Apache Storm Certification Training Course Syllabus

This self-paced course provides a comprehensive introduction to Apache Storm for building scalable, real-time stream processing systems. Designed for beginners, it spans approximately 13 hours of content, combining theoretical concepts with hands-on labs. You'll learn to set up Storm clusters, design topologies with spouts and bolts, implement stream groupings, and integrate with external systems like Kafka and Cassandra. The course concludes with a capstone project that reinforces end-to-end pipeline development. With lifetime access and practical exercises, this program prepares learners for roles in real-time data engineering.

Module 1: Introduction & Environment Setup

Estimated time: 1 hours

• Overview of real-time analytics
• Understanding the Storm ecosystem
• Installation of Java, Storm, and Zookeeper
• Hands-on: Set up a local Storm cluster
• Run the “Word Count” example topology

Module 2: Storm Architecture & Components

Estimated time: 1.5 hours

• Role of Nimbus and Supervisors
• Worker processes and execution model
• Zookeeper coordination in Storm
• Using the Storm UI for monitoring
• Scale workers in a running cluster

Module 3: Spouts and Bolts

Estimated time: 2 hours

• Defining spouts for data ingestion
• Implementing bolts for stream processing
• Understanding anchoring and acknowledgements
• Hands-on: Write custom spouts and bolts in Java or Python
• Test topologies in local mode

Module 4: Topology Design & Stream Grouping

Estimated time: 2 hours

• Stream groupings: shuffle, fields, all
• Parallelism hints and task distribution
• Designing multi-stage topologies
• Fault tolerance mechanisms in Storm
• Deploy and monitor a topology

Module 5: Windowing & Triggers

Estimated time: 1.5 hours

• Time-based and count-based windows
• Sliding vs. tumbling windows
• Configuring triggers for window emission
• Hands-on: Implement a tumbling window for rolling metrics

Module 6: Stateful Processing

Estimated time: 1.5 hours

• Maintaining state across tuples
• Checkpointing for fault-tolerant state
• State storage options in Storm
• Hands-on: Build a stateful bolt for running aggregates

Module 7: Integration with External Systems

Estimated time: 2 hours

• Connecting Storm to Kafka for ingestion
• Writing to Cassandra and HBase
• End-to-end data pipeline patterns
• Hands-on: Ingest from Kafka and write to Cassandra

Module 8: Monitoring, Management & Optimization

Estimated time: 1 hours

• Collecting and interpreting metrics
• Tuning parallelism for performance
• Latency vs. throughput trade-offs
• Hands-on: Profile and optimize a topology

Module 9: Real-World Use Case & Capstone Project

Estimated time: 2 hours

• Design a real-time log processing pipeline
• Ingest, process, and store streaming data
• Deliver a complete Storm application

Prerequisites

• Basic knowledge of Java or Python
• Familiarity with command-line tools
• Understanding of distributed systems concepts

What You'll Be Able to Do After

• Architect and deploy real-time stream processing pipelines using Apache Storm
• Design and optimize Storm topologies with appropriate stream groupings
• Develop custom spouts and bolts for data ingestion and transformation
• Integrate Storm with Kafka and Cassandra for end-to-end solutions
• Implement windowing, triggers, and stateful processing for complex event handling