Data Stream Processing in Elixir: Moving Average Traffic Predictor

In the world of real-time data, processing high-volume streams like social media traffic requires efficient, concurrent tools. Functional programming with Elixir is uniquely suited for handling these live data pipelines smoothly.\n\nThis course guides you through building a practical traffic predictor that calculates a moving average of incoming tweet counts using a sliding window approach. You will transition from basic functional concepts to managing stateful data streams with confidence.\n\nWhat you'll learn:\n- Understand the core principles of functional programming, pattern matching, and recursion in Elixir\n- Implement a sliding window algorithm to calculate rolling averages of simulated tweet traffic\n- Manage state concurrently across data streams using Elixir's GenServer and Agent abstractions\n- Process real-time data efficiently using modern Elixir Stream modules and concurrent pipelines\n- Write robust unit tests for your data processing logic using the ExUnit framework\n\nWe begin with foundational Elixir syntax and concurrent design patterns before guiding you step-by-step through designing, coding, and testing your traffic prediction logic. This text-only course is designed for beginner developers and data enthusiasts who want to learn stream processing, with no prior Elixir experience required. Start reading today to master real-time stream processing with Elixir.