. Retrieved from https://grafana.com/docs/

  2. Prometheus. (2023). Prometheus Monitoring System. Retrieved from https://prometheus.io/docs/

Development Tools:

  1. Black Team. (2023). Black: The uncompromising Python code formatter. Retrieved from https://black.readthedocs.io/

  2. Mypy Team. (2023). Mypy: Static type checker for Python. Retrieved from https://mypy.readthedocs.io/

  3. Pytest Team. (2023). Pytest Documentation. Retrieved from https://docs.pytest.org/

Online Resources & Tutorials​

  1. Real Python. (2023). Python Tutorials. Retrieved from https://realpython.com/

  2. Google for Developers. (2023). Gemini API Documentation. Retrieved from https://ai.google.dev/docs

  3. MDN Web Docs. (2023). Web technology for developers. Mozilla. Retrieved from https://developer.mozilla.org/

  4. AWS Machine Learning Blog. (2023). ML Best Practices. Amazon Web Services. Retrieved from https://aws.amazon.com/blogs/machine-learning/

Conference Proceedings & Standards​

  1. NeurIPS. (2023). Conference on Neural Information Processing Systems. Retrieved from https://nips.cc/

  2. ICML. (2023). International Conference on Machine Learning. Retrieved from https://icml.cc/

  3. ACL. (2023). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/

  4. IEEE. (2023). IEEE Standards Association. Retrieved from https://standards.ieee.org/

Additional Reading​

For readers interested in deepening their understanding, the following resources provide excellent foundations:

Software Engineering:

  • Fowler, M. (2002). Patterns of Enterprise Application Architecture
  • Hunt, A., & Thomas, D. (2019). The Pragmatic Programmer (20th Anniversary Edition)

AI/ML Fundamentals:

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning
  • Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction

Systems Design:

  • Tanenbaum, A. S., & Van Steen, M. (2017). Distributed Systems (3rd ed.)
  • Burns, B. (2018). Designing Distributed Systems

Data Engineering:

  • Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering
  • Lakshmanan, V., Robinson, S., & Munn, M. (2020). Machine Learning Design Patterns

This bibliography is maintained and updated regularly. Last updated: November 2025

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