Introduction

Apache Storm is a powerful open-source, distributed real-time computation system that enables the processing of large streams of data with low latency and high throughput. It is highly suitable for real-time analytics, complex event processing, and real-time decision-making. This course is designed to help participants master the concepts and tools of Apache Storm, covering everything from stream processing fundamentals to advanced configurations and deployment techniques. Participants will learn how to design and implement real-time data processing pipelines using Apache Storm and integrate them with other tools and systems for scalable, low-latency data processing.

By the end of this course, participants will have the skills needed to build and deploy production-ready, real-time data processing solutions with Apache Storm.

Objectives

By the end of this course, participants will:

• Understand the fundamental concepts and components of real-time stream processing.
• Learn how to use Apache Storm for distributed, real-time data processing.
• Gain hands-on experience with Storm topology, spouts, bolts, and stream processing concepts.
• Understand the architecture of Apache Storm and how to configure and optimize it.
• Learn how to integrate Apache Storm with other big data tools like Apache Kafka and Hadoop.
• Understand the fault tolerance and reliability features of Apache Storm.
• Implement real-time data processing solutions and handle failure scenarios in Apache Storm topologies.
• Gain experience deploying and monitoring Apache Storm clusters in production environments.

Who Should Attend?

This course is ideal for:

• Data engineers, architects, and developers interested in real-time data processing.
• Big data professionals who want to implement low-latency and scalable data pipelines.
• Software engineers working with distributed systems or those looking to integrate stream processing in their applications.
• Data scientists and analysts interested in leveraging real-time data streams for analytics.

Day 1: Introduction to Real-Time Data Processing and Apache Storm

Morning Session: Introduction to Stream Processing

• What is stream processing? Comparing batch vs. real-time data processing.
• Use cases for real-time data processing: Financial systems, e-commerce, IoT, etc.
• Overview of stream processing architectures: Complex event processing, real-time analytics.
• The importance of low-latency, high-throughput systems in modern data applications.
• Hands-on: Understand and visualize the flow of data in a real-time data pipeline.

Afternoon Session: Introduction to Apache Storm

• What is Apache Storm? Overview of the system architecture and components.
• Storm topology: Spouts, bolts, and streams.
• Storm clusters and nodes: Master nodes, worker nodes, and supervisors.
• Key concepts in Apache Storm: Streams, tuples, tuple processing, and backpressure.
• Hands-on: Set up a simple Apache Storm cluster and run a basic topology.

Day 2: Building and Configuring Apache Storm Topologies

Morning Session: Understanding Spouts and Bolts

• Introduction to Spouts: What are Spouts, and how do they source streams of data?
• Introduction to Bolts: What are Bolts, and how do they process data in a Storm topology?
• Types of Bolts: Simple, streaming, and batch-processing bolts.
• Designing a topology: Choosing the right combination of Spouts and Bolts for specific tasks.
• Hands-on: Build a simple Storm topology with a Spout and a Bolt.

Afternoon Session: Advanced Topology Design

• Configuring and optimizing Spouts and Bolts for performance.
• Handling complex transformations and aggregations within bolts.
• Using Apache Kafka with Storm for real-time messaging and data ingestion.
• Fault tolerance in Storm topologies: How Storm ensures reliability and consistency.
• Hands-on: Integrate Storm with Apache Kafka to process streaming data.

Day 3: Advanced Storm Concepts and Fault Tolerance

Morning Session: Advanced Storm Features

• Stream grouping and field grouping: How to control the flow of data between components.
• Windowing in Storm: Sliding windows and tumbling windows for time-based processing.
• Trident API: Simplified stream processing with stateful and batch operations in Storm.
• State management in Storm: Using the Trident framework for maintaining state.
• Hands-on: Implement stateful stream processing with Trident.

Afternoon Session: Fault Tolerance and Scalability in Apache Storm

• The concept of tuple processing in Storm: Reliability and acking mechanisms.
• Ensuring fault tolerance: What happens when a failure occurs in a topology.
• Scalability considerations: Scaling out Storm clusters to handle larger data volumes.
• Optimizing performance: Tuning Storm components for optimal throughput and low-latency.
• Hands-on: Test fault tolerance in a Storm topology and analyze the behavior of the system during failures.

Day 4: Integrating Apache Storm with Big Data Ecosystems

Morning Session: Storm and Big Data Integration

• Integration with Apache Hadoop for batch processing and storage.
• Using Storm with HBase and Cassandra for real-time storage and retrieval.
• Data visualization and real-time analytics: Integrating Storm with BI tools like Tableau or custom dashboards.
• Stream processing pipelines with Apache Kafka: Ingesting and processing real-time streams in Storm.
• Hands-on: Set up a real-time Storm pipeline with HBase for real-time data storage.

Afternoon Session: Real-World Storm Applications

• Use cases for Apache Storm in various industries: IoT, social media analytics, fraud detection, and more.
• Implementing complex event processing with Storm.
• Real-time data monitoring and alerts.
• Case study: Designing a real-time analytics platform for monitoring e-commerce transactions.
• Hands-on: Implement a real-time monitoring solution for data anomalies using Apache Storm.

Day 5: Deploying and Monitoring Apache Storm in Production

Morning Session: Apache Storm Deployment

• Setting up a production-ready Storm cluster: Configuring Zookeeper and Nimbus.
• Best practices for deploying Apache Storm in a distributed environment.
• Running Storm topologies on cloud platforms like AWS or Google Cloud.
• Managing Storm clusters with Mesos or Kubernetes.
• Hands-on: Deploy a Storm topology in a cloud environment using AWS.

Afternoon Session: Monitoring, Performance Tuning, and Troubleshooting

• Tools for monitoring Apache Storm: Metrics, logging, and visualizations.
• Performance tuning: Identifying bottlenecks and optimizing resource usage.
• Common troubleshooting techniques and debugging Storm topologies.
• Best practices for scaling Storm clusters and managing resource utilization.
• Hands-on: Set up monitoring and performance tuning for an Apache Storm cluster.

Materials and Tools:

• Required tools: Apache Storm, Apache Kafka, HBase, Cassandra, AWS, Google Cloud, Tableau, Trident API.
• Sample datasets: IoT data, social media stream data, e-commerce transaction logs, and real-time sensor data.
• Recommended resources: Official documentation and online tutorials for Apache Storm, Apache Kafka, and related technologies.