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

## `Broadway`

In this chapter, we learned about `Broadway`, which not only simplifies working with message brokers but also takes away the complexity of assembling data-processing pipelines. 

## Configure our pipeline and using processors

We configured our pipeline to connect to a RabbitMQ server and consume events. We used processors to perform work concurrently and batchers for further processing in bulk. 

## Make our producer

Finally, we saw how to make our producer from scratch, showing how versatile `Broadway` can be.


## Additional features

There are even more features in `Broadway` that we don't explore in this course but are well documented online. Examples include rate limiting, partitioning, and metrics exposed using the `telemetry` library. Various helpers make testing `Broadway` pipelines easy. We encourage you to check the [official documentation](https://hexdocs.pm/broadway) to learn more.



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# `Broadway`

In this chapter, we learned about `Broadway`, which not only simplifies working with message brokers but also takes away the complexity of assembling data-processing pipelines. 

# Configure our pipeline and using processors

We configured our pipeline to connect to a RabbitMQ server and consume events. We used processors to perform work concurrently and batchers for further processing in bulk. 

# Make our producer

Finally, we saw how to make our producer from scratch, showing how versatile `Broadway` can be.


# Additional features

There are even more features in `Broadway` that we don't explore in this course but are well documented online. Examples include rate limiting, partitioning, and metrics exposed using the `telemetry` library. Various helpers make testing `Broadway` pipelines easy. We encourage you to check the [official documentation](https://hexdocs.pm/broadway) to learn more.



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Get a recap of what we've learned in this chapter.

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

Wrapping Up

`Broadway`

Configure our pipeline and using processors

Make our producer

Additional features