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

# Streams

We know that the `Stream` data structure is lazily evaluated and allows us to process data only when needed. The `Stream` module has lazy implementations of `map/2`, `filter/2`, and other functions. We can use `Stream.map/2` and `Stream.filter/2` to replace their `Enum` counterparts, but what about reading and parsing the file?

# Use `File.stream!`

Don't worry, Elixir has us covered. The `File` module has a lazy equivalent to `read!/1` called `stream!/1`, which is exactly what we need. The `CSV` module has `parse_stream/1`, which can replace `parse_string/1`. We have everything we need to convert our code from eager to lazy. Here's the new version:


Learn how streams reduce the time taken by the open_airports() function.

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

Improve Performance with Streams

Streams

Use `File.stream!`

Improve Performance with Streams

Streams

Use File.stream!

Use `File.stream!`