Reddit mentions of Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World, 3rd Edition

Depending on how deep you want to go, use the following resources:

Learning the basics:

[The Odin Project: Databases] (https://www.theodinproject.com/courses/databases)

Teach Yourself Computer Science: Databases

Teach Yourself CS recommends as an introduction UCBerkeley's 2015 Course: Computer Science 186 - Introduction to Database Systems video recordings.

Going further:

Teach Yourself CS also recommends some further reading and just writing a simple relational database management system from scratch. Which will probably put your understanding well ahead of most junior devs.

Teach Yourself CS continues with the following recommendation:

>Finally, data modeling is a neglected and poorly taught aspect of working with databases. Our suggested book on the topic is Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World.

Some further readings from the The Architecture of Open Source Applications book series:

Berkeley DB

Dog Bed Database

[SQLAlchemy](
http://aosabook.org/en/sqlalchemy.html)


An introduction to NoSQL, but most importantly an explanation of the cases where SQL and the relational model suit your needs, and others where a NoSQL system might be a better fit.

> It’s hard to consolidate databases theory without writing a good amount of code. CS 186 students add features to Spark, which is a reasonable project, but we suggest just writing a simple relational database management system from scratch. It will not be feature rich, of course, but even writing the most rudimentary version of every aspect of a typical RDBMS will be illuminating.
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> Finally, data modeling is a neglected and poorly taught aspect of working with databases. Our suggested book on the topic is Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World.
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> ### Languages and Compilers
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> Most programmers learn languages, whereas most computer scientists learn about languages. This gives the computer scientist a distinct advantage over the programmer, even in the domain of programming! Their knowledge generalizes; they are able to understand the operation of a new language more deeply and quickly than those who have merely learnt specific languages.
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> The canonical introductory text is Compilers: Principles, Techniques & Tools, commonly called “the Dragon Book”. Unfortunately, it’s not designed for self-study, but rather for instructors to pick out 1-2 semesters worth of topics for their courses. It’s almost essential then, that you cherrypick the topics, ideally with the help of a mentor.
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> If you choose to use the Dragon Book for self-study, we recommend following a video lecture series for structure, then dipping into the Dragon Book as needed for more depth. Our recommended online course is Alex Aiken’s, available from Stanford’s MOOC platform Lagunita.
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> As a potential alternative to the Dragon Book we suggest Language Implementation Patterns by Terence Parr. It is written more directly for the practicing software engineer who intends to work on small language projects like DSLs, which may make it more practical for your purposes. Of course, it sacrifices some valuable theory to do so.
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> For project work, we suggest writing a compiler either for a simple teaching language like COOL, or for a subset of a language that interests you. Those who find such a project daunting could start with Make a Lisp, which steps you through the project.
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> [Compilers: Principles, Techniques & Tools](https://teachyourselfcs.com//dragon.jpg) [Language Implementation Patterns](https://teachyourselfcs.com//parr.jpg)> Don’t be a boilerplate programmer. Instead, build tools for users and other programmers. Take historical note of textile and steel industries: do you want to build machines and tools, or do you want to operate those machines?
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> — Ras Bodik at the start of his compilers course
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> ### Distributed Systems
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> As computers have increased in number, they have also spread. Whereas businesses would previously purchase larger and larger mainframes, it’s typical now for even very small applications to run across multiple machines. Distributed systems is the study of how to reason about the tradeoffs involved in doing so, an increasingly important skill.
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> Our suggested textbook for self-study is Maarten van Steen and Andrew Tanenbaum’s Distributed Systems, 3rd Edition. It’s a great improvement over the previous edition, and is available for free online thanks to the generosity of its authors. Given that the distributed systems is a rapidly changing field, no textbook will serve as a trail guide, but Maarten van Steen’s is the best overview we’ve seen of well-established foundations.
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> A good course for which some videos are online is MIT’s 6.824 (a graduate course), but unfortunately the audio quality in the recordings is poor, and it’s not clear if the recordings were authorized.
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> No matter the choice of textbook or other secondary resources, study of distributed systems absolutely mandates reading papers. A good list is here, and we would highly encourage attending your local Papers We Love chapter.
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> [Distributed Systems 3rd edition](https://teachyourselfcs.com//distsys.png)
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> ## Frequently asked questions
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> #### What about AI/graphics/pet-topic-X?
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> We’ve tried to limit our list to computer science topics that we feel every practicing software engineer should know, irrespective of specialty or industry. With this foundation, you’ll be in a much better position to pick up textbooks or papers and learn the core concepts without much guidance. Here are our suggested starting points for a couple of common “electives”:
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> - For artificial intelligence: do Berkeley’s intro to AI course by watching the videos and completing the excellent Pacman projects. As a textbook, use Russell and Norvig’s Artificial Intelligence: A Modern Approach.
> - For machine learning: do Andrew Ng’s Coursera course. Be patient, and make sure you understand the fundamentals before racing off to shiny new topics like deep learning.
> - For computer graphics: work through Berkeley’s CS 184 material, and use Computer Graphics: Principles and Practice as a textbook.
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> #### How strict is the suggested sequencing?
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> Realistically, all of these subjects have a significant amount of overlap, and refer to one another cyclically. Take for instance the relationship between discrete math and algorithms: learning math first would help you analyze and understand your algorithms in greater depth, but learning algorithms first would provide greater motivation and context for discrete math. Ideally, you’d revisit both of these topics many times throughout your career.
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> As such, our suggested sequencing is mostly there to help you just get started… if you have a compelling reason to prefer a different sequence, then go for it. The most significant “pre-requisites” in our opinion are: computer architecture before operating systems or databases, and networking and operating systems before distributed systems.
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> #### Who is the target audience for this guide?
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> We have in mind that you are a self-taught software engineer, bootcamp grad or precocious high school student, or a college student looking to supplement your formal education with some self-study. The question of when to embark upon this journey is an entirely personal one, but most people tend to benefit from having some professional experience before diving too deep into CS theory. For instance, we notice that students love learning about database systems if they have already worked with databases professionally, or about computer networking if they’ve worked on a web project or two.
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> #### How does this compare to Open Source Society or freeCodeCamp curricula?
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> The OSS guide has too many subjects, suggests inferior resources for many of them, and provides no rationale or guidance around why or what aspects of particular courses are valuable. We strove to limit our list of courses to those which you really should know as a software engineer, irrespective of your specialty, and to help you understand why each course is included.
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> freeCodeCamp is focused mostly on programming, not computer science. For why you might want to learn computer science, see above.
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> #### What about language X?
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> Learning a particular programming language is on a totally different plane to learning about an area of computer science — learning a language is much easier and much less valuable. If you already know a couple of languages, we strongly suggest simply following our guide and fitting language acquisition in the gaps, or leaving it for afterwards. If you’ve learned programming well (such as through Structure and Interpretation of Computer Programs), and especially if you have learned compilers, it should take you little more than a weekend to learn the essentials of a new language.
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> #### What about trendy technology X?
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> (continues in next comment)