The Best Books for Software Engineers (and Developers) | Highlights and Annotations by Gistr.

Distributed Systems: Designing Data-Intensive Applications(for a comprehensive guide) Fundamentals of Data Pipelines(for a practical approach focusing on modern data pipelines). Introduction to Distributed Systems(offers a free, introductory understanding.) Data Engineering: Focus on "Designing Data-Intensive Applications" for a broad understanding, and "Fundamentals of Data Pipelines" for a practical, modern perspective on data pipelines, including batch and stream processing, cloud technologies, and architectural considerations. Machine Learning: Suggestions include a concise 100-page introduction to machine learning concepts, "Deep Learning" (for a comprehensive guide), and "Introduction to Machine Learning" (from MIT Press) for a foundational understanding. "Designing Machine Learning Systems" addresses the engineering challenges of deploying ML models. DevOps: "The Phoenix Project" is recommended for understanding DevOps concepts, while " Accelerate" provides a practical guide combining lean thinking and DevOps principles. "Docker in Action" focuses on containerization and its integration with DevOps. Algorithms and Data Structures: "Grokking Algorithms" offers a visual and approachable introduction, while "Introduction to Algorithms" provides a more comprehensive and academically rigorous treatment. Overall Approach: The recommendations are categorized by skill level (beginner to advanced) and tailored to match individual interests and expertise to help software engineers enhance their skills for the future.