Complete Course of Apache Storm

Apache Storm is a real-time, distributed stream processing system designed to process unbounded streams of data with low latency and high fault tolerance. It is widely used for applications requiring real-time analytics, event processing, and continuous data computation, such as fraud detection, monitoring systems, and log processing. Learning Apache Storm is crucial for big data professionals, as it is a popular choice for real-time data processing in industries like finance, e-commerce, and IoT. Mastering Storm enhances your ability to build scalable and fault-tolerant systems for processing high-velocity data streams.
A tutor can accelerate this process by offering a structured learning path, practical examples, and hands-on projects, helping you understand Storm's architecture, concepts like spouts and bolts, and its integration with other big data tools, making you job-ready faster.

Chapter 1: Introduction to Distributed Streaming and Big Data Processing

Lesson 1: What Is Distributed Stream Processing?
Lesson 2: Fundamentals of Big Data in the Context of Real-Time Processing
Lesson 3: Challenges of Real-Time Data Streams and How They Differ from Batch Processing
Lesson 4: Overview of Big Data Tools for Streaming (e.g., Apache Storm, Spark Streaming, Flink)
Lesson 5: Comparing Apache Storm with Other Streaming Tools
Lesson 6: Why Real-Time Processing Matters in Modern Big Data Applications

Chapter 2: Introduction to Apache Storm

Lesson 1: What Is Apache Storm and Its Role in Real-Time Data Processing?
Lesson 2: History and Evolution of Apache Storm
Lesson 3: Key Features and Architectural Advantages of Storm
Lesson 4: Real-World Use Cases and Success Stories
Lesson 5: How Apache Storm Addresses the Unique Challenges of Streaming Data

Chapter 3: Setting Up Apache Storm

Lesson 1: System Requirements and Prerequisites for Storm Deployment
Lesson 2: Installing Apache Storm in Local Mode vs. Cluster Mode
Lesson 3: Configuring Core Components (Nimbus, Supervisor, and Zookeeper)
Lesson 4: IDE Setup for Storm Development (Eclipse, IntelliJ, etc.)
Lesson 5: Overview of Storm Configuration Files (storm.yaml, log4j configuration, etc.)

Chapter 4: Apache Storm Architecture and Core Concepts

Lesson 1: Overview of Storm’s Distributed Architecture (Nimbus, Supervisor, Zookeeper, Storm UI)
Lesson 2: Understanding Spouts and Bolts: The Building Blocks of a Topology
Lesson 3: The Concept of Tuples, Streams, and Groupings
Lesson 4: Fault Tolerance, Reliability, and Acknowledgment Mechanisms
Lesson 5: Resource Management and Scheduler Overview in Storm

Chapter 5: Developing and Deploying Storm Topologies

Lesson 1: Designing a Storm Topology: Concepts and Best Practices
Lesson 2: Writing a Simple Topology: A Step-by-Step Code Walkthrough
Lesson 3: In-Depth Exploration of Storm’s API for Spouts, Bolts, and Streams
Lesson 4: Testing and Debugging Topologies in Local Mode
Lesson 5: Deploying Topologies to a Storm Cluster
Lesson 6: Using Storm Commands to Submit, Kill, and Monitor Topologies

Chapter 6: Apache Storm Command-Line Tools and Commands

Lesson 1: Overview of the Storm CLI and Its Importance in Administration
Lesson 2: Detailed Breakdown of Storm Commands (submit, kill, list, backtype, etc.)
Lesson 3: Managing Topologies and Monitoring Cluster Health Through CLI
Lesson 4: Editing and Utilizing Storm Configuration Files via Command Line
Lesson 5: Automating Storm Operations with Shell Scripts and Command Chaining

Chapter 7: Metrics, Monitoring, and Troubleshooting in Storm

Lesson 1: Introduction to Storm’s Built-In Metrics and Logging Facilities
Lesson 2: Using the Storm UI to Monitor Real-Time Topology Performance
Lesson 3: Integrating External Monitoring Tools (e.g., Prometheus, Grafana)
Lesson 4: Common Pitfalls and Techniques for Debugging Storm Topologies
Lesson 5: Analyzing Storm Logs and Diagnostic Information
Lesson 6: Best Practices for Proactive Troubleshooting and Maintenance

Chapter 8: Advanced Stream Processing Concepts in Storm

Lesson 1: Advanced Windowing and Time-Based Operations in Storm
Lesson 2: Managing State: Stateful vs. Stateless Processing in Storm
Lesson 3: Deep Dive into Storm Trident for High-Level Stream Processing
Lesson 4: Implementing Reliability and Handling Backpressure in Complex Topologies
Lesson 5: Techniques for Load Balancing and Resource Optimization

Chapter 9: Integrating Apache Storm with Other Big Data Tools

Lesson 1: Ingesting Data with Apache Kafka and Other Messaging Systems
Lesson 2: Integrating with NoSQL Databases (e.g., Cassandra, HBase) for Storage
Lesson 3: Combining Storm with Apache Spark for Hybrid Data Processing
Lesson 4: Real-Time Analytics with Storm and Elasticsearch
Lesson 5: Connecting Storm with External Systems (REST APIs, IoT Platforms)

Chapter 10: Security, Scalability, and Performance Optimization in Storm

Lesson 1: Security Fundamentals: Authentication, Authorization, and Data Encryption
Lesson 2: Configuring Storm for Multi-Tenancy and Resource Isolation
Lesson 3: Performance Tuning: Optimizing Spout and Bolt Execution
Lesson 4: Scalability Strategies: Dynamic Resource Allocation and Auto-Scaling
Lesson 5: Best Practices for Managing and Maintaining a High-Availability Storm Cluster

Chapter 11: Apache Storm Advanced Features and New Releases

Lesson 1: Overview of New Features in the Latest Apache Storm Releases
Lesson 2: Enhancements in Fault Tolerance, Throughput, and Latency
Lesson 3: Updated CLI Commands and Configuration Options in New Versions
Lesson 4: Migrating Existing Topologies to Newer Storm Versions
Lesson 5: Exploring the Roadmap and Future Trends in Storm Development

Chapter 12: Fault Tolerance, Reliability, and Debugging Strategies

Lesson 1: Understanding Storm’s Fault Tolerance Mechanisms
Lesson 2: Handling Node Failures and Automatic Recovery Techniques
Lesson 3: Best Practices for State Management and Transactional Topologies
Lesson 4: Debugging Complex Topologies: Tools and Techniques
Lesson 5: Load Testing and Simulating Failure Scenarios for Resilience

Chapter 13: Real-World Applications and Case Studies of Apache Storm

Lesson 1: Apache Storm in Financial Services: Real-Time Analytics and Fraud Detection
Lesson 2: Storm Applications in IoT and Sensor Data Processing
Lesson 3: Real-Time Monitoring and Anomaly Detection Use Cases
Lesson 4: Case Studies from Social Media, Ad Tech, and E-Commerce
Lesson 5: Lessons Learned from Large-Scale Storm Deployments

Chapter 14: Storm Ecosystem and Community Contributions

Lesson 1: Overview of the Apache Storm Ecosystem and Related Tools
Lesson 2: Popular Extensions and Libraries that Enhance Storm Functionality
Lesson 3: How to Contribute to the Storm Open-Source Project
Lesson 4: Leveraging Community Resources and Best Practices
Lesson 5: Keeping Up-to-Date: Forums, Conferences, and Training Resources

Chapter 15: Storm Cluster Setup, Administration, and Management

Lesson 1: Planning and Deploying a Production-Grade Storm Cluster
Lesson 2: Best Practices for Cluster Configuration and Resource Allocation
Lesson 3: Administration Tools for Managing Storm Clusters
Lesson 4: Backup, Recovery, and Disaster Management Strategies
Lesson 5: Upgrading and Maintaining Storm Clusters in Production Environments

Chapter 16: Advanced Storm Commands and Automation Techniques

Lesson 1: Scripting and Automating Storm CLI Commands for Efficiency
Lesson 2: Customizing Storm Configurations for Specific Workloads
Lesson 3: Advanced Command Options for Debugging and Performance Tuning
Lesson 4: Integrating Automation Tools (Ansible, Chef, Puppet) with Storm
Lesson 5: Building Custom Tools and Utilities for Cluster Management

Chapter 17: Future Trends and Emerging Technologies in Real-Time Stream Processing

Lesson 1: The Evolving Landscape of Real-Time Data Processing Frameworks
Lesson 2: Comparing Apache Storm with Emerging Technologies (e.g., Kafka Streams, Flink)
Lesson 3: Cloud-Native Real-Time Processing and Serverless Architectures
Lesson 4: Impact of AI and Machine Learning on Streaming Data Applications
Lesson 5: Preparing for the Future: Next-Generation Storm Applications and Best Practices

The online class is held via Skype (or Zoom or Microsoft Teams) and the cost per hour of tutoring is only $15. At the end of this long course, you will master all the required basic and advanced concepts of Apache Storm and we will develop a real world project together for about 10 hours, that fully prepares you to find a job as a professional Database Administrator or entry-level Big Data Engineer.
To book this class, message or call my telegram or WhatsApp:
+98 (912) 490-8372 or +98 (935) 490-8372
You can also send email to me:
abolfazl.mohammadijoo@gmail.com

Complete Course of Apache Storm

Course Duration: about 50+10 hours

GET IN TOUCH