Apache Storm Training
Introduction to Apache Storm
Apache Storm is a distributed real-time computation system designed for processing large streams of data. This module introduces Apache Storm, covering its core features, architecture, and use cases in real-time data processing and analytics.
Setting Up Apache Storm
Learn how to install and configure Apache Storm. This section covers system requirements, installation procedures, and initial setup. Explore how to configure Storm clusters and understand the basics of Storm’s user interface.
Storm Architecture and Components
Discover the architecture of Apache Storm, including its key components such as topologies, bolts, and spouts. Learn how Storm’s architecture supports real-time data processing and how to design efficient streaming data pipelines.
Creating and Managing Real-Time Data Pipelines
Gain insights into creating and managing real-time data pipelines in Apache Storm. Learn how to design streaming data workflows, configure components, and optimize data processing for performance. Explore how to handle real-time data transformation, enrichment, and aggregation.
Monitoring and Troubleshooting
Learn how to monitor and troubleshoot Apache Storm. Explore Storm’s monitoring tools, logs, and performance metrics. Understand techniques for diagnosing issues, managing system health, and ensuring reliable data processing.
Integration with Other Systems
Discover how to integrate Apache Storm with other systems and technologies. Learn about Storm’s connectors and integrations with databases, message queues, cloud services, and big data platforms. Explore how to use Storm for end-to-end real-time data processing.
Data Security and Access Control
Understand data security and access control in Apache Storm. Learn about authentication, authorization, and encryption. Explore how to secure data processing workflows, manage user access, and ensure compliance with security policies.
Performance Tuning and Optimization
Learn about performance tuning and optimization for Apache Storm. Explore techniques for improving data processing efficiency, managing system resources, and handling large volumes of real-time data. Understand best practices for configuring and maintaining Storm clusters.
Advanced Features and Customization
Explore advanced features and customization options in Apache Storm. Learn how to extend Storm with custom components and tools. Understand how to adapt Storm to meet specific real-time processing needs and use cases.
Apache Storm Training Syllabus
Introduction to Apache Storm
- Overview of Apache Storm: Features, benefits, and use cases
- History and evolution of real-time stream processing
- Comparison with other stream processing frameworks (e.g., Apache Kafka, Apache Flink)
Setting Up Apache Storm
- Installation and Configuration of Apache Storm
- Setting up Zookeeper for coordination and state management
- Configuring and deploying Storm clusters (single-node and multi-node setups)
Apache Storm Architecture
- Understanding Storm architecture components: Nimbus, Supervisors, Workers, Zookeeper
- Topologies and Tasks: Spouts, Bolts, and their roles in data processing
- Fault tolerance and reliability mechanisms in Storm
Developing Storm Topologies
- Writing Storm Topologies in Java: Creating Spouts and Bolts
- Implementing Real-time Data Processing: Data flow and transformations
- Configuring Parallelism: Strategies for task and worker parallelism
Data Model and Message Passing
- Tuples and Streams: Storm's data model for message passing
- Groupings and Stream Partitions: Controlling how data is distributed among tasks
- Using Fields Grouping, Shuffle Grouping, and Custom Grouping
Stream Processing Patterns
- Common Stream Processing Patterns: Filtering, Transformation, Aggregation
- Windowing and Time-based Operations: Sliding and tumbling windows
- Exactly-once Processing Semantics: Achieving message processing guarantees
Trident API for Stateful Stream Processing
- Introduction to Trident: High-level abstraction for stateful processing
- Trident Topologies: Using stateful operators and transactions
- Integrating Trident with existing Storm topologies
Integrating Storm with Data Sources
- Integration with Messaging Systems: Apache Kafka, RabbitMQ
- Reading and Writing to Databases: Using Storm with JDBC connectors
- Implementing Custom Spouts and Bolts for specific data sources
Performance Tuning and Optimization
- Monitoring Storm Clusters: Metrics and monitoring tools
- Tuning Storm Configurations: JVM settings, parallelism hints
- Scaling and managing large-scale Storm deployments
Security and Reliability in Storm
- Securing Storm Clusters: Authentication and authorization mechanisms
- Configuring SSL/TLS for secure communication within Storm
- Handling Failures and Recovery: Strategies for fault tolerance
Advanced Topics in Apache Storm
- Advanced Bolt Techniques: Implementing custom stateful bolts, complex event processing (CEP)
- Machine Learning with Storm: Real-time predictive analytics and model deployment
- IoT Use Cases: Handling Internet of Things (IoT) data streams with Storm
Real-world Use Cases and Projects
- Implementing Apache Storm in Production: Case studies across industries
- Project Work: Hands-on projects to apply learned concepts
- Designing and deploying Storm topologies for specific business requirements
Career Development and Job Preparation
- Building a career in Real-time Data Processing: Skills and certifications
- Interview Preparation: Apache Storm-related interview questions
- Freelancing and Consulting Opportunities in real-time stream processing
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details