Books every software engineer must read in 2025

each year share list of books that think every software engineer should read for this year I've kept some really good books from last year but also added some exciting new ones to help you keep up with the latest Trend in software engineering especially the ongoing AI Revolution hi there I'm utsav seattle-based software engineer with over 15 years of experience in both big Tech and startups if you're new here Mentor software Engineers to help them excel in their careers if you think that could benefit you consider subscribing and connect with me on Instagram LinkedIn or any other social media where I'll be happy to answer your questions directly for this video I've categorized my recommendations into skill sets like distributed systems or machine learning with options for all experience levels from beginner to Advanced know it's impossible to read everything which is why always recommend range of books to cover different interests and expertise with that said let's start with books for distributed systems my first recommendation is distributed systems for Fun and Profit if you are completely new to distributed systems this is great starting it's concise approachable book that breaks down the core ideas of distributed architectures into very simple Concepts it is perfect for getting quick but meaningful introduction into the world of distributed systems without drowning in technical details and here's the best part this book is actually free I'll put the link in the description below next up is understanding distributed systems this book is incredibly effective at explaining how the various components of distributed systems work and collaborate with one another it covers key Concepts like consistency replication recovery while showing how they apply to systems like databases and caches with real world examples making it relevant for software engineers and Architects alike this book keeps staying on my list year-over-year because it excels at simplifying complex concepts with practical examples making distributed systems approachable even for software Engineers without Real World Experience next up is pretty much the Bible of distributed systems Martin kman designing data intensive applications this is comprehensive guide that focuses on Building Systems that are scalable reliable and maintainable it explores the core principles of scalable systems including data storage retrieval encoding replication partitioning consistency to name few through very good mix of academic and practical examples the author explains the architecture of key components of distributed systems and data pipelines showing you how to balance performance reliability and maintainability personally love this book and think it's must read for everybody because it provides perfect balance of theory and practice breaking down complex topics into digestible sections while offering real world applicability for modern datadriven systems and since we are in the topic of high volumes of data let's look at some of my recommendations for data engineering first up is the fundamentals of data engineering by Joe Rice and Matt Housley this book covers the essential principles practices and tools for building modern data pipelines it walks through the complete life cycle of data engineering from ingestion and storage to processing and transformation as well as analytics it helps you understand the crucial architectural decisions and trade-offs needed to design scalable reliable and efficient Data Systems the authors also expertly break down key Concepts like batch in stream processing data modeling and orchestration they explore modern Cloud Technologies and introduce you to tools like ca spark Snowflake and airflow showing you how to evaluate and integrate them into your data workflows unlike books that solely focus on coding or tools this book provides strategic view of how to align data engineering with business goals and organizational needs offering you holistic view of data engineering while also remaining accessible to readers that are very new to this field obviously no discussion of data engineering would be complete without talking about streaming systems so my next recommendation is streaming systems the what where when and how of large scale data processing in nutshell this book teaches you how to design build and operate streaming systems that handle real-time data at scale the authors dive into essential Concepts like event time processing time watermarks and the fundamental differences between batch and stream processing they emphasize what truly matters in the real world streaming systems like correctness latency and Fa tolerance they also tackle Advanced topics like windowing State Management and the unique challenges of distributed processing in Dynamic environment what really like about this book is that that the authors draw from their experience as Google Engineers to provide clear actionable advice for Designing data processing systems and explain the principles behind modern stream processing Frameworks like beam Flink and Spark okay large volumes of data naturally lead us to the hottest Topic in Tech right now machine learning but before that if you found this video useful so far please give it like and consider subscribing for more content like this would really appreciate it okay so if you're absolutely new to machine learning learning and want quick rundown of what it's all about recommend starting with machine learning the new AI this book offers simple and concise introduction into machine learning it is part of the MIT press Essential Knowledge series and it explains how machine Learning Works its applications across Industries and its societal impact and it's really designed for readers with little to no technical background at all like this book Because of its simple and non-technical approach which makes it ideal for beginners and anyone looking to understand the fun Ms of machine learning without having to dive into the details of mathematics and coding and all that stuff and trust me this is not common thing among books that cover the topic of machine learning for more technical but still beginner friendly introduction into machine learning recommend the 100 page machine learning it's concise but also offers essential Concepts like supervised learning unsupervised learning and deep learning while offering practical insights into model selection and implementation what like about this book is that it offers direct clear explanations and delivers dense yet digestible overview of machine learning in just 100 Pages next up is machine learning the Art and Science of algorithms that make sense of data this is more comprehensive introduction to machine learning principles and techniques and primarily focuses on how to design algorithms that learn from data it thoroughly covers essential topics like classification regression clustering feature selection model evaluation overfitting bias variance trade-off and performance metrics and the author also highlights how understanding both the problem domain and the data is vital for Effective algorithm design the standout feature for this book for me is it's focus on understanding the why behind machine learning techniques and not just the how think that makes it an excellent resource for readers who want to develop an intuitive understanding of the field rather than solely focusing on the implementation okay so just as designing data intensive applications is the de facto standard for distributed systems deep learning by Ian Goodfellow yosua benio and Aaron Corville is the definitive book on deep learning written by Leading researchers in the field it offers comprehensive introduction into the foundations techniques and applications of deep learning making it must read for anyone looking to become an expert in the subject of deep learning this book starts with the fundamental prerequisites like linear algebra probability and machine learning Basics then it rapidly progresses into more advanced topics like neural networks optimization algorithms and regularization techniques you'll also explore other crucial topics like convolutional neural networks recurrent neural networks generative models deep and reinforcement learning supervised learning autoencoders representation learning and much more think this book's key strength is its depth which is critical for Mastery in any field especially deep learning it explains the mathematical principles behind deep learning algorithms while also discussing best practices for real world implementation which is why it has become the go-to reference for researchers and practitioners in the field of artific IAL intelligence okay so not everyone builds system in Tech right lot of us are there just to build pipelines that handle the deployment and delivery of these multi-layer distributed systems so understanding devops is also very important in 2025 and Beyond so let's look at some recommendations for devops if you're absolutely new to devops recommend reading the Phoenix project this is very cool novel style book that demonstrates devop Concepts through the story of an IT manager tasked with saving failing project it uses storytelling to explain four devops practices like continuous delivery collaboration and automation but for those that are already sort of familiar with devops recommend Lean devops by Robert benfeld this is practical guide that combines lean thinking principles with devops practices showing you how to deliver software faster better and more coste effectively the author does pretty good job at connecting core Lin principles like waste reduction flow optimization and continuous improvement with modern develops practices like cicd infrastructure as code or automated testing like this book Because of its practical nature it includes case studies real world examples and Frameworks to help you adopt lean practices and improve software delivery pipelines my next recommendation is Docker in action this book teaches you how to use Docker for containerization it starts off by covering Docker fundamentals like installation containers and image creation then progresses into more advanced topics like networking or cration and security you learn essential features like Docker compose and swarm along with practical skills in debugging storage management and container deployment there's also strong focus in this book on devops integration and cicd workflows so that's great thing as well as an alternate to this book you can also try Cloud native devops with kubernetes which teaches you how to combine devops practices with kubernetes to build scalable Cloud native systems next up is designing machine learning systems by chip this is practical guide to building and deploying machine learning systems that are scalable maintainable and production ready it focuses on the endtoend process of machine Learning System design from understanding business needs and data requirements to deploying and monitoring machine learning models in production the author also emphasizes the importance of integrating machine learning into larger systems covering topics like data pipelines feature engineering model training and evaluation this book also delves into real world challenges such as scalability lat latency reproducibility and model drift think this book does very good job at Bridging the Gap between training models and deploying them effectively in production by focusing on real world engineering challenges and practical design principles let's move on to the foundational knowledge that is core to most of the areas that I've mentioned so far that is data structures and algorithms if you are absolutely new to algorithms recommend starting off with grocking algorithms by Aditya varva this is visually engaging and beginner friendly introduction to data structures and algorithms the book uses combination of clear explanations analogies and detailed illustrations to make complex Concepts appear simple to readers who may not have strong mathematical or computer science background it introduces the fundamental data structures you need to know like arrays link lists hash table trees and graphs and also covers all of the foundational Topics in algorithms like binary search recursion sorting and graph algorithms each chapter includes prac iCal examples pseudo code and exercises to read force your learning what sets grocking algorithms apart is the visual approach diagrams and step-by-step walkthroughs make it easier to understand how each algorithm Works in practice and also like the conversational tone that this book has and the gradual progression of difficulty makes it suitable for self-learners and beginners who want to develop intuitive grasp of algorithms next up is Introduction to algorithms which is commonly referred to as CL RS after its author this is the definitive book for learning algorithms and data structures it provides comprehensive and rigorous treatment of algorithms covering everything from elementary topics like sorting and searching to Advanced topics like dynamic programming graph algorithms and NP completeness the book emphasizes algorithm design and Analysis with each chapter providing pseudo code mathematical explanations and exercises it focuses on efficiency and problem solving serving both academic study and practical programming needs the content is organized progressively starting with the fundamentals and moving on to Advanced topics like string matching and computational geometry this allows you to gradually build your knowledge over time what makes this book great is its depth and breadth of coverage which is why it's the gold standard for learning algorithms both academically and in practice it's must have for anyone that is serious about computer science okay look as said in the beginning of the video the goal here isn't to read all of these books but to pick few that match your interest and expertise that being said I'm sure missed couple of your favorite books so do me favor and leave me comment below with your recommendations would love to check them out also if you notice my recommendations in this video were divided into skill sets that was by Design because believe that these skill sets are critical for any software engineer's future so watch this video to learn why these skills matter so much in 2025 and going forward I'll see you in the next one cheers