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coding-interview-university

Fri, 19 Sep 2025 15:27:46 +0800

jwasham/coding-interview-university

Coding Interview University

I originally created this as a short to-do list of study topics for becoming a software engineer, but it grew to the large list you see today. After going through this study plan, I got hired as a Software Development Engineer at Amazon! You probably won’t have to study as much as I did. Anyway, everything you need is here.

I studied about 8-12 hours a day, for several months. This is my story: Why I studied full-time for 8 months for a Google interview

Please Note: You won’t need to study as much as I did. I wasted a lot of time on things I didn’t need to know. More info about that is below. I’ll help you get there without wasting your precious time.

The items listed here will prepare you well for a technical interview at just about any software company, including the giants: Amazon, Facebook, Google, and Microsoft.

Best of luck to you!

Translations:

Translations in progress:

What is it?

This is my multi-month study plan for becoming a software engineer for a large company.

Required:

A little experience with coding (variables, loops, methods/functions, etc)
Patience
Time

Note this is a study plan for software engineering, not frontend engineering or full-stack development. There are really super roadmaps and coursework for those career paths elsewhere (see https://roadmap.sh/ for more info).

There is a lot to learn in a university Computer Science program, but only knowing about 75% is good enough for an interview, so that’s what I cover here. For a complete CS self-taught program, the resources for my study plan have been included in Kamran Ahmed’s Computer Science Roadmap: https://roadmap.sh/computer-science

The Study Plan

Topics of Study

Algorithmic complexity / Big-O / Asymptotic analysis
Data Structures
- Arrays
- Linked Lists
- Stack
- Queue
- Hash table
More Knowledge
- Binary search
- Bitwise operations
Trees
- Trees - Intro
- Binary search trees: BSTs
- Heap / Priority Queue / Binary Heap
- balanced search trees (general concept, not details)
- traversals: preorder, inorder, postorder, BFS, DFS
Sorting
- selection
- insertion
- heapsort
- quicksort
- mergesort
Graphs
- directed
- undirected
- adjacency matrix
- adjacency list
- traversals: BFS, DFS
Even More Knowledge
Final Review

Getting the Job

—————- Everything below this point is optional —————-

Optional Extra Topics & Resources

Additional Books
System Design, Scalability, Data Handling (if you have 4+ years experience)
Additional Learning
- Compilers
- Emacs and vi(m)
- Unix command line tools
- Information theory
- Parity & Hamming Code
- Entropy
- Cryptography
- Compression
- Computer Security
- Garbage collection
- Parallel Programming
- Messaging, Serialization, and Queueing Systems
- A*
- Fast Fourier Transform
- Bloom Filter
- HyperLogLog
- Locality-Sensitive Hashing
- van Emde Boas Trees
- Augmented Data Structures
- Balanced search trees
  - AVL trees
  - Splay trees
  - Red/black trees
  - 2-3 search trees
  - 2-3-4 Trees (aka 2-4 trees)
  - N-ary (K-ary, M-ary) trees
  - B-Trees
- k-D Trees
- Skip lists
- Network Flows
- Disjoint Sets & Union Find
- Math for Fast Processing
- Treap
- Linear Programming
- Geometry, Convex hull
- Discrete math
Additional Detail on Some Subjects
Video Series
Computer Science Courses
Papers

Why use it?

If you want to work as a software engineer for a large company, these are the things you have to know.

If you missed out on getting a degree in computer science, like I did, this will catch you up and save four years of your life.

When I started this project, I didn’t know a stack from a heap, didn’t know Big-O anything, or anything about trees, or how to traverse a graph. If I had to code a sorting algorithm, I can tell ya it would have been terrible. Every data structure I had ever used was built into the language, and I didn’t know how they worked under the hood at all. I never had to manage memory unless a process I was running would give an “out of memory” error, and then I’d have to find a workaround. I used a few multidimensional arrays in my life and thousands of associative arrays, but I never created data structures from scratch.

It’s a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time.

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How to use it

Everything below is an outline, and you should tackle the items in order from top to bottom.

I’m using GitHub’s special markdown flavor, including tasks lists to track progress.

More about GitHub-flavored markdown

If you don’t want to use git

On this page, click the Code button near the top, then click “Download ZIP”. Unzip the file and you can work with the text files.

If you’re open in a code editor that understands markdown, you’ll see everything formatted nicely.

If you’re comfortable with git

Create a new branch so you can check items like this, just put an x in the brackets: [x]

Fork the GitHub repo: https://github.com/jwasham/coding-interview-university by clicking on the Fork button.

Clone to your local repo:

git clone https://github.com/<YOUR_GITHUB_USERNAME>/coding-interview-university.git
cd coding-interview-university
git remote add upstream https://github.com/jwasham/coding-interview-university.git
git remote set-url --push upstream DISABLE  # so that you don't push your personal progress back to the original repo

Mark all boxes with X after you completed your changes:

git commit -am "Marked personal progress"
git pull upstream main  # keep your fork up-to-date with changes from the original repo

git push # just pushes to your fork

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Don’t feel you aren’t smart enough

Successful software engineers are smart, but many have an insecurity that they aren’t smart enough.
The following videos may help you overcome this insecurity:
- The myth of the Genius Programmer
- It’s Dangerous to Go Alone: Battling the Invisible Monsters in Tech

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A Note About Video Resources

Some videos are available only by enrolling in a Coursera or EdX class. These are called MOOCs. Sometimes the classes are not in session so you have to wait a couple of months, so you have no access.

It would be great to replace the online course resources with free and always-available public sources, such as YouTube videos (preferably university lectures), so that you people can study these anytime, not just when a specific online course is in session.

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Choose a Programming Language

You’ll need to choose a programming language for the coding interviews you do, but you’ll also need to find a language that you can use to study computer science concepts.

Preferably the language would be the same, so that you only need to be proficient in one.

For this Study Plan

When I did the study plan, I used 2 languages for most of it: C and Python

C: Very low level. Allows you to deal with pointers and memory allocation/deallocation, so you feel the data structures and algorithms in your bones. In higher-level languages like Python or Java, these are hidden from you. In day-to-day work, that’s terrific, but when you’re learning how these low-level data structures are built, it’s great to feel close to the metal.
- C is everywhere. You’ll see examples in books, lectures, videos, everywhere while you’re studying.
- The C Programming Language, 2nd Edition
  - This is a short book, but it will give you a great handle on the C language and if you practice it a little you’ll quickly get proficient. Understanding C helps you understand how programs and memory work.
  - You don’t need to go super deep in the book (or even finish it). Just get to where you’re comfortable reading and writing in C.
Python: Modern and very expressive, I learned it because it’s just super useful and also allows me to write less code in an interview.

This is my preference. You do what you like, of course.

You may not need it, but here are some sites for learning a new language:

For your Coding Interview

You can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:

C++
Java
Python

You could also use these, but read around first. There may be caveats:

JavaScript
Ruby

Here is an article I wrote about choosing a language for the interview: Pick One Language for the Coding Interview. This is the original article my post was based on: Choosing a Programming Language for Interviews

You need to be very comfortable in the language and be knowledgeable.

Books for Data Structures and Algorithms

This book will form your foundation for computer science.

Just choose one, in a language that you will be comfortable with. You’ll be doing a lot of reading and coding.

Python

Coding Interview Patterns: Nail Your Next Coding Interview (Main Recommendation)
- An insider’s perspective on what interviewers are truly looking for and why.
- 101 real coding interview problems with detailed solutions.
- Intuitive explanations that guide you through each problem as if you were solving it in a live interview.
- 1000+ diagrams to illustrate key concepts and patterns.

C

Algorithms in C, Parts 1-5 (Bundle), 3rd Edition
- Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms

Java

Your choice:

Goodrich, Tamassia, Goldwasser
- Data Structures and Algorithms in Java
Sedgewick and Wayne:
- Algorithms
- Free Coursera course that covers the book (taught by the authors!):
  - Algorithms I
  - Algorithms II

C++

Your choice:

Goodrich, Tamassia, and Mount
- Data Structures and Algorithms in C++, 2nd Edition
Sedgewick and Wayne
- Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching
- Algorithms in C++ Part 5: Graph Algorithms

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Interview Prep Books

Here are some recommended books to supplement your learning.

Coding Interview Patterns: Nail Your Next Coding Interview
Programming Interviews Exposed: Coding Your Way Through the Interview, 4th Edition
- Answers in C++ and Java
- This is a good warm-up for Cracking the Coding Interview
- Not too difficult. Most problems may be easier than what you’ll see in an interview (from what I’ve read)
Cracking the Coding Interview, 6th Edition
- answers in Java

If you have tons of extra time:

Choose one:

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Don’t Make My Mistakes

This list grew over many months, and yes, it got out of hand.

Here are some mistakes I made so you’ll have a better experience. And you’ll save months of time.

1. You Won’t Remember it All

I watched hours of videos and took copious notes, and months later there was much I didn’t remember. I spent 3 days going through my notes and making flashcards, so I could review. I didn’t need all of that knowledge.

Please, read so you won’t make my mistakes:

Retaining Computer Science Knowledge.

2. Use Flashcards

To solve the problem, I made a little flashcard site where I could add flashcards of 2 types: general and code. Each card has a different formatting. I made a mobile-first website, so I could review on my phone or tablet, wherever I am.

Make your own for free:

Flashcards site repo

I DON’T RECOMMEND using my flashcards. There are too many and most of them are trivia that you don’t need.

But if you don’t want to listen to me, here you go:

Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It’s way too much for what’s required.

Note on flashcards: The first time you recognize you know the answer, don’t mark it as known. You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain.

An alternative to using my flashcard site is Anki, which has been recommended to me numerous times. It uses a repetition system to help you remember. It’s user-friendly, available on all platforms, and has a cloud sync system. It costs $25 on iOS but is free on other platforms.

My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks @xiewenya).

Some students have mentioned formatting issues with white space that can be fixed by doing the following: open the deck, edit the card, click cards, select the “styling” radio button, and add the member “white-space: pre;” to the card class.

3. Do Coding Interview Questions While You’re Learning

THIS IS VERY IMPORTANT.

Start doing coding interview questions while you’re learning data structures and algorithms.

You need to apply what you’re learning to solve problems, or you’ll forget. I made this mistake.

Once you’ve learned a topic, and feel somewhat comfortable with it, for example, linked lists:

Open one of the coding interview books (or coding problem websites, listed below)
Do 2 or 3 questions regarding linked lists.
Move on to the next learning topic.
Later, go back and do another 2 or 3 linked list problems.
Do this with each new topic you learn.

Keep doing problems while you’re learning all this stuff, not after.

You’re not being hired for knowledge, but how you apply the knowledge.

There are many resources for this, listed below. Keep going.

4. Focus

There are a lot of distractions that can take up valuable time. Focus and concentration are hard. Turn on some music without lyrics and you’ll be able to focus pretty well.

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What you won’t see covered

These are prevalent technologies but not part of this study plan:

Javascript
HTML, CSS, and other front-end technologies
SQL

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The Daily Plan

This course goes over a lot of subjects. Each will probably take you a few days, or maybe even a week or more. It depends on your schedule.

Each day, take the next subject in the list, watch some videos about that subject, and then write an implementation of that data structure or algorithm in the language you chose for this course.

You can see my code here:

You don’t need to memorize every algorithm. You just need to be able to understand it enough to be able to write your own implementation.

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Coding Question Practice

Why is this here? I'm not ready to interview.

Then go back and read this.

Why you need to practice doing programming problems:

Problem recognition, and where the right data structures and algorithms fit in
Gathering requirements for the problem
Talking your way through the problem like you will in the interview
Coding on a whiteboard or paper, not a computer
Coming up with time and space complexity for your solutions (see Big-O below)
Testing your solutions

There is a great intro for methodical, communicative problem-solving in an interview. You’ll get this from the programming interview books, too, but I found this outstanding: Algorithm design canvas

Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then type it and test it out on a computer.

If you don’t have a whiteboard at home, pick up a large drawing pad from an art store. You can sit on the couch and practice. This is my “sofa whiteboard”. I added the pen in the photo just for scale. If you use a pen, you’ll wish you could erase. Gets messy quickly. I use a pencil and eraser.

Coding question practice is not about memorizing answers to programming problems.

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Coding Problems

Don’t forget your key coding interview books here.

Solving Problems:

Coding Interview Question Videos:

IDeserve (88 videos)
Tushar Roy (5 playlists)
- Super for walkthroughs of problem solutions
Nick White - LeetCode Solutions (187 Videos)
- Good explanations of the solution and the code
- You can watch several in a short time
FisherCoder - LeetCode Solutions

Challenge/Practice sites:

LeetCode
- My favorite coding problem site. It’s worth the subscription money for the 1-2 months you’ll likely be preparing.
- See Nick White and FisherCoder Videos above for code walk-throughs.
HackerRank
TopCoder
Codeforces
Codility
Geeks for Geeks
AlgoExpert
- Created by Google engineers, this is also an excellent resource to hone your skills.
Project Euler
- very math-focused, and not really suited for coding interviews

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Let’s Get Started

Alright, enough talk, let’s learn!

But don’t forget to do coding problems from above while you learn!

Algorithmic complexity / Big-O / Asymptotic analysis

Nothing to implement here, you’re just watching videos and taking notes! Yay!
There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.
Don’t worry if you don’t understand all the math behind it.
You just need to understand how to express the complexity of an algorithm in terms of Big-O.
Harvard CS50 - Asymptotic Notation (video)
Big O Notations (general quick tutorial) (video)
Big O Notation (and Omega and Theta) - best mathematical explanation (video)
Skiena (video)
UC Berkeley Big O (video)
Amortized Analysis (video)
TopCoder (includes recurrence relations and master theorem):
- Computational Complexity: Section 1
- Computational Complexity: Section 2
Cheat sheet
[Review] Analyzing Algorithms (playlist) in 18 minutes (video)

Well, that’s about enough of that.

When you go through “Cracking the Coding Interview”, there is a chapter on this, and at the end there is a quiz to see if you can identify the runtime complexity of different algorithms. It’s a super review and test.

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Data Structures

Arrays
- About Arrays:
  - Arrays CS50 Harvard University
  - Arrays (video)
  - UC Berkeley CS61B - Linear and Multi-Dim Arrays (video) (Start watching from 15m 32s)
  - Dynamic Arrays (video)
  - Jagged Arrays (video)
- Implement a vector (mutable array with automatic resizing):
  - Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
  - New raw data array with allocated memory
    - can allocate int array under the hood, just not use its features
    - start with 16, or if the starting number is greater, use power of 2 - 16, 32, 64, 128
  - size() - number of items
  - capacity() - number of items it can hold
  - is_empty()
  - at(index) - returns the item at a given index, blows up if index out of bounds
  - push(item)
  - insert(index, item) - inserts item at index, shifts that index’s value and trailing elements to the right
  - prepend(item) - can use insert above at index 0
  - pop() - remove from end, return value
  - delete(index) - delete item at index, shifting all trailing elements left
  - remove(item) - looks for value and removes index holding it (even if in multiple places)
  - find(item) - looks for value and returns first index with that value, -1 if not found
  - resize(new_capacity) // private function
    - when you reach capacity, resize to double the size
    - when popping an item, if the size is 1/4 of capacity, resize to half
- Time
  - O(1) to add/remove at end (amortized for allocations for more space), index, or update
  - O(n) to insert/remove elsewhere
- Space
  - contiguous in memory, so proximity helps performance
  - space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)
Linked Lists
- Description:
  - Linked Lists CS50 Harvard University - this builds the intuition.
  - Singly Linked Lists (video)
  - CS 61B - Linked Lists 1 (video)
  - CS 61B - Linked Lists 2 (video)
  - [Review] Linked lists in 4 minutes (video)
- C Code (video) - not the whole video, just portions about Node struct and memory allocation
- Linked List vs Arrays:
  - Core Linked Lists Vs Arrays (video)
  - In The Real World Linked Lists Vs Arrays (video)
- Why you should avoid linked lists (video)
- Gotcha: you need pointer to pointer knowledge: (for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don’t recommend this list traversal style. Readability and maintainability suffer due to cleverness.
  - Pointers to Pointers
- Implement (I did with tail pointer & without):
  - size() - returns the number of data elements in the list
  - empty() - bool returns true if empty
  - value_at(index) - returns the value of the nth item (starting at 0 for first)
  - push_front(value) - adds an item to the front of the list
  - pop_front() - remove the front item and return its value
  - push_back(value) - adds an item at the end
  - pop_back() - removes end item and returns its value
  - front() - get the value of the front item
  - back() - get the value of the end item
  - insert(index, value) - insert value at index, so the current item at that index is pointed to by the new item at the index
  - erase(index) - removes node at given index
  - value_n_from_end(n) - returns the value of the node at the nth position from the end of the list
  - reverse() - reverses the list
  - remove_value(value) - removes the first item in the list with this value
- Doubly-linked List
  - Description (video)
  - No need to implement
Stack
- Stacks (video)
- [Review] Stacks in 3 minutes (video)
- Will not implement. Implementing with the array is trivial
Queue
- Queue (video)
- Circular buffer/FIFO
- [Review] Queues in 3 minutes (video)
- Implement using linked-list, with tail pointer:
  - enqueue(value) - adds value at a position at the tail
  - dequeue() - returns value and removes least recently added element (front)
  - empty()
- Implement using a fixed-sized array:
  - enqueue(value) - adds item at end of available storage
  - dequeue() - returns value and removes least recently added element
  - empty()
  - full()
- Cost:
  - a bad implementation using a linked list where you enqueue at the head and dequeue at the tail would be O(n) because you’d need the next to last element, causing a full traversal of each dequeue
  - enqueue: O(1) (amortized, linked list and array [probing])
  - dequeue: O(1) (linked list and array)
  - empty: O(1) (linked list and array)
Hash table
- Videos:
- Online Courses:
  - Core Hash Tables (video)
  - Data Structures (video)
  - Phone Book Problem (video)
  - distributed hash tables:
    - Instant Uploads And Storage Optimization In Dropbox (video)
    - Distributed Hash Tables (video)
- Implement with array using linear probing
  - hash(k, m) - m is the size of the hash table
  - add(key, value) - if the key already exists, update value
  - exists(key)
  - get(key)
  - remove(key)

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More Knowledge

Binary search
- Binary Search (video)
- Binary Search (video)
- detail
- blueprint
- [Review] Binary search in 4 minutes (video)
- Implement:
  - binary search (on a sorted array of integers)
  - binary search using recursion
Bitwise operations
- Bits cheat sheet
  - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
- Get a really good understanding of manipulating bits with: &, |, ^, ~, », «
- 2s and 1s complement
- Count set bits
- Swap values:
  - Swap
- Absolute value:
  - Absolute Integer

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Trees

Trees - Intro
- Intro to Trees (video)
- Tree Traversal (video)
- BFS(breadth-first search) and DFS(depth-first search) (video)
  - BFS notes:
    - level order (BFS, using queue)
    - time complexity: O(n)
    - space complexity: best: O(1), worst: O(n/2)=O(n)
  - DFS notes:
    - time complexity: O(n)
    - space complexity: best: O(log n) - avg. height of tree worst: O(n)
    - inorder (DFS: left, self, right)
    - postorder (DFS: left, right, self)
    - preorder (DFS: self, left, right)
- [Review] Breadth-first search in 4 minutes (video)
- [Review] Depth-first search in 4 minutes (video)
- [Review] Tree Traversal (playlist) in 11 minutes (video)
Binary search trees: BSTs
- Binary Search Tree Review (video)
- Introduction (video)
- MIT (video)
- C/C++:
- Implement:
  - insert // insert value into tree
  - get_node_count // get count of values stored
  - print_values // prints the values in the tree, from min to max
  - delete_tree
  - is_in_tree // returns true if a given value exists in the tree
  - get_height // returns the height in nodes (single node’s height is 1)
  - get_min // returns the minimum value stored in the tree
  - get_max // returns the maximum value stored in the tree
  - is_binary_search_tree
  - delete_value
  - get_successor // returns the next-highest value in the tree after given value, -1 if none
Heap / Priority Queue / Binary Heap
- visualized as a tree, but is usually linear in storage (array, linked list)
- Heap
- Introduction (video)
- Binary Trees (video)
- Tree Height Remark (video)
- Basic Operations (video)
- Complete Binary Trees (video)
- Pseudocode (video)
- Heap Sort - jumps to start (video)
- Heap Sort (video)
- Building a heap (video)
- MIT 6.006 Introduction to Algorithms: Binary Heaps
- CS 61B Lecture 24: Priority Queues (video)
- Linear Time BuildHeap (max-heap)
- [Review] Heap (playlist) in 13 minutes (video)
- Implement a max-heap:
  - insert
  - sift_up - needed for insert
  - get_max - returns the max item, without removing it
  - get_size() - return number of elements stored
  - is_empty() - returns true if the heap contains no elements
  - extract_max - returns the max item, removing it
  - sift_down - needed for extract_max
  - remove(x) - removes item at index x
  - heapify - create a heap from an array of elements, needed for heap_sort
  - heap_sort() - take an unsorted array and turn it into a sorted array in place using a max heap or min heap

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Sorting

Notes:
- Implement sorts & know best case/worst case, average complexity of each:
  - no bubble sort - it’s terrible - O(n^2), except when n <= 16
- Stability in sorting algorithms (“Is Quicksort stable?”)
- Which algorithms can be used on linked lists? Which on arrays? Which of both?
  - I wouldn’t recommend sorting a linked list, but merge sort is doable.
  - Merge Sort For Linked List
For heapsort, see the Heap data structure above. Heap sort is great, but not stable
Sedgewick - Mergesort (5 videos)
Sedgewick - Quicksort (4 videos)
UC Berkeley:
Bubble Sort (video)
Analyzing Bubble Sort (video)
Insertion Sort, Merge Sort (video)
Insertion Sort (video)
Merge Sort (video)
Quicksort (video)
Selection Sort (video)
Merge sort code:
Quick sort code:
[Review] Sorting (playlist) in 18 minutes
Implement:
- Mergesort: O(n log n) average and worst case
- Quicksort O(n log n) average case
- Selection sort and insertion sort are both O(n^2) average and worst-case
- For heapsort, see Heap data structure above
Not required, but I recommended them:

As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see the “Sorting” section in Additional Detail on Some Subjects

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Graphs

Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting.

Notes:
- There are 4 basic ways to represent a graph in memory:
  - objects and pointers
  - adjacency matrix
  - adjacency list
  - adjacency map
- Familiarize yourself with each representation and its pros & cons
- BFS and DFS - know their computational complexity, their trade-offs, and how to implement them in real code
- When asked a question, look for a graph-based solution first, then move on if none
MIT(videos):
- Breadth-First Search
- Depth-First Search
Skiena Lectures - great intro:
Graphs (review and more):
Full Coursera Course:
- Algorithms on Graphs (video)
I’ll implement:
- DFS with adjacency list (recursive)
- DFS with adjacency list (iterative with stack)
- DFS with adjacency matrix (recursive)
- DFS with adjacency matrix (iterative with stack)
- BFS with adjacency list
- BFS with adjacency matrix
- single-source shortest path (Dijkstra)
- minimum spanning tree
- DFS-based algorithms (see Aduni videos above):
  - check for a cycle (needed for topological sort, since we’ll check for the cycle before starting)
  - topological sort
  - count connected components in a graph
  - list strongly connected components
  - check for bipartite graph

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Even More Knowledge

Recursion
- Stanford lectures on recursion & backtracking:
- When it is appropriate to use it?
- How is tail recursion better than not?
  - What Is Tail Recursion Why Is It So Bad?
  - Tail Recursion (video)
- 5 Simple Steps for Solving Any Recursive Problem(video)
Backtracking Blueprint: Java Python
Dynamic Programming
- You probably won’t see any dynamic programming problems in your interview, but it’s worth being able to recognize a problem as being a candidate for dynamic programming.
- This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
- I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
- Videos:
- Yale Lecture notes:
  - Dynamic Programming
- Coursera:
Design patterns
- Quick UML review (video)
- Learn these patterns:
  - strategy
  - singleton
  - adapter
  - prototype
  - decorator
  - visitor
  - factory, abstract factory
  - facade
  - observer
  - proxy
  - delegate
  - command
  - state
  - memento
  - iterator
  - composite
  - flyweight
- Series of videos (27 videos)
- Book: Head First Design Patterns
  - I know the canonical book is “Design Patterns: Elements of Reusable Object-Oriented Software”, but Head First is great for beginners to OO.
- Handy reference: 101 Design Patterns & Tips for Developers
Combinatorics (n choose k) & Probability
- Math Skills: How to find Factorial, Permutation, and Combination (Choose) (video)
- Make School: Probability (video)
- Make School: More Probability and Markov Chains (video)
- Khan Academy:
  - Course layout:
    - Basic Theoretical Probability
  - Just the videos - 41 (each are simple and each are short):
    - Probability Explained (video)
NP, NP-Complete and Approximation Algorithms
- Know about the most famous classes of NP-complete problems, such as the traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
- Know what NP-complete means.
- Computational Complexity (video)
- Simonson:
- Skiena:
- Complexity: P, NP, NP-completeness, Reductions (video)
- Complexity: Approximation Algorithms (video)
- Complexity: Fixed-Parameter Algorithms (video)
- Peter Norvig discusses near-optimal solutions to the traveling salesman problem:
  - Jupyter Notebook
- Pages 1048 - 1140 in CLRS if you have it.
How computers process a program
Caches
- LRU cache:
- CPU cache:
  - MIT 6.004 L15: The Memory Hierarchy (video)
  - MIT 6.004 L16: Cache Issues (video)
Processes and Threads
- Computer Science 162 - Operating Systems (25 videos):
  - for processes and threads see videos 1-11
  - Operating Systems and System Programming (video)
- What Is The Difference Between A Process And A Thread?
- Covers:
  - Processes, Threads, Concurrency issues
    - Difference between processes and threads
    - Processes
    - Threads
    - Locks
    - Mutexes
    - Semaphores
    - Monitors
    - How do they work?
    - Deadlock
    - Livelock
  - CPU activity, interrupts, context switching
  - Modern concurrency constructs with multicore processors
  - Paging, segmentation, and virtual memory (video)
  - Interrupts (video)
  - Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
  - Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own PC, stack counter, registers, and stack)
  - Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
  - Context switching
    - How context switching is initiated by the operating system and underlying hardware?
- threads in C++ (series - 10 videos)
- CS 377 Spring ‘14: Operating Systems from University of Massachusetts
- concurrency in Python (videos):
Testing
- To cover:
  - how unit testing works
  - what are mock objects
  - what is integration testing
  - what is dependency injection
- Agile Software Testing with James Bach (video)
- Open Lecture by James Bach on Software Testing (video)
- Steve Freeman - Test-Driven Development (that’s not what we meant) (video)
  - slides
- Dependency injection:
  - video
  - Tao Of Testing
- How to write tests
String searching & manipulations
If you need more detail on this subject, see the “String Matching” section in Additional Detail on Some Subjects.
Tries
- Note there are different kinds of tries. Some have prefixes, some don’t, and some use strings instead of bits to track the path
- I read through the code, but will not implement
- Sedgewick - Tries (3 videos)
- Notes on Data Structures and Programming Techniques
- Short course videos:
- The Trie: A Neglected Data Structure
- TopCoder - Using Tries
- Stanford Lecture (real-world use case) (video)
- MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)
Floating Point Numbers
- simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)
Unicode
- The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
- What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text
Endianness
- Big And Little Endian
- Big Endian Vs Little Endian (video)
- Big And Little Endian Inside/Out (video)
  - Very technical talk for kernel devs. Don’t worry if most is over your head.
  - The first half is enough.
Networking
- If you have networking experience or want to be a reliability engineer or operations engineer, expect questions
- Otherwise, this is just good to know
- Khan Academy
- UDP and TCP: Comparison of Transport Protocols (video)
- TCP/IP and the OSI Model Explained! (video)
- Packet Transmission across the Internet. Networking & TCP/IP tutorial. (video)
- HTTP (video)
- SSL and HTTPS (video)
- SSL/TLS (video)
- HTTP 2.0 (video)
- Video Series (21 videos) (video)
- Subnetting Demystified - Part 5 CIDR Notation (video)
- Sockets:
  - Java - Sockets - Introduction (video)
  - Socket Programming (video)

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Final Review

This section will have shorter videos that you can watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.

Series of 2-3 minutes short subject videos (23 videos)
- Videos
Series of 2-5 minutes short subject videos - Michael Sambol (48 videos):
- Videos
- Code Examples
Sedgewick Videos - Algorithms I
Sedgewick Videos - Algorithms II

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Update Your Resume

See Resume prep information in the books: “Cracking The Coding Interview” and “Programming Interviews Exposed”
“This Is What A GOOD Resume Should Look Like” by Gayle McDowell (author of Cracking the Coding Interview),
- Note by the author: “This is for a US-focused resume. CVs for India and other countries have different expectations, although many of the points will be the same.”
“Step-by-step resume guide” by Tech Interview Handbook
- Detailed guide on how to set up your resume from scratch, write effective resume content, optimize it, and test your resume

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Interview Process & General Interview Prep

How to Pass the Engineering Interview in 2021
Demystifying Tech Recruiting
How to Get a Job at the Big 4:
- How to Get a Job at the Big 4 - Amazon, Facebook, Google & Microsoft (video)
- How to Get a Job at the Big 4.1 (Follow-up video)
Cracking The Coding Interview Set 1:
- Gayle L McDowell - Cracking The Coding Interview (video)
- Cracking the Coding Interview with Author Gayle Laakmann McDowell (video)
Cracking the Facebook Coding Interview:
- The Approach
- Problem Walkthrough
Prep Courses:
- Python for Data Structures, Algorithms, and Interviews (paid course):
  - A Python-centric interview prep course that covers data structures, algorithms, mock interviews, and much more.
- Intro to Data Structures and Algorithms using Python (Udacity free course):
  - A free Python-centric data structures and algorithms course.
- Data Structures and Algorithms Nanodegree! (Udacity paid Nanodegree):
  - Get hands-on practice with over 100 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and on-the-job scenarios.
- Grokking the Behavioral Interview (Educative free course):
  - Many times, it’s not your technical competency that holds you back from landing your dream job, it’s how you perform on the behavioral interview.
- AlgoMonster (paid course with free content):
  - The crash course for LeetCode. Covers all the patterns condensed from thousands of questions.

Mock Interviews:

Gainlo.co: Mock interviewers from big companies - I used this and it helped me relax for the phone screen and on-site interview
Pramp: Mock interviews from/with peers - a peer-to-peer model to practice interviews
interviewing.io: Practice mock interview with senior engineers - anonymous algorithmic/systems design interviews with senior engineers from FAANG anonymously
Meetapro: Mock interviews with top FAANG interviewers - an Airbnb-style mock interview/coaching platform.
Hello Interview: Mock Interviews with Expert Coaches and AI - interview directly with AI or with FAANG staff engineers and managers.
Codemia: Practice system design problems with AI or community solutions and feedback - Practice system design problems via AI practice tool. Share your solution with the community to get human feedback as well.

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Be thinking of for when the interview comes

Think of about 20 interview questions you’ll get, along with the lines of the items below. Have at least one answer for each. Have a story, not just data, about something you accomplished.

Why do you want this job?
What’s a tough problem you’ve solved?
Biggest challenges faced?
Best/worst designs seen?
Ideas for improving an existing product
How do you work best, as an individual and as part of a team?
Which of your skills or experiences would be assets in the role and why?
What did you most enjoy at [job x / project y]?
What was the biggest challenge you faced at [job x / project y]?
What was the hardest bug you faced at [job x / project y]?
What did you learn at [job x / project y]?
What would you have done better at [job x / project y]?

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Have questions for the interviewer

Some of mine (I already may know the answers, but want their opinion or team perspective):

How large is your team?
What does your dev cycle look like? Do you do waterfall/sprints/agile?
Are rushes to deadlines common? Or is there flexibility?
How are decisions made in your team?
How many meetings do you have per week?
Do you feel your work environment helps you concentrate?
What are you working on?
What do you like about it?
What is the work life like?
How is the work/life balance?

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Once You’ve Got The Job

Congratulations!

Keep learning.

You’re never really done.

*****************************************************************************************************
*****************************************************************************************************

Everything below this point is optional. It is NOT needed for an entry-level interview.
However, by studying these, you'll get greater exposure to more CS concepts and will be better prepared for
any software engineering job. You'll be a much more well-rounded software engineer.

*****************************************************************************************************
*****************************************************************************************************

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Additional Books

These are here so you can dive into a topic you find interesting.

The Unix Programming Environment
- An oldie but a goodie
The Linux Command Line: A Complete Introduction
- A modern option
TCP/IP Illustrated Series
Head First Design Patterns
- A gentle introduction to design patterns
Design Patterns: Elements of Reusable Object-Oriented Software
- AKA the “Gang Of Four” book or GOF
- The canonical design patterns book
Algorithm Design Manual (Skiena)
- As a review and problem-recognition
- The algorithm catalog portion is well beyond the scope of difficulty you’ll get in an interview
- This book has 2 parts:
  - Class textbook on data structures and algorithms
    - Pros:
      - Is a good review as any algorithms textbook would be
      - Nice stories from his experiences solving problems in industry and academia
      - Code examples in C
    - Cons:
      - Can be as dense or impenetrable as CLRS, and in some cases, CLRS may be a better alternative for some subjects
      - Chapters 7, 8, and 9 can be painful to try to follow, as some items are not explained well or require more brain than I have
      - Don’t get me wrong: I like Skiena, his teaching style, and mannerisms, but I may not be Stony Brook material
  - Algorithm catalog:
    - This is the real reason you buy this book.
    - This book is better as an algorithm reference, and not something you read cover to cover.
- Can rent it on Kindle
- Answers:
  - Solutions
- Errata
Algorithm (Jeff Erickson)
Write Great Code: Volume 1: Understanding the Machine
- The book was published in 2004, and is somewhat outdated, but it’s a terrific resource for understanding a computer in brief
- The author invented HLA, so take mentions and examples in HLA with a grain of salt. Not widely used, but decent examples of what assembly looks like
- These chapters are worth the read to give you a nice foundation:
  - Chapter 2 - Numeric Representation
  - Chapter 3 - Binary Arithmetic and Bit Operations
  - Chapter 4 - Floating-Point Representation
  - Chapter 5 - Character Representation
  - Chapter 6 - Memory Organization and Access
  - Chapter 7 - Composite Data Types and Memory Objects
  - Chapter 9 - CPU Architecture
  - Chapter 10 - Instruction Set Architecture
  - Chapter 11 - Memory Architecture and Organization
Introduction to Algorithms
- Important: Reading this book will only have limited value. This book is a great review of algorithms and data structures, but won’t teach you how to write good code. You have to be able to code a decent solution efficiently
- AKA CLR, sometimes CLRS, because Stein was late to the game
Computer Architecture, Sixth Edition: A Quantitative Approach
- For a richer, more up-to-date (2017), but longer treatment

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System Design, Scalability, Data Handling

You can expect system design questions if you have 4+ years of experience.

Scalability and System Design are very large topics with many topics and resources, since there is a lot to consider when designing a software/hardware system that can scale. Expect to spend quite a bit of time on this
Considerations:
- Scalability
  - Distill large data sets to single values
  - Transform one data set to another
  - Handling obscenely large amounts of data
- System design
  - features sets
  - interfaces
  - class hierarchies
  - designing a system under certain constraints
  - simplicity and robustness
  - tradeoffs
  - performance analysis and optimization
START HERE: The System Design Primer
System Design from HiredInTech
How Do I Prepare To Answer Design Questions In A Technical Interview?
8 steps guide to ace your system design interview
Database Normalization - 1NF, 2NF, 3NF and 4NF (video)
System Design Interview - There are a lot of resources in this one. Look through the articles and examples. I put some of them below
How to ace a systems design interview
Numbers Everyone Should Know
How long does it take to make a context switch?
Transactions Across Datacenters (video)
A plain English introduction to CAP Theorem
MIT 6.824: Distributed Systems, Spring 2020 (20 videos)
Consensus Algorithms:
- Paxos - Paxos Agreement - Computerphile (video)
- Raft - An Introduction to the Raft Distributed Consensus Algorithm (video)
  - Easy-to-read paper
  - Infographic
Consistent Hashing
NoSQL Patterns
Scalability:
- You don’t need all of these. Just pick a few that interest you.
- Great overview (video)
- Short series:
  - Clones
  - Database
  - Cache
  - Asynchronism
- Scalable Web Architecture and Distributed Systems
- Fallacies of Distributed Computing Explained
- Jeff Dean - Building Software Systems At Google and Lessons Learned (video)
- Introduction to Architecting Systems for Scale
- Scaling mobile games to a global audience using App Engine and Cloud Datastore (video)
- How Google Does Planet-Scale Engineering for Planet-Scale Infra (video)
- The Importance of Algorithms
- Sharding
- Engineering for the Long Game - Astrid Atkinson Keynote(video)
- 7 Years Of YouTube Scalability Lessons In 30 Minutes
  - video
- How PayPal Scaled To Billions Of Transactions Daily Using Just 8VMs
- How to Remove Duplicates in Large Datasets
- A look inside Etsy’s scale and engineering culture with Jon Cowie (video)
- What Led Amazon to its Own Microservices Architecture
- To Compress Or Not To Compress, That Was Uber’s Question
- When Should Approximate Query Processing Be Used?
- Google’s Transition From Single Datacenter To Failover, To A Native Multihomed Architecture
- The Image Optimization Technology That Serves Millions Of Requests Per Day
- A Patreon Architecture Short
- Tinder: How Does One Of The Largest Recommendation Engines Decide Who You’ll See Next?
- Design Of A Modern Cache
- Live Video Streaming At Facebook Scale
- A Beginner’s Guide To Scaling To 11 Million+ Users On Amazon’s AWS
- A 360 Degree View Of The Entire Netflix Stack
- Latency Is Everywhere And It Costs You Sales - How To Crush It
- What Powers Instagram: Hundreds of Instances, Dozens of Technologies
- Salesforce Architecture - How They Handle 1.3 Billion Transactions A Day
- ESPN’s Architecture At Scale - Operating At 100,000 Duh Nuh Nuhs Per Second
- See “Messaging, Serialization, and Queueing Systems” way below for info on some of the technologies that can glue services together
- Twitter:
  - O’Reilly MySQL CE 2011: Jeremy Cole, “Big and Small Data at @Twitter” (video)
  - Timelines at Scale
- For even more, see the “Mining Massive Datasets” video series in the Video Series section
Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
- review: The System Design Primer
- System Design from HiredInTech
- cheat sheet
- flow:
  1. Understand the problem and scope:
    - Define the use cases, with the interviewer’s help
    - Suggest additional features
    - Remove items that the interviewer deems out of scope
    - Assume high availability is required, add as a use case
  2. Think about constraints:
    - Ask how many requests per month
    - Ask how many requests per second (they may volunteer it or make you do the math)
    - Estimate reads vs. writes percentage
    - Keep the 80/20 rule in mind when estimating
    - How much data is written per second
    - Total storage required over 5 years
    - How much data read per second
  3. Abstract design:
    - Layers (service, data, caching)
    - Infrastructure: load balancing, messaging
    - Rough overview of any key algorithm that drives the service
    - Consider bottlenecks and determine solutions
- Exercises:

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Additional Learning

I added them to help you become a well-rounded software engineer and to be aware of certain
technologies and algorithms, so you'll have a bigger toolbox.

Compilers
- How a Compiler Works in ~1 minute (video)
- Harvard CS50 - Compilers (video)
- C++ (video)
- Understanding Compiler Optimization (C++) (video)
Emacs and vi(m)
- Familiarize yourself with a UNIX-based code editor
- vi(m):
- emacs:
- The Absolute Beginner’s Guide to Emacs (video by David Wilson)
- The Absolute Beginner’s Guide to Emacs (notes by David Wilson)
Unix/Linux command line tools
- I filled in the list below from good tools.
- bash
- cat
- grep
- sed
- awk
- curl or wget
- sort
- tr
- uniq
- strace
- tcpdump
- Essential Linux Commands Tutorial
DevOps
- DevOps Roadmap
Information theory (videos)
- Khan Academy
- More about Markov processes:
- See more in the MIT 6.050J Information and Entropy series below
Parity & Hamming Code (videos)
- Intro
- Parity
- Hamming Code:
  - Error detection
  - Error correction
- Error Checking
Entropy
- Also see the videos below
- Make sure to watch information theory videos first
- Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)
Cryptography
- Also see the videos below
- Make sure to watch information theory videos first
- Khan Academy Series
- Cryptography: Hash Functions
- Cryptography: Encryption
Compression
- Make sure to watch information theory videos first
- Computerphile (videos):
- Compressor Head videos
- (optional) Google Developers Live: GZIP is not enough!
Computer Security
- MIT (23 videos)
Garbage collection
- GC in Python (video)
- Deep Dive Java: Garbage Collection is Good!
- Deep Dive Python: Garbage Collection in CPython (video)
Parallel Programming
- Coursera (Scala)
- Efficient Python for High-Performance Parallel Computing (video)
Messaging, Serialization, and Queueing Systems
- Thrift
  - Tutorial
- Protocol Buffers
  - Tutorials
- gRPC
  - gRPC 101 for Java Developers (video)
- Redis
  - Tutorial
- Amazon SQS (queue)
- Amazon SNS (pub-sub)
- RabbitMQ
  - Get Started
- Celery
  - First Steps With Celery
- ZeroMQ
  - Intro - Read The Manual
- ActiveMQ
- Kafka
- MessagePack
- Avro
A*
- A Search Algorithm
- A* Pathfinding (E01: algorithm explanation) (video)
Fast Fourier Transform
- An Interactive Guide To The Fourier Transform
- What is a Fourier transform? What is it used for?
- What is the Fourier Transform? (video)
- Divide & Conquer: FFT (video)
- Understanding The FFT
Bloom Filter
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
- Bloom Filters (video)
- Bloom Filters | Mining of Massive Datasets | Stanford University (video)
- Tutorial
- How To Write A Bloom Filter App
HyperLogLog
- How To Count A Billion Distinct Objects Using Only 1.5KB Of Memory
Locality-Sensitive Hashing
- Used to determine the similarity of documents
- The opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same
- Simhashing (hopefully) made simple
van Emde Boas Trees
- Divide & Conquer: van Emde Boas Trees (video)
- MIT Lecture Notes
Augmented Data Structures
- CS 61B Lecture 39: Augmenting Data Structures
Balanced search trees
- Know at least one type of balanced binary tree (and know how it’s implemented):
- “Among balanced search trees, AVL and 2/3 trees are now passé and red-black trees seem to be more popular. A particularly interesting self-organizing data structure is the splay tree, which uses rotations to move any accessed key to the root.” - Skiena
- Of these, I chose to implement a splay tree. From what I’ve read, you won’t implement a balanced search tree in your interview. But I wanted exposure to coding one up and let’s face it, splay trees are the bee’s knees. I did read a lot of red-black tree code
  - Splay tree: insert, search, delete functions If you end up implementing a red/black tree try just these:
  - Search and insertion functions, skipping delete
- I want to learn more about B-Tree since it’s used so widely with very large data sets
- Self-balancing binary search tree
- AVL trees
  - In practice: From what I can tell, these aren’t used much in practice, but I could see where they would be: The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it attractive for data structures that may be built once and loaded without reconstruction, such as language dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter)
  - MIT AVL Trees / AVL Sort (video)
  - AVL Trees (video)
  - AVL Tree Implementation (video)
  - Split And Merge
  - [Review] AVL Trees (playlist) in 19 minutes (video)
- Splay trees
  - In practice: Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors, data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory, networking and file system code) etc
  - CS 61B: Splay Trees (video)
  - MIT Lecture: Splay Trees:
    - Gets very mathy, but watch the last 10 minutes for sure.
    - Video
- Red/black trees
  - These are a translation of a 2-3 tree (see below).
  - In practice: Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time. Not only does this make them valuable in time-sensitive applications such as real-time applications, but it makes them valuable building blocks in other data structures that provide worst-case guarantees; for example, many data structures used in computational geometry can be based on red-black trees, and the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In version 8 of Java, the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor hashcodes, a Red-Black tree is used
  - Aduni - Algorithms - Lecture 4 (link jumps to the starting point) (video)
  - Aduni - Algorithms - Lecture 5 (video)
  - Red-Black Tree
  - An Introduction To Binary Search And Red Black Tree
  - [Review] Red-Black Trees (playlist) in 30 minutes (video)
- 2-3 search trees
  - In practice: 2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
  - You would use 2-3 trees very rarely because its implementation involves different types of nodes. Instead, people use Red-Black trees.
  - 23-Tree Intuition and Definition (video)
  - Binary View of 23-Tree
  - 2-3 Trees (student recitation) (video)
- 2-3-4 Trees (aka 2-4 trees)
  - In practice: For every 2-4 trees, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an important tool for understanding the logic behind red-black trees, and this is why many introductory algorithm texts introduce 2-4 trees just before red–black trees, even though 2-4 trees are not often used in practice.
  - CS 61B Lecture 26: Balanced Search Trees (video)
  - Bottom Up 234-Trees (video)
  - Top Down 234-Trees (video)
- N-ary (K-ary, M-ary) trees
  - note: the N or K is the branching factor (max branches)
  - binary trees are a 2-ary tree, with branching factor = 2
  - 2-3 trees are 3-ary
  - K-Ary Tree
- B-Trees
  - Fun fact: it’s a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor).
  - In Practice: B-trees are widely used in databases. Most modern filesystems use B-trees (or Variants). In addition to its use in databases, the B-tree is also used in filesystems to allow quick random access to an arbitrary block in a particular file. The basic problem is turning the file block address into a disk block (or perhaps to a cylinder head sector) address
  - B-Tree
  - B-Tree Datastructure
  - Introduction to B-Trees (video)
  - B-Tree Definition and Insertion (video)
  - B-Tree Deletion (video)
  - MIT 6.851 - Memory Hierarchy Models (video) - covers cache-oblivious B-Trees, very interesting data structures - the first 37 minutes are very technical, and may be skipped (B is block size, cache line size)
  - [Review] B-Trees (playlist) in 26 minutes (video)
k-D Trees
- Great for finding a number of points in a rectangle or higher-dimensional object
- A good fit for k-nearest neighbors
- kNN K-d tree algorithm (video)
Skip lists
- “These are somewhat of a cult data structure” - Skiena
- Randomization: Skip Lists (video)
- For animations and a little more detail
Network Flows
- Ford-Fulkerson in 5 minutes — Step by step example (video)
- Ford-Fulkerson Algorithm (video)
- Network Flows (video)
Disjoint Sets & Union Find
- UCB 61B - Disjoint Sets; Sorting & selection (video)
- Sedgewick Algorithms - Union-Find (6 videos)
Math for Fast Processing
- Integer Arithmetic, Karatsuba Multiplication (video)
- The Chinese Remainder Theorem (used in cryptography) (video)
Treap
- Combination of a binary search tree and a heap
- Treap
- Data Structures: Treaps explained (video)
- Applications in set operations
Linear Programming (videos)
- Linear Programming
- Finding minimum cost
- Finding maximum value
- Solve Linear Equations with Python - Simplex Algorithm
Geometry, Convex hull (videos)
- Graph Alg. IV: Intro to geometric algorithms - Lecture 9
- Geometric Algorithms: Graham & Jarvis - Lecture 10
- Divide & Conquer: Convex Hull, Median Finding
Discrete math
- Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory
- Discrete Mathematics by Shai Simonson (19 videos)
- Discrete Mathematics By IIT Ropar NPTEL

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Additional Detail on Some Subjects

I added these to reinforce some ideas already presented above, but didn't want to include them
above because it's just too much. It's easy to overdo it on a subject.
You want to get hired in this century, right?

SOLID
- Bob Martin SOLID Principles of Object Oriented and Agile Design (video)
- S - Single Responsibility Principle | Single responsibility to each Object
  - more flavor
- O - Open/Closed Principle | On production level Objects are ready for extension but not for modification
  - more flavor
- L - Liskov Substitution Principle | Base Class and Derived class follow ‘IS A’ Principle
  - more flavor
- I - Interface segregation principle | Clients should not be forced to implement interfaces they don’t use
  - Interface Segregation Principle in 5 minutes (video)
  - more flavor
- D -Dependency Inversion principle | Reduce the dependency In composition of objects.
  - Why Is The Dependency Inversion Principle And Why Is It Important
  - more flavor
Union-Find
More Dynamic Programming (videos)
Advanced Graph Processing (videos)
- Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
- Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):
Simonson: Approximation Algorithms (video)
String Matching
- Rabin-Karp (videos):
- Knuth-Morris-Pratt (KMP):
  - TThe Knuth-Morris-Pratt (KMP) String Matching Algorithm
- Boyer–Moore string search algorithm
  - Boyer-Moore String Search Algorithm
  - Advanced String Searching Boyer-Moore-Horspool Algorithms (video)
- Coursera: Algorithms on Strings
  - starts off great, but by the time it gets past KMP it gets more complicated than it needs to be
  - nice explanation of tries
  - can be skipped
Sorting
- Stanford lectures on sorting:
  - Lecture 15 | Programming Abstractions (video)
  - Lecture 16 | Programming Abstractions (video)
- Shai Simonson:
  - Algorithms - Sorting - Lecture 2 (video)
  - Algorithms - Sorting II - Lecture 3 (video)
- Steven Skiena lectures on sorting:
  - CSE373 2020 - Mergesort/Quicksort (video)
  - CSE373 2020 - Linear Sorting (video)
NAND To Tetris: Build a Modern Computer from First Principles

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Video Series

Sit back and enjoy.

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Computer Science Courses

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Algorithms implementation

Multiple Algorithms implementation by Princeton University

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Papers

Love classic papers?
1978: Communicating Sequential Processes
- implemented in Go
2003: The Google File System
- replaced by Colossus in 2012
2004: MapReduce: Simplified Data Processing on Large Clusters
- mostly replaced by Cloud Dataflow?
2006: Bigtable: A Distributed Storage System for Structured Data
2006: The Chubby Lock Service for Loosely-Coupled Distributed Systems
2007: Dynamo: Amazon’s Highly Available Key-value Store
- The Dynamo paper kicked off the NoSQL revolution
2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)
2012: AddressSanitizer: A Fast Address Sanity Checker:
- paper
- video
2013: Spanner: Google’s Globally-Distributed Database:
- paper
- video
2015: Continuous Pipelines at Google
2015: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
2015: How Developers Search for Code: A Case Study
More papers: 1,000 papers

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LICENSE

CC-BY-SA-4.0

awesome-aws

Mon, 23 Jun 2025 15:32:45 +0800

donnemartin/awesome-aws

Awesome AWS

A curated list of awesome AWS libraries, open source repos, guides, blogs, and other resources.

Inspired by the awesome list.

The Fiery Meter of AWSome

Repo with 0100+ Stars: :fire:
Repo with 0200+ Stars: :fire::fire:
Repo with 0500+ Stars: :fire::fire::fire:
Repo with 1000+ Stars: :fire::fire::fire::fire:
Repo with 2000+ Stars: :fire::fire::fire::fire::fire:

Repos not on The Fiery Meter of AWSome can still be awesome, see A Note on Repo AWSomeness.

`awesome-aws` Python Module

The Python module awesome-aws regularly scans repos on Awesome AWS to maintain the accuracy of the Fiery Meter of AWSome.

Contributing

Contributions are welcome!

Review the Contributing Guidelines.

Also check out the Watch List.

Index

SDKs and Samples

AWS and community SDKs with samples and docs, grouped by language.

Android SDK

C++ SDK

The C++ SDK is a labs project with limited docs and/or samples.

Clojure SDK

The Clojure SDK is a community project with limited docs and/or samples.)

Go SDK

Related Repos:

goamz/goamz :fire::fire:

iOS SDK

IoT SDK

The IoT SDK is a labs project with limited docs and/or samples.

Java SDK

JavaScript SDK

Related Repos:

Haskell SDK

Related Repos:

aristidb/aws :fire::fire:

The Haskell SDK is a community project with limited docs and/or samples.

Perl SDK

The Perl SDK is a community project.

PHP SDK

Related Repos:

Python SDK

Related Repos:

Ruby SDK

Related Repos:

Rust SDK

The Rust SDK is a community project with limited docs and/or samples.

Scala SDK

Repo

Related Repos:

The Scala SDK is a labs project with limited docs and/or samples.

Unity SDK

Xamarin SDK

The Xamarin SDK is a labs project with limited docs and/or samples.

.NET SDK

Command Line Tools

AWS and community command line tools with samples and docs.

Universal Command Line Interface

Related Repos:

Windows PowerShell

IDE Toolkits

Official IDE toolkits with samples and docs.

Eclipse Toolkit

Visual Studio Toolkit

Open Source Repos

AWS and community open source projects, grouped by service. See A Note on Repo AWSomeness for more details.

API Gateway

AWS Repos:

api-gateway-secure-pet-store :fire::fire: - Cognito credentials through Lambda.
aws-apigateway-sdk-java - SDK for Java.
aws-apigateway-swagger-importer :fire::fire::fire: - Tools to work with Swagger.

Community Repos:

Contribute

CLI

AWS Repos:

awscli-aliases :fire::fire: - Repository for AWS CLI aliases.
amazon-ecs-cli :fire::fire::fire::fire: - ECS CLI using the same Docker Compose file format and familiar Compose commands.
aws-cli :fire::fire::fire::fire::fire: - Universal Command Line Interface.
aws-shell :fire::fire::fire::fire::fire:
awscli-cookbook - Installs the CLI tools and provides a set of LWRPs for use within chef cookbooks.
awsmobile-cli :fire: - CLI experience for Frontend developers in the JavaScript ecosystem.

Community Repos:

achiku/jungle :fire::fire::fire: - Operations by EC2 and ELB cli should be simpler.
dbcli/athenacli :fire: - a CLI tool for AWS Athena service that can do auto-completion and syntax highlighting.
donnemartin/saws :fire::fire::fire::fire::fire: - A Supercharged AWS Command Line Interface.
timkay/aws :fire::fire: - Easy command line access to Amazon EC2, S3, SQS, ELB, and SDB.
wallix/awless :fire::fire::fire::fire::fire: - a Powerful CLI for EC2, IAM and S3 in Go.
99designs/aws-vault :fire::fire::fire::fire::fire: - A tool for securely storing AWS credentials, written in Go.

CloudFormation

AWS Repos:

aws-cdk :fire::fire::fire::fire::fire: - Framework for defining cloud infrastructure in code.
aws-cfn-custom-resource-examples - Custom resource examples.
aws-cfn-resource-bridge - Custom resource framework.
cfn-python-lint :fire::fire::fire::fire::fire: - A tool for linting/validating CloudFormation.
cfncluster-cookbook - Sample Cookbook.
cfncluster :fire::fire::fire: - Framework that deploys and maintains HPC clusters.

Community Repos:

Appliscale/perun - A CLI tool for linting/validation and managing CloudFormation templates and stacks.
bazaarvoice/cloudformation-ruby-dsl :fire::fire: - Ruby DSL for creating templates.
beaknit/cform :fire: - SublimeText plugin.
cloudreach/sceptre :fire::fire::fire::fire: - A CLI tool for automating CloudFormation.
cloudtools/troposphere :fire::fire::fire::fire::fire: - Python library to create descriptions.
peterkh/cumulus :fire::fire: - Manages stacks.
envato/stack_master :fire::fire: - A CLI tool to manage CloudFormation stacks.
sparkleformation/sfn - CLI for stack management.
sparkleformation/sparkle_formation :fire::fire: - Ruby DSL for template creation.
Stelligent/cfn_nag :fire::fire::fire::fire: - Linting tool for CloudFormation templates

CloudSearch

AWS Repos:

cloudsearchable - An ActiveRecord-style ORM query interface.

Community Repos:

Contribute

CloudTrail

AWS Repos:

aws-cloudtrail-processing-library - Easily consume and process log files.

Community Repos:

AppliedTrust/traildash :fire::fire: - Slick dashboard.
GorillaStack/auto-tag :fire::fire: - Automatically tag AWS resources on creation, for cost assignment.

CloudWatch

AWS Repos:

cloudwatch-logs-subscription-consumer :fire::fire: - Kinesis stream reader.
ecs-cloudwatch-logs - Assets in the blog post on using Amazon ECS and Amazon CloudWatch logs.
logstash-output-cloudwatchlogs - A logstash plugin that sends logs to CloudWatch.
opsworks-cloudwatch-logs-cookbooks - OpsWorks sample cookbook.

Community Repos:

jorgebastida/awslogs :fire::fire::fire::fire::fire: - Simple CLI for querying groups, streams and events.
newrelic-platform/newrelic_aws_cloudwatch_plugin :fire: - New Relic plugin.

Code Deploy

AWS Repos:

aws-codedeploy-agent :fire::fire: - Sample agent.
aws-codedeploy-plugin :fire: - Jenkins plugin.
aws-codedeploy-samples :fire::fire::fire: - Samples and template scenarios.

Community Repos:

Contribute

Code Pipeline

AWS Repos:

aws-codepipeline-custom-job-worker - Develop your own job worker when creating a custom action.
aws-codepipeline-jenkins-aws-codedeploy_linux - Four-stage pipeline for Linux.
aws-codepipeline-plugin-for-jenkins - Jenkins plugin.
aws-codepipeline-s3-aws-codedeploy_linux :fire: - Simple pipeline for Linux.
AWSCodePipeline-Jenkins-AWSCodeDeploy_Windows - Four-stage pipeline for Windows.
AWSCodePipeline-S3-AWSCodeDeploy_Windows - Simple pipeline for Windows.

Community Repos:

Contribute

Cognito

AWS Repos:

amazon-cognito-android - Sync SDK for Android.
amazon-cognito-developer-authentication-sample - Authentication sample.
amazon-cognito-dotnet - Sync SDK for .NET.
amazon-cognito-ios - Sync SDK for iOS.
amazon-cognito-js :fire::fire: - Sync SDK for JavaScript.
amazon-cognito-streams-sample - Consuming Streams sample.
cognito-sample-nodejs :fire: - Sample App for Node.js.

Community Repos:

capeless/warrant :fire::fire: - Python library for using Cognito.
rahulpsd18/cognito-backup-restore :fire: - Tool for backing up and restoring Cognito user pools.

Data Pipeline

AWS Repos:

data-pipeline-samples :fire::fire: - Sample pipelines.

Community Repos:

Contribute

Device Farm

AWS Repos:

aws-device-farm-appium-tests-for-sample-app - Appium TestNG Android tests.
aws-device-farm-calabash-tests-for-sample-app - Calabash Android tests.
aws-device-farm-gradle-plugin - Gradle plugin.
aws-device-farm-jenkins-plugin - Jenkins plugin.
aws-device-farm-sample-app-for-android :fire: - Sample Android app.

Community Repos:

Contribute

DynamoDB

AWS Repos:

aws-dotnet-session-provider - A session state provider for ASP.NET apps.
aws-dotnet-trace-listener - A trace listener for System.Diagnostics that can be used to log events.
aws-dynamodb-encryption-java :fire: - Encryption Client for Java.
aws-dynamodb-examples :fire::fire: - Samples using the Java SDK.
aws-dynamodb-mars-json-demo - Stores and indexes NASA JPL Mars images.
aws-dynamodb-session-tomcat - Session store for Apache Tomcat.
aws-sessionstore-dynamodb-ruby - Handles sessions for Ruby web apps.
dynamodb-cross-region-library :fire::fire: - Cross-region replication.
dynamodb-geo :fire::fire: - Library to create and query geospatial data.
dynamodb-import-export-tool - Import and export examples.
dynamodb-online-index-violation-detector - Finds violations on an online GSI’s hash key and range key.
dynamodb-streams-kinesis-adapter - Kinesis interface to consume and process data from a DynamoDB stream.
dynamodb-tictactoe-example-app - Lightweight python app.
dynamodb-titan-storage-backend :fire::fire: - Storage Backend for Titan.
dynamodb-transactions :fire::fire: - Performs atomic writes and isolated reads across multiple items and tables.
logstash-input-dynamodb :fire: - Logstash input plugin.

Community Repos:

channl/dynamodb-lambda-autoscale :fire::fire: - Autoscale DynamoDB provisioned capacity using Lambda.
lyft/confidant :fire::fire::fire::fire: - Stores secrets, encrypted at rest.
sebdah/dynamic-dynamodb :fire::fire::fire: - Provides auto-scaling.
sensedeep/dynamodb-onetable :fire::fire::fire: - DynamoDB library for single table designs using NodeJS.

Elastic Beanstalk

AWS Repos:

aws-eb-glassfish-dockerfiles - GlassFish docker files.
aws-eb-python-dockerfiles - Python docker files.
eb-demo-php-simple-app :fire: - Simple PHP app.
eb-docker-multiple-ports - Simple Node.js and Tomcat apps using Docker images.
eb-docker-nginx-proxy :fire: - Simple PHP app using the PHP-FPM and Nginx Docker images.
eb-docker-virtual-hosting - Simple PHP, Tomcat, and Nginx applications using Docker images.
eb-node-express-sample :fire::fire: - Sample express app.
eb-node-express-signup - Express framework and Bootstrap Node.js sample app.
eb-node-express - Sample app referenced in the Developer Guide.
eb-py-flask-signup-worker - Python app that illustrates worker roles.
eb-py-flask-signup :fire::fire: - Python signup form app with Flask and Bootstrap.
eb-python-flask - Simple Python and Flask app.
eb-wif-sample - Sample login app with Web Identity Federation.

Community Repos:

alienfast/elastic-beanstalk :fire: - Gem with rake configuration and deployment for rails apps.
ThoughtWorksStudios/eb_deployer :fire::fire: - Blue-green deployment automation.

Elastic Compute Cloud

AWS Repos:

Contribute

Community Repos:

alestic/ec2-consistent-snapshot :fire::fire: - Initiate consistent EBS snapshots in EC2.
ConradIrwin/aws-name-server :fire::fire::fire: - DNS server that lets you look up instances by name.
cristim/autospotting :fire::fire::fire::fire::fire: - Automatically rolling-replace on-demand EC2 instances in AutoScaling groups with compatible spot instances.
evannuil/aws-snapshot-tool :fire::fire: - Automates EBS snapshots and rotation.
kelseyhightower/kubernetes-the-hard-way :fire::fire::fire::fire::fire: - Bootstrap Kubernetes the hard way on EC2. No scripts.
mirakui/ec2ssh :fire::fire: - SSH config manager.
openebs/openebs :fire::fire::fire::fire::fire: - Containerized block storage QoS SLAs, tiering and replica policies across AZs and environments, and predictable and scalable performance.
skavanagh/EC2Box :fire::fire: - A web-based SSH console to manage multiple instances simultaneously.
wbailey/claws :fire: - CLI-driven console with capistrano integration.

Elastic Container Service

AWS Repos:

amazon-ecs-agent :fire::fire::fire::fire: - Agent that runs on and starts containers.
amazon-ecs-amazon-efs - Persists Data from containers.
amazon-ecs-init :fire: - RPM developed to support the Amazon ECS Container Agent.
blox :fire::fire::fire: - Open source tools for building custom schedulers on ECS.
ecs-blue-green-deployment :fire::fire: - Blue-green deployment on ECS.
ecs-cloudwatch-logs - Assets from the blog using Amazon ECS and Amazon CloudWatch logs.
ecs-demo-php-simple-app :fire: - Simple PHP app.
ecs-mesos-scheduler-driver :fire: - Integrates Apache Mesos.
ecs-refarch-continuous-deployment :fire::fire::fire: - Reference Architecture for continuous deployment to ECS using CodePipeline.
ecs-task-kite - Simple ambassador container for inter-task communication.
lambda-ecs-worker-pattern :fire::fire: - Extends Lambda using SQS and ECS.
py-flask-signup-docker - Python sample app.
service-discovery-ecs-consul :fire: - Assets from the blog Service Discovery via Consul with Amazon ECS.

Community Repos:

Lumoslabs/broadside - Command line tool for deploying revisions of containerized applications.
Stelligent/mu :fire::fire::fire: - Command line tool to simplify ECS deployments via CodeBuild and CodePipeline.

Elastic File System

AWS Repos:

amazon-ecs-amazon-efs - Persist data from ECS.

Community Repos:

Contribute

Elastic MapReduce

AWS Repos:

emr-bootstrap-actions :fire::fire::fire: - Sample bootstrap actions.
emr-sample-apps - Sample apps.

Community Repos:

Yelp/mrjob :fire::fire::fire::fire::fire: - Run MapReduce jobs on Hadoop or EMR.

Elastic Search

AWS Repos:

logstash-output-amazon_es :fire::fire: - Logstash output plugin to sign and export events.
opsworks-elasticsearch-cookbook - OpsWorks Elasticsearch sample cookbook.

Community Repos:

elastic/elasticsearch-cloud-aws :fire::fire::fire: - Plugin for Elasticsearch.

Elasticache

AWS Repos:

aws-elasticache-cluster-client-libmemcached - Libmemcached library support.
aws-elasticache-cluster-client-memcached-for-java - Client for Java.
aws-elasticache-cluster-client-memcached-for-php - Enhanced PHP library connecting to ElastiCache.
elasticache-cluster-config-net - Config object for Enyim’s MemcachedClient to enable auto discovery.

Community Repos:

Contribute

Glacier

Community Repos:

vsespb/mt-aws-glacier :fire::fire::fire: - Perl Multithreaded Multipart sync to Glacier.

Kinesis

AWS Repos:

amazon-kinesis-aggregators :fire: - Provides a simple way to create real time aggregations.
amazon-kinesis-client-net - Client Library for .NET.
amazon-kinesis-client-nodejs :fire::fire: - Client Library for Node.js.
amazon-kinesis-client-python :fire::fire: - Client Library for Python.
amazon-kinesis-client-ruby :fire: - Client Library for Ruby.
amazon-kinesis-client :fire::fire::fire: Client library for Amazon Kinesis.
amazon-kinesis-connectors :fire::fire: - Libary to integrate with other AWS and non-AWS services.
amazon-kinesis-data-visualization-sample :fire: - Sample data visualization app.
amazon-kinesis-learning - Learning Kinesis Development.
amazon-kinesis-producer :fire::fire: - Producer Library.
amazon-kinesis-scaling-utils :fire::fire: - Provides the ability to scale streams.
aws-fluent-plugin-kinesis :fire::fire: - Fluent Plugin.
dynamodb-streams-kinesis-adapter - DynamoDB Streams Adapter.
kinesis-log4j-appender - Log4J Appender.
kinesis-poster-worker - Simple multi-threaded Python Poster and Worker.
kinesis-storm-spout :fire: - Spout for Storm.
mqtt-kinesis-bridge - Simple MQTT bridge in Python.

Community Repos:

Contribute

Lambda

AWS Repos:

amazon-elasticsearch-lambda-samples :fire::fire: - Data ingestion for Elasticsearch from S3 and Kinesis.
awslabs/aws-sam-local :fire::fire::fire::fire::fire: - CLI tool for local development and testing of Serverless applications.
aws-lambda-go :fire::fire::fire::fire::fire: - Libraries, samples and tools to help Go developers develop Lambda functions.
aws-lambda-java-libs :fire::fire: - Official mirror for interface definitions and helper classes.
aws-lambda-redshift-loader :fire::fire::fire: - Redshift loader.
chalice :fire::fire::fire::fire::fire: - Python Serverless Microframework.
create-thumbnails-lambda - Uses the grunt-aws-lambda plugin to help you develop and test.
lambda-ecs-worker-pattern :fire::fire: - Extends Lambda using SQS and ECS.
lambda-refarch-fileprocessing :fire::fire: - Reference Architecture for Real-time File Processing.
lambda-refarch-iotbackend :fire::fire: - Reference Architecture for creating an IoT Backend.
lambda-refarch-mobilebackend :fire::fire::fire: - Reference Architecture for creating a Mobile Backend.
lambda-refarch-webapp :fire::fire::fire::fire: - Reference Architecture for creating a Web Application.

Community Repos:

alestic/lambdash :fire::fire::fire: - Lambda shell - Run sh commands inside the Lambda environment.
Alephbet/gimel :fire::fire: - Run your own A/B testing backend using Lambda.
apex/apex - Minimal AWS Lambda function manager with Go support.
claudiajs/claudia :fire::fire::fire::fire::fire: - Deploy Node.js projects to Lambda and API Gateway easily.
cloudnative/lambda-chat :fire::fire: - A chat application without servers.
danilop/LambdAuth :fire::fire::fire::fire: - Sample authentication service.
eawsy/aws-lambda-go :fire::fire::fire: - A fast and clean way to execute Go on Lambda.
garnaat/kappa :fire::fire::fire: - Kappa is a CLI tool that makes it easier to deploy, update, and test functions for AWS Lambda.
goadapp/goad :fire::fire::fire::fire: - Lambda powered, highly distributed, load testing tool.
graphcool/chromeless :fire::fire::fire::fire::fire: - Automate Chrome through Lambda.
grycap/scar :fire::fire::fire: - Transparently execute containers out of Docker images in AWS Lambda.
jeremydaly/lambda-api :fire::fire::fire::fire: - Lightweight web framework for your serverless applications.
jimpick/lambda-comments :fire::fire::fire: - Blog commenting system built with Lambda.
jorgebastida/gordon :fire::fire::fire::fire::fire: - λ Gordon is a tool to create, wire and deploy AWS Lambdas using CloudFormation.
ks888/LambStatus :fire::fire::fire::fire: - A status page system inspired by StatusPage.io, built on AWS Lambda.
kubek2k/lambdoku :fire::fire::fire: - Heroku-like experience when using Lambda.
lambci/lambci :fire::fire::fire::fire::fire: - A continuous integration system built on Lambda.
littlstar/s3-lambda :fire::fire::fire::fire: - Lambda functions over S3 objects with concurrency control (each, map, reduce, filter).
mentum/lambdaws :fire::fire::fire::fire: - Deploy, run and get results in a breeze.
Miserlou/Zappa :fire::fire::fire::fire::fire: - Serverless WSGI Python Web Apps with AWS Lambda + API Gateway.
nficano/python-lambda :fire::fire::fire::fire: - A toolkit for developing and deploying serverless Python code in Lambda.
serverless/serverless :fire::fire::fire::fire::fire: The Serverless Application Framework (formerly JAWS).
Tim-B/grunt-aws-lambda :fire::fire: - Grunt plugin.
trek10inc/aws-lambda-debugger :fire::fire: - Remote debugging tool for Lambda functions running on Node 6.10

Machine Learning

AWS Repos:

machine-learning-samples :fire::fire::fire: - Sample apps.

Community Repos:

Contribute

Mobile Analytics

AWS Repos:

aws-sdk-mobile-analytics-js - JavaScript SDK.

Community Repos:

Contribute

OpsWorks

AWS Repos:

opsworks-attribute-customization - Attribute customization example.
opsworks-capistrano - Capistrano with instances.
opsworks-cloudwatch-logs-cookbooks - CloudWatch sample cookbook.
opsworks-cookbooks :fire::fire::fire::fire: - Chef Cookbooks.
opsworks-demo-php-photo-share-app - Simple PHP photo share app.
opsworks-demo-php-simple-app - Simple PHP app.
opsworks-demo-rails-photo-share-app - A sample Rails app.
opsworks-elasticsearch-cookbook - Elasticsearch sample cookbook.
opsworks-example-cookbooks :fire: - Cookbooks used with the sample apps.
opsworks-first-cookbook - Cookbook used to demonstrate simple recipes.
opsworks-windows-demo- - A sample Node.JS app.
opsworks-windows-demo-cookbooks - Cookbooks for Windows.
todo-sample-app-cookbooks - Custom cookbooks associated with the todo-sample-app.

Community Repos:

Contribute

Redshift

AWS Repos:

aws-lambda-redshift-loader :fire::fire::fire: - Lambda database loader.
amazon-redshift-utils :fire::fire::fire::fire::fire: - Applies optimal Column Encoding to existing Tables.

Community Repos:

Lumoslabs/aleph - A full featured web application for writing and running Redshift queries. Supports revision tracking of queries and has basic visualization support.
getredash/redash :fire::fire::fire::fire::fire: - A web application that allows to easily query an existing database, share the dataset and visualize it in different ways. Initially was developed to work with Redshift, and has great support for it.
everythingMe/redshift_console - A simple tool to monitor and manage a Redshift cluster. The first release has basic tools to monitor running queries, WLM queue and your tables/schemas.

Route 53

AWS Repos:

route53-infima :fire::fire: - Manages service-level fault isolation.

Community Repos:

barnybug/cli53 :fire::fire::fire::fire: - cli53 is a command line tool for Amazon Route 53 which provides import and export from BIND format and simple command line management of Route 53 domains.
winebarrel/roadworker :fire::fire: - Roadworker is a tool to manage Route53. It defines the state of Route53 using DSL, and updates Route53 according to DSL.

S3

Community Repos:

anomalizer/ngx_aws_auth :fire::fire: - Implements proxying of authenticated requests.
bloomreach/s4cmd :fire::fire::fire::fire: - S3 command line tool, faster than S3cmd for large files.
CulturalMe/meteor-slingshot :fire::fire::fire: - Upload files in meteor.
danilop/yas3fs :fire::fire::fire: - Yet Another S3-backed File System, inspired by s3fs.
grippy/node-s3 - Node.js app to manage buckets.
jubos/fake-s3 :fire::fire::fire::fire::fire: - Lightweight S3 clone that simulates most commands.
kahing/goofys :fire::fire::fire::fire::fire: - a Filey System for Amazon S3 written in Go.
littlstar/s3renity :fire::fire::fire::fire: - Batch functions with concurrency control (each, map, reduce, filter, join)
marcel/aws-s3 :fire::fire::fire: - Ruby implementation of Amazon’s S3 REST API.
mardix/flask-cloudy :fire::fire: - Flask extension.
MathieuLoutre/grunt-aws-s3 :fire::fire: - Grunt plugin.
mickael-kerjean/filestash :fire::fire::fire::fire::fire: - A modern web client for S3.
minio/mc :fire::fire::fire::fire::fire: - Minio Client for filesystem and cloud storage.
minio/minio :fire::fire::fire::fire::fire: - Object storage server compatible with S3.
mumrah/s3-multipart :fire: - Parallel upload/download to S3 via Python.
ncw/rclone :fire::fire::fire::fire::fire: - Rsync for various cloud storage providers such as S3.
owocki/s3_disk_util :fire: - S3 Disk usage (du) utility.
peak/s5cmd :fire::fire::fire: - Fast S3 and local filesystem execution tool with wildcard and batch command support.
pgherveou/gulp-awspublish :fire::fire: - Gulp plugin.
rlmcpherson/s3gof3r :fire::fire::fire::fire: - Fast, concurrent, streaming access, includes a CLI.
s3git/s3git :fire::fire::fire::fire: - CLI tool that allows you to create a distributed, decentralized and versioned repository.
s3fs-fuse/s3fs-fuse :fire::fire::fire::fire::fire: - Allows Linux and Mac OS X to mount an S3 bucket via FUSE.
s3tools/s3cmd :fire::fire::fire::fire::fire: - CLI for managing S3 and CloudFront.
schickling/git-s3 :fire::fire: - Deploy your git repo to a bucket.
sorentwo/carrierwave-aws :fire::fire: - Adapter for CarrierWave.
spring-projects/aws-maven :fire::fire: - Maven Wagon for S3.
tongwang/s3fs-c :fire: - Mounts buckets for use on a local file system.
mishudark/s3-parallel-put :fire::fire: - CLI that supports parallel uploads.
waynehoover/s3_direct_upload :fire::fire::fire: - Direct Upload to Amazon S3 With CORS
weavejester/clj-aws-s3 :fire: - Client library for Clojure.

SES

Community Repos:

drewblas/aws-ses :fire::fire::fire: - Provides an easy ruby DSL & interface.
microapps/MoonMail :fire::fire::fire::fire: - Shoot billions of emails using SES and Lambda.

Simple Workflow

AWS Repos:

aws-flow-ruby :fire: - Creates background jobs and multistep workflows.
aws-flow-ruby-samples - AWS Flow Framework for Ruby samples.
aws-flow-ruby-opsworks-helloworld - Hello World sample.

Community Repos:

Contribute

SimpleDB

Community Repos:

rjrodger/simpledb :fire: - Node.js library.

SNS

AWS Repos:

aws-php-sns-message-validator :fire: - Message validation for PHP.

Community Repos:

Contribute

SQS

AWS Repos:

amazon-sqs-java-messaging-lib :fire: - Holds the Java Message Service to communicate with SQS.

Community Repos:

phstc/shoryuken :fire::fire::fire::fire: - A super efficient SQS thread based message processor for Ruby.

Data

AWS Repos:

aws-data-wrangler :fire::fire::fire::fire::fire: - Connects Pandas DataFrames and AWS data related services.

Community Repos:

donnemartin/data-science-ipython-notebooks :fire::fire::fire::fire::fire: - Big data/data science notebooks.
everpeace/vagrant-mesos :fire::fire: - Spin up your Mesos Cluster with Vagrant.
jhorey/ferry :fire::fire: - Define, run, and deploy big data apps using Docker.
nathanmarz/storm-deploy :fire::fire::fire: - One click deploy for Storm clusters.

DevOps

Community Repos:

cloud-custodian/cloud-custodian :fire::fire::fire::fire::fire: - Rules engine for management, DSL in yaml for query, filter, and actions on resources.
chef-cookbooks/aws :fire::fire: - Development repository for aws Chef cookbook.
colinbjohnson/aws-missing-tools :fire::fire::fire::fire: - Tools for managing resources including EC2, EBS, RDS and Route53.
k1LoW/awspec :fire::fire::fire::fire: - RSpec tests your resources.
mitchellh/vagrant-aws :fire::fire::fire::fire::fire: - Use Vagrant to manage your EC2 and VPC instances.
NixOS/nixops :fire::fire::fire::fire: - Use NixOS to provision EC2 instances, S3 buckets, and other resources.

Security

AWS Repos:

aws-sha256-agentcs - SHA256 Agent Compatibility Ccanner.
aws-tvm-anonymous - Token Vending Machine for Anonymous Registration.
aws-tvm-identity - Token Vending Machine for Identity Registration.
s2n :fire::fire::fire::fire::fire: - An implementation of the TLS/SSL protocols.

Community Repos:

AdRoll/hologram :fire::fire::fire: - Easy, painless credentials on developer laptops.
alex/letsencrypt-aws :fire::fire::fire: - Automatically provision and update certificates.
bridgecrewio/checkov :fire::fire::fire::fire::fire: - Terraform static analysis, verifies security best practices.
cloudsploit/scans :fire::fire::fire::fire: - Detects security risks.
iSECPartners/Scout2 :fire::fire::fire::fire: - Security auditing tool.
jordanpotti/AWSBucketDump :fire::fire::fire::fire: - Security Tool to Look For Interesting Files in S3 Buckets.
Netflix/bless :fire::fire::fire::fire::fire: - SSH Certificate Authority that runs as a Lambda function.
Netflix/security_monkey :fire::fire::fire::fire::fire: - Monitors policy changes and alerts on insecure configurations.
RiotGames/cloud-inquisitor :fire::fire: - Tool to enforce ownership and data security.
salesforce/policy_sentry :fire::fire::fire::fire: - IAM Least Privilege Policy Generator.
sebsto/AWSVPN :fire: - Start a private VPN server in the cloud.
trailofbits/algo :fire::fire::fire::fire::fire: - Set up a personal IPSEC VPN on EC2 and other cloud services.
ttlequals0/autovpn :fire::fire::fire::fire: - Create On Demand Disposable OpenVPN Endpoints.

Accompanying Repos

AWS Repos:

Repos Accompanying Blogs, Training Events, and Conferences.

aws-arch-backoff-simulator :fire: - Jitter and backoff Simulator for AWS architecture blog.
aws-big-data-blog :fire::fire::fire: - Samples from the AWS Big Data Blog.
aws-demo-php-simple-app - PHP apps from the AWS Blogs.
aws-mobile-sample-wif - Samples from the AWS Mobile SDK blog.
aws-mobile-self-paced-labs-samples - Android Snake Game from a self-paced lab.
aws-quickstart - Official repository for AWS Quick Start.
aws-spot-labs :fire::fire::fire: - Best practices using AWS Spot Instances.
aws-training-demo :fire: - Demos from the Technical Trainers community.
java-meme-generator-sample - Meme generation app from re:Invent 2012.
railsconf2013-tech-demo :fire: - Seahorse demo from RailsConf 2013.
reinvent2013-js-blog-demo - Demo blogging app from re:Invent 2013.
reinvent2013-mobile-photo-share - Mobile photo share app from re:Invent 2014.
reinvent2014-scalable-site-management - Scalable site management sample from re:Invent 2014.
reinvent2015-dev309 - Large Scale Metrics Analysis from re:Invent 2015.
timely-security-analytics - Security analytics sample from 2015 re:Invent 2015.
todo-sample-app - Simple “Todo” app from RailsConf 2014.

Community Repos:

startup-class/setup :fire::fire: - EC2 setup files for Startup Engineering MOOC.

Miscellaneous Repos

AWS Repos:

amediamanager - Media manager.
aws-hal-client-java - Java client for the Hypertext Application Language.
aws-model-validators - Tools for validating the AWS service JSON model files.
aws-sdk-js-sample-video-transcoder - Sample cross-platform video transcoder app.
simplebeerservice :fire::fire: - Cloud-connected kegerator that streams live sensor data to AWS.

Community Repos:

bcoe/thumbd :fire::fire: - Node.js/ImageMagick-based image thumbnailing service.
cdkpatterns/serverless :fire::fire::fire::fire: - Deployable serverless architecture patterns built in AWS CDK.
Comcast/cmb :fire::fire: - Highly available, horizontally scalable queuing and notification service.
convox/rack :fire::fire::fire::fire: - Open-source PaaS on AWS.
devops-israel/aws-inventory :fire::fire: - Display all your AWS resources on a single web page.
donnemartin/dev-setup :fire::fire::fire::fire: - Mac setup of various developer tools and AWS services.
dtan4/terraforming :fire::fire::fire::fire::fire: - Export existing resources to Terraform style (tf, tfstate).
segmentio/stack :fire::fire::fire::fire::fire: - A set of Terraform modules for configuring production infrastructure.
j2labs/microarmy - Deploy micro instances to launch a coordinated siege.
jpillora/grunt-aws :fire: - Grunt interface into the Node.JS SDK.
jvehent/haproxy-aws :fire::fire: - Documentation on building a HTTPS stack with HAProxy.
localstack/localstack :fire::fire::fire::fire::fire: - A fully functional local AWS cloud stack. Develop and test your cloud apps offline!
meducation/propono :fire::fire: - Easy-to-use pub/sub in Ruby.
mozilla/awsbox :fire::fire::fire: - A featherweight PaaS on top of EC2 for deploying node apps.
Netflix/aminator :fire::fire::fire: - A tool for creating EBS AMIs.
Netflix/archaius :fire::fire::fire::fire::fire: - Library for configuration management API.
Netflix/asgard :fire::fire::fire::fire::fire: - Web interface for application deployments and cloud management.
Netflix/aws-autoscaling :fire::fire: - Tools for using auto scaling and documentation best practices.
Netflix/chaosmonkey :fire::fire::fire::fire::fire: - Resiliency tool that helps applications tolerate random instance failures.
Netflix/eureka :fire::fire::fire::fire::fire: - Service registry for resilient mid-tier load balancing and failover.
Netflix/EVCache :fire::fire::fire::fire: - A distributed in-memory data store.
Netflix/Fenzo :fire::fire::fire: - Extensible Scheduler for Mesos Frameworks.
Netflix/ice :fire::fire::fire::fire::fire: - Usage and cost monitoring tool.
Netflix/ribbon :fire::fire::fire::fire::fire: - Remote procedure call library with built in software load balancers.
Netflix/SimianArmy :fire::fire::fire::fire::fire: - Tools to keep your cloud operating in top form.
Netflix/zuul :fire::fire::fire::fire::fire: - Edge service that provides dynamic routing, monitoring, resiliency, security, and more.
niftylettuce/gulp-aws-splash :fire::fire: - Open-source LaunchRock alternative. Build beautiful splash pages.
puppetlabs/puppetlabs-aws :fire: - Puppet module for managing resources to build out infrastructure.
mhart/react-server-routing-example :fire::fire: - Sample universal client/server routing and data in React.
Similarweb/finala :fire::fire::fire: - A resource cloud scanner that analyzes and reports wasteful and unused resources to cut unwanted expenses.
snowplow/snowplow :fire::fire::fire::fire::fire: - Enterprise-strength web, mobile and event analytics, powered by Hadoop, Kafka, Kinesis, Redshift and Elasticsearch.
Spinnaker/spinnaker :fire::fire::fire::fire::fire: - Successor to asgard supporting pipelines and more.
spulec/moto :fire::fire::fire::fire::fire: - Allows your python tests to easily mock out the boto library.

Guides, Books, Documentation, and Training

How-to’s, training, whitepapers, docs, and case studies.

Getting Started Guides

AWS Guides:

Community Guides:

Contribute

General Guides

AWS Guides:

Community Guides:

Open Guide to AWS :fire::fire::fire::fire::fire:

Books

Amazon Web Services in Action Manning or Amazon.com
AWS Lambda in Action Manning or Amazon.com - Code Repo :fire::fire:

Whitepapers

Documentation

Training

Case Studies: Powered by AWS

Blogs, discussion groups, conferences, and social media.

Blogs

AWS Blogs:

Official Blog
- Brasil
- China
- Germany
- Japan
- Korea
DevOps
Architecture
Big Data
Compute
Mobile
Messaging
Java
PHP
Ruby
.NET
Security
Startup
Partner Network
SAP

Community Blogs:

All Things Distributed - Werner Vogels, AWS CTO.
Things I Like… - Jeff Barr, AWS Chief Evangelist.
Netflix Tech Blog
A Curated List of Engineering Blogs
AWS Geek

Twitter Influencers

AWS Tweeps:

@awscloud - Official Twitter feed.
@AWS_Partners
@AWSIdentity
@AWSMarketplace
@AWSreInvent - Official Twitter account for re:Invent.
@AWSStartups
@ajassy - Andy Jassy: Senior Vice-President.
@Ianmmmm - Ian Massingham - Technical Evangelist.
@jeffbarr - Jeff Barr: Chief Evangelist.
@mndoci - Deepak Singh: GM EC2.
@mza - Matt Wood: Product Strategy.
@Werner - Werner Vogels: CTO.
Community heroes, Evangelists, etc

Community Tweeps:

Facebook Pages

AWS Pages:

amazonwebservices - Official Facebook page.
awsreinvent - Official Facebook page for re:Invent.

Community Pages:

Contribute

YouTube Channels

AWS Channels:

Community Channels:

LinkedIn Groups

AWS Page:

Amazon Web Services

Community Groups:

Subreddits

Conferences

AWS Conferences:

re:Invent - Annual user conference. The event features keynote announcements, training and certification opportunities, over 250 technical sessions, a partner expo, after hours activities, and more.
Summits - Global one-day events that are designed to educate new customers about the AWS platform and offer existing customers deep technical content to be more successful with AWS.
AWSome Day - Global one-day events are delivered by AWS Education’s technical instructors and are ideal for IT pros, developers and technical managers who would like to learn about how to get started in the AWS Cloud.

Community Conferences:

Contribute

Latest KPIs and Stats

Latest key performance indicators and other interesting stats.

Over 1 million customers active in past 30 days.¹
$7B+ annual revenue run-rate business.¹
- 81% year over year revenue growth.¹
EC2 usage up 95% year over year.¹
S3 data transfer up 120% year over year.¹
- S3 holds trillions of objects and regularly peaks at 1.5 million requests per second.²
Database services usage up 127% year over year.¹
- $1B annual revenue run-rate business.¹
2 million new EBS volumes created per day.⁴
Customers have launched more than 15 million Hadoop clusters.³
102Tbps network capacity into a data center.²
500+ major new features and services launched since 2014.¹
All 14 other cloud providers combined have 1/5th the aggregate capacity of AWS.²
Every day, AWS adds enough new server capacity to support all of Amazon’s global infrastructure when it was a $7B annual revenue enterprise (in 2004).²

Appendix of Core Services

Appendix of official services, grouped by service category.

Services in Plain English

Amazon Web Services in Plain English - Entertaining and educational, a community contribution.

Compute Services

Auto Scaling - Launches or terminates EC2 instances based on policies, schedules, and health checks.
Batch - Run batch jobs at scale.
Blox - Open source projects for building custom schedulers on ECS.
EC2 Container Service (ECS) - Supports Docker containers on EC2 instances.
EC2 Systems Manager - Easily configure and manage EC2 and on-premises systems.
Elastic Beanstalk - Provides quick deployment and management of applications in the cloud.
Elastic Compute Cloud (EC2) - Provides scalable virtual private servers using Xen.
Elastic GPUs - Attach low-cost GPUs to EC2 instances for graphics acceleration.
Elastic Load Balancing (ELB) - Automatically distributes incoming traffic across multiple EC2 instances.
Lambda - Runs code in response to events and automatically manages EC2 instances.
Lightsail - Launch and manage simple virtual private servers.
Virtual Private Cloud (VPC) - Creates a logically isolated set of EC2 instances which can be connected to an existing network using a VPN connection.

Networking Services

Direct Connect - Provides dedicated connections to AWS for faster and cheaper data throughput.
Elastic Load Balancing (ELB) - Automatically distributes incoming traffic across multiple EC2 instances.
Route 53 - Provides a highly available and scalable Domain Name System (DNS) web service.
Virtual Private Cloud (VPC) - Creates a logically isolated set of EC2 instances which can be connected to an existing network using a VPN connection.

Enterprise Applications

WorkDocs - Provides a fully managed, secure enterprise storage and sharing service.
WorkMail - Provides managed email and calendaring service.
WorkSpaces - Provides a cloud-based desktop experience to end-users.
Workspaces Application Manager (WAM) - Simplifies deployment and management of WorkSpaces.

Analytics Services

Athena - Query data on S3 instantly.
Data Pipeline - Provides workload management by processing and moving data between services.
Elastic MapReduce (EMR) - Hosts a Hadoop and Spark framework running on EC2 and S3.
Elasticsearch Service (ES) - Managed Elasticsearch, a popular open-source search and analytics engine.
Glue - Prepare and load data to data stores.
Kinesis - Provides real-time data processing over large, distributed data streams.
Kinesis Analytics - Write standard SQL queries on streaming data without having to learn any new programming skills.
Kinesis Firehose - Captures and automatically loads streaming data into S3 and Redshift.
Quicksight - Provides cloud-powered business intelligence for 1/10th the cost of traditional BI solutions.
Redshift - Provides petabyte-scale data warehousing with columnar storage and multi-node compute.

Artificial Intelligence

Lex - Build conversational interfaces through voice or text.
Machine Learning - Provides managed machine learning technology.
Polly - Turn text into lifelike speech.
Rekognition - Deep learning-based image analysis.

Management Tools

CloudFormation - Provides a file-based interface for provisioning other resources.
CloudTrail - Provides logs of all activity.
CloudWatch - Provides monitoring for AWS cloud resources and applications, starting with EC2.
Command Line Interface (CLI) - Provides a CLI to manage all services.
Config - Provides a detailed view of all resources.
Management Console (AWS Console) - A web-based interface to manage all services.
OpsWorks - Provides configuration of EC2 services using Chef.
Personal Health Dashboard - Your personalized view of service health.
Service Catalog - Service Catalog allows IT administrators to create, manage, and distribute portfolios of approved products to end users, who can then access the products they need in a personalized portal.

Security and Identity Services

Certificate Manager - Lets you easily provision, manage, and deploy SSL/TLS certificates for use with AWS services.
CloudHSM - Helps meet corporate, contractual and regulatory compliance requirements for data security by using dedicated Hardware Security Module (HSM) appliances within the AWS cloud.
Directory Service - A managed service that allows you to connect your resources with an existing on-premises Microsoft Active Directory or to set up a new, stand-alone directory in the AWS Cloud.
Identity and Access Management (IAM) - An implicit service, the authentication infrastructure used to authenticate access to the various services.
Inspector - An automated security assessment service that helps improve the security and compliance of applications deployed on AWS.
Key Management Service (KMS) - A managed service that makes it easy for you to create and control the encryption keys used to encrypt your data.
Shield - Managed DDoS Protection.
WAF - A web application firewall service that monitors and manages CloudFront distributions.

Internet of Things Service

IoT - Enables secure, bi-directional communication between internet-connected things (such as sensors, actuators, embedded devices, or smart appliances) and the AWS cloud over MQTT and HTTP.

Mobile Services

API Gateway - Service for publishing, maintaining and securing web service APIs.
Cognito - Provides user identity and data synchronization.
Device Farm - App testing service for iOS, Android and Fire OS apps on physical devices.
Mobile Analytics - Service for collecting, visualizing, and understanding app usage data.
Mobile Hub - Provides an integrated console that helps you build, test, and monitor your mobile apps.
Pinpoint - Targeted push notifications for mobile apps.
Simple Notification Service (SNS) - Provides a hosted multi-protocol “push” messaging for applications.

Storage and Content Delivery Services

CloudFront - A content delivery network (CDN) for distributing objects to locations near the requester.
Elastic Block Store (EBS) - Provides persistent block-level storage volumes for EC2.
Elastic File System (EFS) - A file storage service for EC2 instances.
Glacier - Provides a low-cost, long-term storage option, intended for archiving data.
Import/Export - Accelerates moving large amounts of data into and out of AWS using portable storage devices for transport.
Simple Storage Service (S3) - Provides Web Service based storage.
Storage Gateway - An iSCSI block storage virtual appliance with cloud-based backup.

Databases

Aurora - MySQL and PostgreSQL compatible relational database with improved performance.
DynamoDB - Provides a scalable, low-latency NoSQL online Database Service backed by SSDs.
ElastiCache - Provides in-memory caching for web apps (Memcached, Redis).
Redshift - Provides petabyte-scale data warehousing with columnar storage and multi-node compute.
Relational Database Service (RDS) - Provides a scalable database server with MySQL, Oracle, SQL Server, PostgreSQL, and MariaDB support.
Schema Conversion Tool - App that helps you convert your database schema from an Oracle or Microsoft SQL Server database, to an RDS MySQL DB instance or an Aurora DB cluster.
SimpleDB - Allows developers to run queries on structured data.

Application Services

API Gateway - Service for publishing, maintaining and securing web service APIs.
AppStream - Flexible, low-latency streaming service for apps and games.
CloudSearch - Provides basic full-text search and indexing of textual content.
DevPay - Provides billing and account management.
Elastic Transcoder (ETS) - Provides video transcoding of S3 hosted videos.
Flexible Payments Service (FPS) - Provides an interface for micropayments.
Simple Email Service (SES) - Provides bulk and transactional email sending.
Simple Notification Service (SNS) - Provides a hosted multi-protocol “push” messaging for applications.
Simple Queue Service (SQS) - Provides a hosted message queue for web applications.
Simple Workflow (SWF) - A workflow service for building scalable, resilient applications.
Step Functions - Coordinate components of distributed applications.

Developer Tools

CodeBuild - Build and test code.
CodeCommit - Hosted Git version control service.
CodeDeploy - Provides automated code deployment to EC2 instances.
CodePipeline - Continuous delivery service.
Command Line Interface (CLI) - Provides a CLI to manage all services.
X-Ray - Analyze and debug your applications.

Miscellaneous Services

Fulfillment Web Service - Provides a programmatic web service for sellers to ship items to and from Amazon using Fulfillment by Amazon.
Mechanical Turk - Manages small units of work distributed among many persons.
Partner Network (APN) - Provides partners with the technical information and sales and marketing support to increase business opportunities.
Product Advertising API - Provides access to product data and electronic commerce functionality.

Credits

Check out the Credits page.

Other Awesome Lists

Other awesome lists can be found in awesome and awesome-awesomeness.

Contact Info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Copyright 2017 Donne Martin

Creative Commons Attribution 4.0 International License (CC BY 4.0)

http://creativecommons.org/licenses/by/4.0/

aws-sdk-java-v2

Sun, 04 May 2025 15:26:09 +0800

aws/aws-sdk-java-v2

AWS SDK for Java 2.0

The AWS SDK for Java 2.0 is a rewrite of 1.0 with some great new features. As with version 1.0, it enables you to easily work with Amazon Web Services but also includes features like non-blocking IO and pluggable HTTP implementation to further customize your applications. You can get started in minutes using Maven or any build system that supports MavenCentral as an artifact source.

Getting Started

Before you begin, you need an AWS account. Please see the Sign Up for AWS section of the developer guide for information about how to create an AWS account and retrieve your AWS credentials.

Minimum requirements

To run the SDK you will need Java 1.8+. For more information about the requirements and optimum settings for the SDK, please see the Installing a Java Development Environment section of the developer guide.

Using the SDK

The recommended way to use the AWS SDK for Java in your project is to consume it from Maven Central.

Importing the BOM

To automatically manage module versions (currently all modules have the same version, but this may not always be the case) we recommend you use the Bill of Materials import as follows:

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>software.amazon.awssdk</groupId>
      <artifactId>bom</artifactId>
      <version>2.31.35</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

Then individual modules may omit the version from their dependency statement:

<dependencies>
  <dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>ec2</artifactId>
  </dependency>
  <dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>s3</artifactId>
  </dependency>
  <dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>dynamodb</artifactId>
  </dependency>
</dependencies>

Individual Services

Alternatively you can add dependencies for the specific services you use only:

<dependency>
  <groupId>software.amazon.awssdk</groupId>
  <artifactId>ec2</artifactId>
  <version>2.31.35</version>
</dependency>
<dependency>
  <groupId>software.amazon.awssdk</groupId>
  <artifactId>s3</artifactId>
  <version>2.31.35</version>
</dependency>

Whole SDK

You can import the whole SDK into your project (includes ALL services). Please note that it is recommended to only import the modules you need.

<dependency>
  <groupId>software.amazon.awssdk</groupId>
  <artifactId>aws-sdk-java</artifactId>
  <version>2.31.35</version>
</dependency>

See the Set up the AWS SDK for Java section of the developer guide for more usage information.

New Features for 2.0

Provides a way to plug in your own HTTP implementation.
Provides first class support for non-blocking IO in Async clients.

Building From Source

Once you check out the code from GitHub, you can build it using the following commands.

Linux:

./mvnw clean install

# Skip tests, checkstyles, findbugs, etc for quick build
./mvnw clean install -P quick

# Build a specific service module
./mvnw clean install -pl :s3 -P quick --am

Windows:

`1`	`./mvnw.cmd clean install`

Sample Code

You can find sample code for v2 in the following places:

aws-doc-sdk-examples repo.
Integration tests in this repo. They are located in the it directory under each service module, eg: s3-integration-tests

Maintenance and Support for SDK Major Versions

For information about maintenance and support for SDK major versions and their underlying dependencies, see the following in the AWS SDKs and Tools Reference Guide:

Maintenance and Support for Java Versions

We maintain full support on Long-Term Support(LTS) releases: Java 8, Java 11, Java 17, and Java 21. Note that there may be individual features in newer releases that are not supported, because the SDK must be compatible with Java 8.

Giving Feedback

We need your help in making this SDK great. Please participate in the community and contribute to this effort by submitting issues, participating in discussion forums and submitting pull requests through the following channels:

Submit issues - this is the preferred channel to interact with our team
Articulate your feature request or upvote existing ones on our Issues page

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_sullis 💻	_{Austin Brooks} 💻	_{Konrad `ktoso` Malawski} 💻	_{Andrew Hopkins} 💻	_{Adam Thomas} 💻	_{Steven Swor} 💻	_{Carey Burgess} 💻
_{Anuraag Agrawal} 💻	_jeffalder 💻	_Boris 💻	_{Guillaume Corré} 💻	_{Henri Yandell} 💻	_{Ryan Schmitt} 💻	_Somaya 💻
_{Steven Aerts} 💻	_{Steven Wong} 💻	_{Tomasz Elendt} 💻	_{Will Erickson} 💻	_{Julien Hoarau} 💻	_{SEOKHYOENCHOI} 💻	_adriannistor 💻
_{Xian Sun} 💻	_{Andreas Scheja} 💻	_{Anton Egorov} 💻	_roexber 💻	_brharrington 💻	_{Christopher Radek} 💻	_Foivos 💻
_{Frank Wesemann} 💻	_{Gergely Varga} 💻	_Guillermo 💻	_{Henry Heikkinen} 💻	_{Jochen Schalanda} 💻	_{Joe Barnett} 💻	_{Kazuhiro Sera} 💻
_{Krishna Chaithanya Ganta} 💻	_{Lee Packham} 💻	_{Matteo Carrara} 💻	_{Michael Scharp} 💻	_{Miguel Jimenez} 💻	_{Russell Bolles} 💻	_{Russell Scheerer} 💻
_Scott 💻	_{Shin'ya Ueoka} 💻	_sushilamazon 💻	_tomliu4uber 💻	_{Vladimir Orany} 💻	_{Xinyu Hu} 💻	_{Yosef Fertel} 💻
_{Denys Konakhevych} 💻	_{Alex Weibel} 💻	_{Ryan Carper} 💻	_{Jonathan M. Henson} 💻	_{Debora N. Ito} 💻	_{Bret Ambrose} 💻	_{Anna-Karin Salander} 💻
_{John Viegas} 💻	_{Dongie Agnir} 💻	_{Matthew Miller} 💻	_{Benjamin Maizels} 💻	_{Quan Zhou} 💻	_{Zoe Wang} 💻	_{Varun Nandi} 💻
_{Andrew Shore} 💻	_{Kyle Thomson} 💻	_{Sam Fink} 💻	_{Jonathan Bond} 💻	_ajs139 💻	_{Dewey Nguyen} 💻	_{David Leen} 💻
_{Michael Li} 💻	_{Bennett Lynch} 💻	_{Ikko Ashimine} 📖	_{Jamie Liu} 📖	_guillepb10 💻	_{Lorenz Nickel} 📖	_{Erin Yang} 💻
_{Roberto Tyley} 💻	_{Alvin See} 💻	_ron1 💻	_{Sai Kumar Reddy Chandupatla} 💻	_{David Ho} 💻	_{Thomas Turrell-Croft} 💻	_{Steven Shan} 💻
_{Barry O'Neill} 💻	_{Andy Kiesler} 💻	_Martin 💻	_{Paulo Lieuthier} 💻	_{Sébastien Crocquesel} 💻	_{David Negrete} 💻	_{Stephen Flavin} 💻
_{Olivier L Applin} 💻	_{Adrian Chlebosz} 💻	_{Chad Wilson} 💻	_{Manish Dait} 📖	_{Dennis Kieselhorst} 💻	_{Nilesh PS} 💻	_{Steven Swartz} 💻
_{Michael Dimchuk} 💻	_{Nikita Sokolov} 💻	_{Manuel Sugawara} 💻	_Anirudh 💻	_{Hayden Baker} 💻	_{Jaykumar Gosar} 💻	_{Michael Graeb} 💻
_{Michael Grundie} 💻	_{Eckard Mühlich} 💻	_{Tobias Soloschenko} 💻	_{Luis Madrigal} 📖	_{Kirill Chaykin} 💻	_prateek-vats 💻	_Bole1155 💻
_{Thomas Farr} 💻	_{Saranya Somepalli} 💻	_{Ran Vaknin} 💻	_{Greg Oledzki} 💻

This project follows the all-contributors specification. Contributions of any kind welcome!

amazon-nova-samples

Sat, 12 Apr 2025 15:25:30 +0800

aws-samples/amazon-nova-samples

Amazon Nova model cookbook

Getting Started

To get started with the code examples, ensure you have access to Amazon Bedrock. Then clone this repo and navigate to one of the folders above. Detailed instructions are provided in each folder’s README.

Enable AWS IAM permissions for Bedrock

The AWS identity you assume from your environment (which is the Studio/notebook Execution Role from SageMaker, or could be a role or IAM User for self-managed notebooks or other use-cases), must have sufficient AWS IAM permissions to call the Amazon Bedrock service.

To grant Bedrock access to your identity, you can:

Open the AWS IAM Console
Find your Role (if using SageMaker or otherwise assuming an IAM Role), or else User
Select Add Permissions > Create Inline Policy to attach new inline permissions, open the JSON editor and paste in the below example policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "BedrockFullAccess",
            "Effect": "Allow",
            "Action": ["bedrock:*"],
            "Resource": "*"
        }
    ]
}

⚠️ Note 1: With Amazon SageMaker, your notebook execution role will typically be separate from the user or role that you log in to the AWS Console with. If you’d like to explore the AWS Console for Amazon Bedrock, you’ll need to grant permissions to your Console user/role too.

⚠️ Note 2: For top level folder changes, please reach out to the GitHub maintainers.

For more information on the fine-grained action and resource permissions in Bedrock, check out the Bedrock Developer Guide.

Contributing

We welcome community contributions! Please see CONTRIBUTING.md for guidelines.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.

👏 Contributors

Shout out to these awesome contributors:

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coding-interview-university

jwasham/coding-interview-university

Coding Interview University

What is it?

Table of Contents

The Study Plan

Topics of Study

Getting the Job

Optional Extra Topics & Resources

Why use it?

How to use it

If you don’t want to use git

If you’re comfortable with git

Don’t feel you aren’t smart enough

A Note About Video Resources

Choose a Programming Language

For this Study Plan

For your Coding Interview

Books for Data Structures and Algorithms

Python

C

Java

C++

Interview Prep Books

If you have tons of extra time:

Don’t Make My Mistakes

1. You Won’t Remember it All

2. Use Flashcards

3. Do Coding Interview Questions While You’re Learning

4. Focus

What you won’t see covered

The Daily Plan

Coding Question Practice

Coding Problems

Let’s Get Started

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Arrays

Linked Lists

Stack

Queue

Hash table

More Knowledge

Binary search

Bitwise operations

Trees

Trees - Intro

Binary search trees: BSTs

Heap / Priority Queue / Binary Heap

Sorting

Graphs

Even More Knowledge

Recursion

Dynamic Programming

Design patterns

Combinatorics (n choose k) & Probability

NP, NP-Complete and Approximation Algorithms

How computers process a program

Caches

Processes and Threads

Testing

String searching & manipulations

Tries

Floating Point Numbers

Unicode

Endianness

Networking

Final Review

Update Your Resume

Interview Process & General Interview Prep

Be thinking of for when the interview comes

Have questions for the interviewer

Once You’ve Got The Job

Additional Books

System Design, Scalability, Data Handling

Additional Learning

Compilers

Emacs and vi(m)

Unix/Linux command line tools

`awesome-aws` Python Module