Books every software engineer should read in 2024.

Summary

In this comprehensive guide, software engineer Utav shares his curated list of essential reads for software engineers aiming to excel in their field, beyond mere programming skills. Covering a holistic approach to software engineering, the video emphasizes understanding data structures, algorithms, best practices in coding, distributed systems, data-driven decision-making, and machine learning. Utav highlights key books that foster a deep understanding of these areas without dwelling on specific programming languages or frameworks. Additionally, the guide touches on productivity, engineering management, and real-world case studies, offering insights into creating maintainable, scalable software and advancing career development in the tech industry.

Takeaways

• 📊 Being a good software engineer requires a holistic understanding beyond just programming skills, including design, implementation, and deployment processes.
• 📚 The video recommends books that focus on concepts and strategies to become a great software engineer, rather than specific programming languages or frameworks.
• 📈 'Grokking Algorithms' is highlighted for its simple and intuitive explanations of complex topics in data structures and algorithms.
• 🔧 Martin Fowler's 'Refactoring' is recommended over 'Clean Code' and 'Clean Architecture' due to the importance of improving existing code in today's rapid prototyping environment.
• 🛠 Understanding distributed systems is deemed crucial for all software engineers, with recommendations for beginners and more advanced learners.
• 📉 Data-driven decision-making is emphasized, with book recommendations on distinguishing valuable data signals from noise and statistical literacy.
• 📱 The importance of core understanding in machine learning is discussed, with book recommendations for beginners and those looking to delve deeper.
• 👨‍💻 Engineering management skills are essential, with 'Engineering Management for the Rest of Us' providing insights and pointers for effective leadership.
• 📏 Case studies in books like 'Software Engineering at Google' offer real-world insights into the practices of large tech companies.
• 🔥 'Deep Work' by Cal Newport is recommended for enhancing productivity by overcoming workplace distractions and achieving focused work.

Q & A

What is the primary focus of the book recommendations mentioned in the video?

The book recommendations focus on providing holistic software engineering knowledge, covering concepts beyond just learning specific programming languages or frameworks. They aim to teach the process of software creation, data understanding, and application of machine learning in a globally distributed setup.

Why does the speaker not recommend books on specific programming languages for software engineers?

The speaker believes that being a good software engineer is not just about mastering programming languages or frameworks, but also about understanding the broader aspects of software creation, data analysis, and machine learning in a global context.

What book does the speaker recommend for understanding data structures and algorithms, and why?

The speaker recommends 'Grokking Algorithms' by Aditya Bhargava, as it explains complex topics in a simple, intuitive, and fun manner, making it more accessible and less intimidating than more academic texts.

Why does the speaker prefer 'Refactoring' by Martin Fowler over 'Clean Code' and 'Clean Architecture' by Robert C. Martin?

The speaker believes that in the current rapid prototyping environment, the act of refactoring (improving existing code) is more important than initially writing perfect code. 'Refactoring' focuses on transforming suboptimal code into maintainable and extensible production-ready code, which aligns better with modern software development practices.

What are the recommended books for understanding distributed systems?

For beginners, 'Understanding Distributed Systems' by Roberto Vitillo is recommended for its simplicity and coverage. For more in-depth knowledge, 'Designing Data-Intensive Applications' by Martin Kleppmann (the red book) is suggested.

What is the purpose of the book 'The Signal and the Noise' by Nate Silver in the context of software engineering?

The book 'The Signal and the Noise' is recommended for understanding how to separate valuable data signals from noise, an essential skill for making informed decisions based on data analytics in software engineering.

What are the suggested books for starting a journey in machine learning?

The speaker suggests starting with 'The 100 Page Machine Learning Book' by Andrei Burkov for a concise introduction, followed by 'Deep Learning' by Yoshua Bengio and Ian Goodfellow for more detailed study. 'Designing Machine Learning Systems' by Chip Huyen is recommended for applying ML in software systems.

Why is 'Engineering Management for the Rest of Us' by Sarah Drasner recommended for software engineering managers?

The book is recommended for its insights into the world of engineering management, offering practical advice, pointers, and real-world examples for effective management in software engineering.

What is the significance of the 'case studies' category in the book recommendations?

The 'case studies' category offers real-world examples and anecdotes about successes and failures in architecting large, high-scale applications, providing insights that typically come with experience but are rarely found in generic books.

Why is 'Deep Work' by Cal Newport suggested for software engineers, and what does it offer?

Deep Work' is suggested for its strategies on overcoming workplace distractions and achieving focused work, which is crucial for high productivity in software engineering.

Outlines

00:00

📚 Essential Reads for Software Engineers

This segment introduces a curated list of books aimed at broadening the skills of software engineers beyond just programming language proficiency. The focus is on understanding the entire software development process, including design, implementation, deployment, data analysis, and machine learning in a globally distributed context. The speaker, Utav, a seasoned software engineer and entrepreneur based in Seattle, emphasizes the importance of holistic career development covering technical skills, efficiency, mindset, entrepreneurship, and financial freedom. Key book recommendations include 'Grokking Algorithms' by Aditya Bhargava for its intuitive approach to complex concepts, and 'Refactoring' by Martin Fowler, highlighting the importance of improving existing code over perfect initial code due to the rapid prototyping environment in today's software development landscape.

05:03

🌐 Navigating Distributed Systems and Data-Driven Design

This section emphasizes the critical importance of understanding distributed systems for modern software engineers, due to the global distribution of application components for improved availability, redundancy, and disaster recovery. Recommended readings include 'Understanding Distributed Systems' by Roberto Vitillo for beginners and 'Designing Data-Intensive Applications' by Martin Kleppmann for a deeper dive. The discussion also covers the necessity of being data-driven in 2024, recommending 'The Signal and the Noise' by Nate Silver for understanding data analysis and 'The Art of Statistics' by David Spiegelhalter for gaining statistical literacy. Additionally, the evolution of AI and its impact on software engineering is highlighted, suggesting books for those looking to start or deepen their understanding of machine learning.

10:04

🤖 Machine Learning Essentials and Career Transition

This paragraph delves into the foundational and advanced aspects of machine learning, crucial for software engineers in the AI-dominated landscape of 2024. Starting recommendations include 'The 100 Page Machine Learning Book' by Andrei Burkov for basics, followed by 'Deep Learning' by Yoshua Bengio and Ian Goodfellow for more comprehensive insights. Additionally, 'Designing Machine Learning Systems' by Chip Huyen is suggested for engineers focusing on building ML systems. The segment also introduces Interview Kickstart's SwitchUp course, offering a structured path for professionals transitioning into AI, data science, or machine learning roles, emphasizing practical learning and job interview preparation.

15:04

👨‍💼 Insights into Engineering Management and Real-world Case Studies

This part of the script targets engineering managers and aspiring ones, recommending 'Engineering Management for the Rest of Us' by Sarah Drasner, which provides valuable insights and tips for effective management. Additionally, the segment introduces a new category of recommendations focusing on case studies from large-scale application development, with 'Software Engineering at Google' and 'Software Engineering the Hard Parts' as highlighted readings. These books offer real-world examples and anecdotes from major tech companies, aiming to impart lessons learned from successes and failures in a way that technical concepts alone cannot.

📈 Maximizing Productivity in Software Engineering

The final segment addresses productivity, a key aspect of achieving peak output in software engineering. 'Deep Work' by Cal Newport is recommended as the go-to book for understanding and overcoming workplace distractions, promoting focused and efficient work practices. The summary stresses the importance of productivity in realizing one's full potential, regardless of the specific field of work. This section also encourages viewers to explore previous book recommendations and share their own, fostering a community of continuous learning among software engineers.

Keywords

💡Software Engineering

Software engineering is a detailed study and application of engineering to the design, development, and maintenance of software. The video emphasizes that being a good software engineer goes beyond just coding skills; it involves understanding the entire process of software creation including requirements, design, implementation, and deployment. The holistic approach is highlighted, indicating that software engineers should understand not just the technical, but also the process and improvement aspects through modern machine learning pipelines in a globally distributed setup.

💡Refactoring

Refactoring is the process of restructuring existing computer code without changing its external behavior. It's aimed at improving the nonfunctional attributes of the software. In the video, refactoring is highlighted as more crucial than writing perfect code initially because in today's rapid prototyping environment, code often starts as a 'hack' and is then gradually improved. The speaker mentions Martin Fowler's book 'Refactoring' as a key resource, emphasizing the importance of transforming hacky, unmaintainable code into something more robust and maintainable.

💡Distributed Systems

Distributed systems refer to a network of computers that communicate and coordinate their actions by passing messages. The video underscores the importance of understanding distributed systems, as most modern applications are built on such architectures for improved availability, redundancy, and disaster recovery. It introduces books for beginners and advanced learners to understand the principles and applications of distributed systems, suggesting that every software engineer should grasp these concepts.

💡Data Driven

Being data-driven refers to making decisions based on data analysis rather than intuition or personal experience. In software engineering, this involves understanding what data to collect, how to interpret it, and distinguishing valuable signals from noise. The video stresses the importance of statistical literacy and being able to interpret data accurately, suggesting books that can provide insights into becoming more data-driven in the software development process.

💡Machine Learning

Machine Learning (ML) is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The video highlights the increasing relevance of ML in software engineering and recommends books to get started with ML concepts. The speaker suggests that even if engineers are not directly working with ML, understanding its core principles is becoming increasingly important in the field.

💡Engineering Management

Engineering management involves coordinating and leading engineering teams to ensure efficient project execution and innovation. The video discusses the transition from individual contributor roles to management positions and recommends 'Engineering Management for the rest of us' by Sarah Drasner for insights into effective engineering management practices. It emphasizes the importance of skills like prioritization, collaboration, and culture in managing engineering teams effectively.

💡Case Studies

Case studies in software engineering provide real-world examples and experiences from the development of large-scale applications. The video introduces a new category of books that delve into case studies from major companies like Google and Microsoft. These books are recommended to gain insights into the successes and failures of large-scale application development, providing practical knowledge that usually comes with years of experience.

💡Productivity

In the context of software engineering, productivity refers to the efficiency and effectiveness with which software engineers complete their work. The video discusses the importance of productivity for reaching one's highest potential output and recommends 'Deep Work' by Cal Newport for strategies to overcome workplace distractions and improve concentration. The focus is on developing the ability to perform deep, focused work to increase productivity.

💡Data Structures and Algorithms

Data structures and algorithms are fundamental concepts in computer science that involve the organization of data and the methods used to manipulate it. The video suggests that a thorough understanding of these concepts is crucial for all software engineers, regardless of their specific domain. 'Grokking Algorithms' is recommended for its simple and intuitive explanation of complex topics, helping engineers build a solid foundation in these essential areas.

💡Career Development

Career development in the context of the video pertains to the growth and progression of an individual's career in software engineering. The speaker outlines five key pillars of career development: technical skills, engineering efficiency, mindset, entrepreneurship, and financial freedom. The video aims to mentor viewers on these aspects, providing guidance and recommendations to enhance their software engineering careers and overall professional development.

Highlights

Annual list of books for software engineers focusing on holistic understanding of software development.

Grokking Algorithms recommended for understanding data structures and algorithms in a simple manner.

Importance of refactoring over perfect coding highlighted through the recommendation of 'Refactoring' by Martin Fowler.

Shift towards rapid prototyping and the practical necessity of refactoring in modern software development.

Understanding distributed systems is imperative, recommended starting with 'Understanding Distributed Systems' by Roberto Vitello.

For advanced learning in distributed systems, 'Designing Data-Intensive Applications' by Martin Kleppmann is recommended.

The importance of data-driven decision making in software engineering, with book recommendations 'The Signal and the Noise' by Nate Silver and 'The Art of Statistics' by David Spiegelhalter.

Machine learning essentials covered with recommendations for 'The 100 Page Machine Learning Book' by Andriy Burkov and 'Deep Learning' by Yoshua Bengio and Ian Goodfellow.

Designing Machine Learning Systems for practical implementation advice in software engineering.

Engineering management insights from 'Engineering Management for the Rest of Us' by Sarah Drasner.

Case studies in software engineering from 'Software Engineering at Google' and 'Software Engineering the Hard Parts' for real-world insights.

Productivity in software engineering emphasized with 'Deep Work' by Cal Newport.

The video aims to provide a comprehensive guide for software engineers to enhance their careers beyond just coding skills.

Interview Kickstart's switchup course for career transition into AI, data science, or machine learning.

The video serves as a holistic resource for both budding and experienced software engineers to develop well-rounded skills.

Transcripts

[Music]

so every year I make a list of books

that I believe that every software

engineer should read regardless of what

your expertise is whether you are a

front-end developer a backend engineer

or you eat machine learning models for

breakfast I believe that being a good

software engineer in today's world isn't

just about being the best programmer or

a great designer a good software

engineer holistically understands the

process of creating software the

requirements design implementation and

deployment and it doesn't stop there a

good software engineer understands the

data signals and incremental

improvements through modern machine

learning pipelines all in a very

globally distributed setup So to that

end none of the books in this video will

be teaching you how to learn a specific

programming language or a framework I

even made a video recently about why you

don't need books to learn a new

programming language um this list of

books will instead teach you the

concepts and give you the tools and

strategies to become a great software

engineer from a very holistic

[Music]

approach hi folks my name is utav I'm a

software engineer based in Seattle over

the past 15 years or so I've held

diverse software engineering roles

created a few Tech startups and I'm

currently at Microsoft if you're new to

this channel my goal here is to help you

get the best out of your career by

mentoring you around five key pillars of

career development technical skills

engineering efficiency mindset

entrepreneurship and Financial Freedom

so if that sounds interesting please

consider subscribing and follow me for

behind the scenes and monthly

[Music]

q&as okay so whether you're just

starting out as a software engineer or

you are a professional already you will

need a thorough understanding of

programming Concepts like data

structures and algorithms for this my

recommendation hasn't changed from last

year the one book I would pick to

recommend is Aditya varg's grocking

algorithms the biggest reason I

recommend this book year after year is

because it explains complex topics in

data structures and algorithms in a very

simple intuitive manner don't take me

wrong very white paperish academic books

like introd algorithms by clrs or

algorithm design manual from skner have

the value but I do feel like those tend

to be unnecessarily complex and scare

away a lot of Engineers from getting

interested in complex Concepts without

interest you can't progress your

learning this book takes the opposite

approach by explaining things in a very

casual and fun manner but is more than

adequate to help you thoroughly grasp

the concepts and if you ever want a more

technical approach you can always pick

one of those more academic books as a

followup to this

one you can learn all the coding you

want want solve all the lead code

problems blazing fast and you will

probably do quite well in coding

interviews but for actually being a good

software engineer that alone won't get

you too far there is an art to putting

all your raw knowledge about coding and

design together as an extensible and

maintainable package that we call

software and this usually comes with

years of experience the learning that

you get from doing the right things and

all the wrong things the good news is

that you don't have to wait a decade to

gain some experience some books package

this information in a very digestible

and usable form as a set of best

practices one of my favorite books for

this is Martin fowers refactoring I used

to recommend clean code and clean

architecture by Robert C Martin as part

of this category but I decided to drop

those books this year I've been building

software for more than two decades now

and now more than ever companies and

teams have moved to rapid prototyping

where you build a hack together proof of

concept completely drop it if it fails

the feasibility test or Gra gradually

turn that hacky po into production grade

code if it passes the initial sniff test

in this system there's really no time to

write this perfect Enterprise level code

that follows all the best practices in

the middle of grabbing the next

opportunity and turning around a

workable demo to get Buy in from your

senior leadership team there is no time

to follow the golden route of perfect

software design and this is why the act

of refactoring which is basically

improving existing code is way more

important than writing great code in the

first place and that's what this book is

all about how to take crappy hacky

unmaintainable code whether it's yours

or someone else's and transform that

into bug-free maintainable and

extensible production ready Cod I'll

also add a note here not just for this

book but for any book that teaches you

best practices don't take them as the

Bible of coding and instead as a

supplemental information that you could

potentially apply in your scenario

because not every principle applies to

every situation and understanding that

is as important as understanding the

concepts themselves

okay if you're still wondering what

distributed systems are in 2024 you have

either been living under a giant rock or

you simply don't realize that literally

every decent application you use is a

collection of one or more globally

distributed systems simply put days of

Hosting your entire application on a Dev

box under your desk are long gone even

if your application is a monolith it

will have multiple tiers that will

eventually be distributed regionally or

globally for availability redundancy

disaster recovery and so on so it's

imperative that every software engineer

at least understands the major Concepts

behind distributed architectures I have

two book recommendations for this and

they have remained the same for a few

years now because one the space hasn't

really seen any radical changes in these

years and two no better books have come

up if you're absolutely new to

distributed systems start with Roberto

vitello's understanding distributed

system this book does for distributed

systems what bling algorithms does for

data structures and algorithms it

simplifies the concepts and makes it

easily digestible even for beginners at

the same time it covers a lot of ground

and gives you a highlevel view of the

entire space I won't go into much more

detail here but if you're interested in

the details about this book check out my

review I did a while back if you have

worked with distributed architectures

whether practically or academically then

the next step is the Holy Grail of books

for distributed systems and that's

Martin kelman's designing data intensive

applications commonly known as ddia or

just the red book uh this book takes

every concept covered in understanding

distributed systems and Dives much

deeper with implementation details case

studies and even algorithms which makes

this book also a great follow-up for the

first

one similar to distributed systems if

you're not data driven in 2024 you're

not doing it right you don't have to be

or need to be a data scientist to

understand data even a high level

understanding understanding of what

signals to look for how to collect data

or interpret your data and Telemetry in

a statistically accurate manner will go

a long way in your software engineering

career now if you're an actual data

scientist you will be reading a lot more

technical material in and around data

science but since this video is for

software Engineers I will recommend two

books that are very easy reads but will

give you deep insights into how you can

leverage data to your advantage the

first book here is the signal and the

Noise by Nate silver which as the name

suggests explores the idea of separating

signals from the noise I've seen this

happen too many times over the course of

my career where teams and even

organizations obsess over certain

metrics just because they have the data

to populate set metric and not because

the metric is actually important or

actionable so how do you decide what

data to collect how to make sense of it

and also confidently ignore a lot of

stuff that comes in as noise the last

part is equally as important as

capturing valuable data I love this book

because it investigates the idea of

making predictions by distinguishing

true signal from a ton of noisy data and

the scenarios used in this book don't

have anything to do with software

engineering there are things like

baseball hurricanes gambling and the

stock market and that is the beauty of

data it is universal which also means

that the lessons you learn from this

book are easily transferable to the

field of software engineering the second

book is about statistical literacy if

you don't understand some key Concepts

in statistics you will not be able to

make sense of data regardless of how

much noise is filtered out I remember

when I first started working with data I

realized I was a bit weak in some of the

concepts in statistics so I reached out

to a buddy of mine who's a data

scientist and he recommended the book

probability and statistics to me it was

a good reference book but I felt like I

was back in time doing College

mathematics and that's the problem most

books that teach mathematical Concepts

tend to be very academic which takes a

different mindset to read and which

isn't always fun but fear not because

the art of Statistics how to learn from

data by David Spiegel halter will help

you learn all the important Concepts and

statistics while chilling in your couch

seriously this is one of the easiest

reads in statistics that covers such a

wide breath of concept this book also

has a ton of examples where statistical

reasoning was applied to real world

problems and this anecdotal form of

teaching really helps you grasp the

concepts helping you form a mental model

of how you can interpret data to answer

the questions you

have since we are discussing the topic

of data I cannot ignore the elephant in

the room especially not in 2024 with

diffusion models like Sora that just

launched a few weeks ago we have gone

from AI generating crappy videos like

scary Will Smith gobling spaghetti to a

pristine render of a couple walking

around in Japan in less than 2 years and

with the speed and intensity at which AI

is being tagged to literally any

application in existence it would be

foolish as a software engineer to not at

least have some core understanding of

machine learning I have three book

recommendations for machine learning the

first one is the 100 page machine

learning book by Andre bof this is a

great little introduction to the world

of machine learning it covers the basics

of some of the most common machine

learning Concepts like neural networks

and deep learning which is what models

like GPT 4 are based on I would say that

this is a very solid first machine

learning book um it's not going to make

you an expert in ml obviously but it is

going to cover all the basics in a

concise and fun manner without

overwhelming you or putting you to sleep

if this book Sparks your interest then

you can always refer to a more advanced

book and that's my second recommendation

if you want to get a bit more in depth

dep my recommendation is to go with deep

learning by yosua Benjo and Ian

Goodfellow this is your typical machine

learning book that covers the details

and nuances in a more academic fashion

or you can also go for the tried and

tested route of the old book AI a modern

approach by Peter norvic and Stuart

Russell my final recommendation here is

designing machine learning systems by

chip huan since we are software

Engineers rather than being experts in

the models and algorithms themselves

chances are likely that we will end up

building systems that deal with machine

learning and that's exactly what this

book is for um it's essentially what

designing data intensive application is

for distributed systems it guides you

through the process of creating training

data feature application model retention

and a whole lot more but if you aren't

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[Music]

video okay my next section is primarily

for engineering managers but I encourage

everyone to read this because most of us

will likely someday become software

engineering managers the book is

Engineering Management for the rest of

us by Sarah drasner this is an excellent

book that gives you an insight into the

world of engineering managers and gives

you some amazing pointers at being a

great one at that as someone who's

managed many different teams both as a

tech lead and as an engineering manager

I definitely think that this book has

some very good advice pointers and real

world examples to help you navigate your

path as a software engineering manager

or a future software engineering manager

this book explores all the key areas

that you'll deal as an em prioritization

execution collaboration escalation

feedback and culture to name a

few okay we're still not done this year

I decided to add a brand new category

called case studies to my book

recommendations look you can learn

technical Concepts from pretty much

anywhere but hearing stories anecdotes

and real world example though they

really help you grow and unfortunately

you can't really get them from most

books or generic YouTube videos that

appeal to the masses that comes with

experience but I have found that there

are a few books over the past few years

that I've released um that do a great

job at filling that void and that's how

this category was born these are two

books that share very unique anecdotes

about the successes and failures in

architecting large highs scale

applications and I think that both these

books do an excellent job at giving you

a glimpse at how it is to build large

applications at some of the biggest

companies like the likes of Microsoft

Google Amazon so on and so forth the

first book Is software engineering the

hard Parts this is a great book to pick

up because it is literally a collection

of real world examples explaining

anecdotally why large applications

especially with distributed

architectures are hard why things worked

when they worked and why they failed

often catastrophically while this book

will never replace real experience as I

said before this is the closest you'll

get to one that can give give you a lot

of information that only comes with

experience please note that this book is

not for absolute beginners you need to

have at least some understanding of how

large applications are built or at least

a theoretical knowledge of distributed

systems to fully enjoy this book my

second recommendation here is software

engineering at Google if you have ever

wondered how it feels like to work at a

large tech company like Google what

engineering practices they follow to

keep their code healthy and maintainable

or how they manage their large code base

this is a great book for you and even

though this book has stories about

Google the information here applies to

pretty much any top tier big tech

company and while this book does not lay

out the best practices like the book

refactoring does for example reading the

stories here will still teach you some

fundamental principles that involve

designing architecting writing and

maintaining

[Music]

code okay I promise this is the last

category I didn't want to make five

different videos about books trying to

milk as many views as possible instead I

wanted to make one really holistic video

that can help every software engineer in

all aspects of their work and even

though productivity isn't directly

related to software engineering per se

it's hard to leave it behind as a

category because if you aren't

productive at what you do you will never

really reach The Highest Potential

output you can actually provide now

productivity is a massive topic one that

probably warrants a video on its own but

for the sake of brevity I will make only

one recommendation here if you had to

read one book on productivity go ahead

and grab deep work by Cal Newport this

book examines current workplace culture

and the unintentional distractions faced

by employees and offers strategies to

overcome these distractions this book is

essentially divided into two parts the

idea which discusses labor Trends and

the rise of machine learning and

practical rules for achieving deep work

and the second part is the rules which

provides practical guidelines and

strategies for achieving deep work by

emphasizing the importance of training

the mind to focus and minimize

distractions software engineer or not

I'm sure anyone that reads this book

will get value out of it also check out

last year book recommendations for

additional books that I did not include

this year that still have value and if

you have any recommendations on your own

please add them in the comments so we

can all read them and definitely watch

my review of the Apple Vision Pro from a

software engineering point of view and

finally please do the usual and help out

the channel like comment and subscribe

cheers