Learn By Example : Apache Storm

Storm is a powerful real-time stream processing framework, much like Hadoop is for batch processing. This course dives into building applications that require instantaneous responsiveness to incoming data, reacting within seconds or minutes. Imagine identifying trending topics on Twitter or monitoring payment gateway failures in real-time – that's the power of Storm.

You'll learn to handle everything from simple data transformations to applying machine learning algorithms on the fly. This course provides 25 solved examples to guide you through building robust Storm applications.

Here's a detailed look at what you'll master:

• Spouts and Bolts: Understand the fundamental building blocks of any Storm topology. Learn how these components work together to process data streams.
• Storm Topology Deployment: Deploy and run your Storm topologies in both local mode for development and remote mode for production environments.
• Data Parallelization: Effectively parallelize data processing within your topology using various grouping strategies:
  • Shuffle Grouping
  • Fields Grouping
  • Direct Grouping
  • All Grouping
  • Custom Grouping
• Reliability & Fault Tolerance: Implement strategies to ensure the reliability and fault-tolerance of your Spouts and Bolts, guaranteeing continuous data processing.
• Complex Transformations with Trident: Perform sophisticated data transformations on the fly using the Trident topology, including:
  • Map operations
  • Filter operations
  • Windowing operations
  • Partitioning operations
• Machine Learning with Storm: Integrate machine learning algorithms directly into your real-time data pipelines using libraries like Trident-ML and Storm-R.

This course equips you with the skills to build scalable, real-time data processing applications with Storm.