Gain insights into data processing and concurrency in Elixir. Explore GenServer, GenStage, Flow, and Broadway to enhance performance, set up pipelines, and handle increased workloads efficiently.

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Most engineers don’t think that concurrency is distinct from programming. Data processing includes transforming information in some way, like reporting, data aggregation, or analytics. On the other hand, concurrency involves significantly improving performance.

This course is designed to help you navigate the world of concurrency tools available in the Elixir ecosystem. You’ll explore popular modules and libraries like GenServer, GenStage, Flow, and Broadway. You’ll be able to ingest data, set up pipelines, and run concurrent processes with Elixir.

By the end of the course, you’ll have a wide range of new techniques to simplify your product, improve code performance, and make your application more resilient to increased workloads.

Concurrent Data Processing in Elixir

# The `Enum` and `Stream` modules

Since Elixir is a functional language and all data is immutable, most Elixir developers quickly get accustomed to using functions like `map`, `filter`, and `reduce` on a daily basis. These and other data-processing functions, found in the `Enum` and `Stream` modules, are essential to functional programming and help us transform data in various ways.


# The limitations of `Enum` and `Stream`
 
However, as the amount of data we have to process grows, so does the time it takes to finish the work. We already have a few tools at our disposal to run code concurrently, but implementing frequently used functions like `reduce` and `group_by` in parallel is going to be challenging. Thankfully, there is already a solution available to us on the `Hex` registry.

# Enter `Flow`

In this chapter, we'll learn about `Flow`---a powerful library with a simple API that makes processing large collections of data a breeze. The `Flow` library uses `GenStage` under the hood, so all operations will run parallel in separate stage processes and take care of back-pressure for us.

# Compare `Flow` with `Enum` and `Stream`

First, we'll introduce `Flow` and comparE it to the commonly used `Enum` and `Stream` modules by analyzing airport data from around the world. We'll see how easy it is to convert existing code to run concurrently and work with large datasets. Then, we'll look at how to run `reduce` operations concurrently and handle infinite and slow streams of data. Finally, we'll revisit the `scraper` project and integrate `Flow` with an already running `GenStage` pipeline. This will give us some extra flexibility when solving problems. Let's get started!

# Create a new `mix` project

Before we begin, let’s scaffold a new project to work on. We’ll build a simple utility to help us analyze airport data by country. We’ll see which countries and territories have the largest number of working airports globally. Let’s call this application `airports`:

```shell
 mix new airports
```

## Include `flow` as a dependency

Now, let’s edit `mix.exs` to add `flow` as a dependency. We’ll also need a CSV parser. Therefore, we change our dependencies list to the following:

Get an overview of the Flow library in Elixir and then create a new mix project containing this dependency.

Getting Started

Easy Concurrency with the Task Module

Long-Running Processes Using GenServer

Data Processing Pipelines with GenStage

Process Collections with Flow

Data Ingestion Pipelines with Broadway

Concluding the Course

Introduction to the Flow Library

The `Enum` and `Stream` modules

The limitations of `Enum` and `Stream`

Introduction to the Flow Library

The Enum and Stream modules

The limitations of Enum and Stream

The `Enum` and `Stream` modules

The limitations of `Enum` and `Stream`