Learn Egghead, Faunadb and Twit

This application serves as an introduction to building products with a JAMStack and Serverless approach. We cover everything from the beginning:

* starting with the Gatsby client
* handling authentication with Netlify Identity
* Shipping a GraphQL server on Netlify Functions
* Building in access control with a serverless capable database

It uses tools that remove as many of the barriers as possible while also allowing upgrading in the future. Netlify Functions can grow into AWS Lambda, Netlify Identity can grow into Auth0 or Cognito, and FaunaDB can grow into DynamoDB.

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

This collection is a sequel to the [Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB](https://egghead.io/playlists/building-a-serverless-jamstack-todo-app-with-netlify-gatsby-graphql-and-faunadb-53bb) collection. In this collection we take the TODO application we built and convert it to run using Netlify Identity, AWS Lambda (using the serverless framework) and DynamoDB. We cover

* Fauna vs DynamoDB and when to use each
* Setting up AWS accounts
* Creating DynamoDB tables and data modeling differences between Fauna and Dynamo
* Converting our Netlify Functions deployment to a Serverless Framework deployment
* Implementing Custom authorizer functions on AWS

It uses tools that remove as many of the barriers as possible. Netlify Functions grows into Serverless Framework adn AWS Lambda, Netlify Identity is kept around, and FaunaDB can grows into DynamoDB.



FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

Fauna is inspired by [Calvin](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/), has [built-in GraphQL integration](https://docs.fauna.com/fauna/current/api/graphql/), and a query language based on lambda calculus. Be sure to check out the [Jepsen analysis](https://jepsen.io/analyses/faunadb-2.5.4) and the associated [chart of consistency models](https://jepsen.io/consistency)

The Complete Guide to FaunaDB

In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

Twitter API Client for node

Supports both the REST and Streaming API.

twit

Create your own twitter bots

This is the video companion to the [egghead.io Instructor Guide](https://howtoegghead.com/instructor/).

Screencasting is hard! There are virtually infinite ways to produce one. For an egghead.io lesson we expect several key aspects to be met. We want to make sure that our lessons have depth and clarity. Clarity is important and starts with the technical aspects of recording your screen and voice. Once we have achieved this objective level of clarity, we dip into the stylistic aspects of recording a lesson. The style is subjective, and the goal of an egghead.io lesson is not to have an army of uniform drone producing flavorless video content for the masses.

Our mission is to make you a badass screencaster. We want to push you to another level. We've found that a small set of standard practices will create an awesome experience for the student, and eliminate frustration as we teach by providing a repeatable pattern for success. It’s not complicated. In fact, it is simple, but it isn’t easy.


This course will show you how to produce an egghead.io lesson. We strive for consistency in style, and these lessons will demonstrate the core elements that create our brand screencasts. Let’s learn how together.

Bite-size video tutorials for badass web developers.

egghead

Record Badass Screencasts for egghead.io

Miscellaneous one-off lessons on debugging, productivity hacks, and JavaScript tricks.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Tips and Tricks

The Chrome Developer Tools (DevTools for short), are a set of web authoring and debugging tools built into Google Chrome.

chrome-devtools

Your page is showing high CPU usage and spinning up your laptop fan, but nothing is visibly happening. What is it doing, and why is it so slow?

This lesson is a real-world performance diagnosis of this very page, right here on egghead. Two infinite (but hidden) animations were causing CPU usage to spike, and Chrome's Task Manager and Performance tools helped uncover the elements causing the problem. You'll see how to use the profiler to record a snapshot, and learn a trick for discovering animations running in the background.

Diagnose a Slow Page Using Developer Tools

codesandbox.io provides one of the fastest ways to start a new React project. In this lesson, we will take a first look at codesandbox, its interface, and the default React project it creates.

Use Codesandbox to Start a New React Application

By re-assigning a couple mouse buttons, you can drastically improve the speed you can edit your lessons in Screenflow. This lesson shows how to customize using the Logitech software, but the concept should work for any mouse customization software as well.

Faster Edits in Screenflow with Mouse Customizations

Sometimes you have an idea that you want to quickly record, but the idea of setting up a project just to get it recorded gets in your way. I'm going to walk your through scripting away the main setup pain points so that you can focus on creating content.

Automate egghead Lesson Setup with ZSH Shell Functions

FaunaDB is a Calvin inspired hosted database marketed towards JAMStack and serverless users. It is geo-replicated across multiple providers (AWS, GCP, and soon Azure), uses a lambda calculus based query language (FQL) or the built-in GraphQL support, and is quick to get started with. Don't worry about the vocabulary here (Consistency, lambda calculus, etc), FaunaDB can be quickly put to work without reading any papers and without knowing what lambda calculus is.

* [Calvin Paper](http://cs.yale.edu/homes/thomson/publications/calvin-sigmod12.pdf) (warning: pdf link)
  - [Morning Paper Overview of Calvin Paper](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/) if you don't want to read the whole paper
* [jepsen report](https://jepsen.io/analyses/faunadb-2.5.4)

An Introduction to FaunaDB

FQL is the main query language for interacting with FaunaDB. It supports all JSON types, plus some additional types like Ref, Date, and Set. FQL is based in expressions and as such everything returns a value. This makes it easy to compose logic into larger queries.

Code shown in the shell:

```
Match(Index("all_customers"))
```

Overview of the FaunaDB Query Language

When working with FQL it's often the case that you'll encounter `Ref`s. `Ref`s don't have any fields on them though! So how do we access the fields? `Get`.

```
Collections()
```

```
Collection("customers")
```

```
Ref(Collection("customers"), "244075679454331401")
```

```
Get(Ref(Collection("customers"), "244075679454331401"))
```

```
Get(Collection("customers"))
```



Refs and Get in FaunaDB

FQL Page objects have additional fields like `before`, and `data`, that may make it seem like you need to `Select` to get at the underlying array of results. However, FQL supports passing Page objects directly to functions that operate on arrays and the function will transparently operate on the contained `data` field.

```
Paginate(Match(Index("all_customers")))
```

```
Drop(1, Paginate(Match(Index("all_customers"))))
```

```
Take(1, Paginate(Match(Index("all_customers"))))
```

FQL Page objects can be arguments to array functions

`Ref`s are opaque. To access the fields on a document associated with a Ref, we first need to run `Get` on our results and then we can access fields using `Select`

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Select(["data", "firstName"], Get(Var("X"))))
)
```

Accessing fields through Refs with Select

We walk through creating a new Database in the FaunaDB console complete with some demo data. We do some exploration of the data, introducing concepts like Collections and Indexes, before showing an example FQL query that runs against the database. This sets us up to talk about FQL in future videos

Creating your first FaunaDB database

Overview of the FaunaDB Query Language (FQL)

When working with FQL it's often the case that you'll encounter Refs. Refs don't have any fields on them though! So how do we access the fields? Get.

```
Collections()
```

```
Collection("customers")
```

```
Ref(Collection("customers"), "244075679454331401")
```

```
Get(Ref(Collection("customers"), "244075679454331401"))
```

```
Get(Collection("customers"))
```

Getting fields out of Refs with Get

FQL Page objects have additional fields like before, and data, that may make it seem like you need to Select to get at the underlying array of results. However, FQL supports passing Page objects directly to functions that operate on arrays and the function will transparently operate on the contained data field.

```
Paginate(Match(Index("all_customers")))
```

```
Drop(1, Paginate(Match(Index("all_customers"))))
```

```
Take(1, Paginate(Match(Index("all_customers"))))
```

FQL Page objects can be arguments to Array functions

Lambdas allow you to use functions like Map or Filter effectively in FQL. In this lesson we should the Lambda syntax by operating on some paginated results.

```
Paginate(Match(Index("all_customers")))
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

Topics

Instructors

Content Type

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

The Complete Guide to FaunaDB

Create your own twitter bots

Record Badass Screencasts for egghead.io

Tips and Tricks

Diagnose a Slow Page Using Developer Tools

Use Codesandbox to Start a New React Application

Faster Edits in Screenflow with Mouse Customizations

Automate egghead Lesson Setup with ZSH Shell Functions

An Introduction to FaunaDB

Overview of the FaunaDB Query Language

Refs and Get in FaunaDB

FQL Page objects can be arguments to array functions

Accessing fields through Refs with Select

An Introduction to FaunaDB

Creating your first FaunaDB database

Overview of the FaunaDB Query Language (FQL)

Getting fields out of Refs with Get

FQL Page objects can be arguments to Array functions

Using FQL Lambda in Array functions