Master Real-Time Stream Processing with Apache Storm: Comprehensive Certification Course

In the fast-paced world of big data, real-time processing is essential for businesses that need to react
to data as it arrives. Apache Storm is a powerful, distributed, real-time computation system
designed to process unbounded streams of data with low latency. It is used extensively for real-time
analytics, machine learning, and monitoring use cases across a wide variety of industries.

ENCODE-IT’s Comprehensive Apache Storm Certification Course will give you the knowledge and
hands-on experience to work with Apache Storm for processing real-time data streams. From the
basics of setting up Storm clusters to designing complex topologies and integrating with other big
data tools, this course covers everything you need to master Apache Storm and utilize it for real-
time analytics, monitoring, and event-driven applications.
Whether you are a data engineer, software developer, or IT professional, this course will equip you
with the skills to build scalable and efficient real-time data processing pipelines using Apache Storm.

Salary Scale in India
Apache Storm expertise is in high demand as organizations adopt real-time data processing
solutions. The average salary for entry-level roles such as Apache Storm Developers or Data
Engineers ranges from ₹7,00,000 to ₹12,00,000 annually in India. With experience, professionals in
roles like Apache Storm Architect or Senior Data Engineer can earn ₹15,00,000 to ₹25,00,000
annually, depending on the scope and scale of the projects. Industries such as e-commerce, finance,
and telecom are increasingly leveraging real-time analytics, offering ample opportunities for those
skilled in Apache Storm.

Placement Assistance & Certification
Upon successful completion of the Comprehensive Apache Storm Certification Course, you will
receive an official certificate from ENCODE-IT, validating your expertise in real-time data processing
with Apache Storm. ENCODE-IT also offers placement assistance to connect you with leading
companies looking for professionals skilled in real-time stream processing. This dual benefit ensures
your knowledge is put to work immediately in a rewarding career.

Course Curriculum

1. Introduction to Apache Storm and Real-Time Stream Processing
 Overview of Apache Storm: Purpose, Use Cases, and Architecture
 Why Real-Time Stream Processing is Critical for Modern Applications
 Comparing Apache Storm with Other Stream Processing Technologies (Spark Streaming,
Flink, etc.)
 Storm’s Role in Big Data Ecosystems: Integration with Hadoop, Kafka, and Other Tools
 Installing Apache Storm and Setting Up a Basic Cluster
 Understanding the Core Concepts of Storm: Spouts, Bolts, Topologies

2. Storm Cluster Architecture and Setup
 Understanding Storm’s Cluster Architecture: Nimbus, Supervisor, Zookeeper
 Setting Up a Local and Distributed Storm Cluster

 Managing and Configuring Cluster Nodes and Topology Workers
 Monitoring and Troubleshooting Storm Clusters
 Scaling Storm: Adding More Nodes and Handling Large-Scale Data Streams
 Managing Fault Tolerance and Ensuring Reliability in a Storm Cluster

3. Storm Topology Design and Execution
 Introduction to Topologies in Apache Storm
 Designing Spouts and Bolts for Stream Processing
 Configuring Stream Processing Logic: Spouts, Bolts, and Streams
 Creating Complex Topologies: Connecting Spouts and Bolts
 Understanding Storm’s Tuple-Based Data Flow Mechanism
 Best Practices for Building Scalable and Efficient Topologies

4. Working with Storm Spouts and Bolts
 Understanding Spouts: The Data Source for Storm
 Implementing Custom Spouts for Various Data Sources (Kafka, Files, Databases)
 Defining Bolts: The Processing Units in Storm Topologies
 Implementing Custom Bolts for Data Transformation, Aggregation, and Processing
 Understanding Storm’s Windowing and Grouping Mechanisms
 Handling Stream Processing in a Stateful Way: Managing State in Bolts

5. Data Processing with Apache Storm
 Real-Time Data Transformation and Aggregation with Storm
 Event-Driven Processing and Low-Latency Operations in Storm
 Stream Processing with Stateful and Stateless Bolts
 Implementing Complex Event Processing (CEP) in Storm
 Windowing in Storm: Sliding, Tumbling, and Session Windows
 Stream Joining and Combining Data from Multiple Streams

6. Integrating Apache Storm with Other Big Data Tools
 Integration with Apache Kafka for Real-Time Data Streaming
 Using Apache Cassandra and HBase for Storing Processed Data
 Integrating Storm with Hadoop for Batch and Real-Time Processing
 Data Ingestion Techniques: Using Storm with REST APIs and Databases
 Real-Time Dashboards and Visualization: Integrating Storm with BI Tools

 Connecting Storm with Spark for Advanced Stream Processing and Analytics

7. Storm Performance Optimization and Troubleshooting
 Optimizing Storm’s Performance: Parallelism, Task Distribution, and Resource Management
 Managing Load Balancing and Scaling Topologies for High-Volume Data
 Fine-Tuning Spouts and Bolts for Maximum Throughput and Low Latency
 Memory Management and Garbage Collection in Storm
 Monitoring Topology Performance with Storm’s Metrics and Logging System
 Debugging Storm Topologies and Resolving Common Issues

8. Advanced Real-Time Analytics with Apache Storm
 Implementing Real-Time Analytics Pipelines with Apache Storm
 Real-Time Machine Learning Model Inference using Storm
 Anomaly Detection and Predictive Analytics in Stream Processing
 Integrating Storm with External Machine Learning and AI Libraries
 Real-Time Monitoring and Alerting Systems Powered by Storm
 Case Studies: Real-World Applications of Apache Storm in E-commerce, Finance, and IoT

9. Security and Fault Tolerance in Apache Storm
 Securing Apache Storm Clusters: Authentication and Authorization
 Data Security Best Practices for Storm Streams
 Ensuring Fault Tolerance in Stream Processing with Storm
 Implementing Data Integrity and Consistency in Real-Time Systems
 Handling Failures in Spouts, Bolts, and Topologies
 Using Backpressure and Reliable Messaging for Fault Recovery

10. Final Project and Certification Exam
 Real-World Project: Building a Real-Time Stream Processing Application with Apache Storm
 Design, Implement, and Deploy a Complete Storm-Based Solution
 Integrating Apache Storm with External Data Sources and Databases
 Performance Tuning, Monitoring, and Troubleshooting for Real-Time Systems
 Certification Exam: Comprehensive Assessment to Validate Apache Storm Expertise
 Certification of Completion and Placement Assistance

Key Features

 Tools & Platforms: Apache Storm, Apache Kafka, Apache Zookeeper, Hadoop, Apache
Cassandra
 Real-World Projects: Hands-on projects and case studies to apply Apache Storm in real-time
processing scenarios
 Certification & Placement Support: Official certification from ENCODE-IT and job placement
assistance
 Expert Instructors: Learn from industry experts with practical experience in real-time stream
processing
 Career Advancement: Develop in-demand skills in real-time data processing, analytics, and
event-driven applications

Why Choose ENCODE-IT for Apache Storm Certification?
ENCODE-IT’s Comprehensive Apache Storm Certification Course provides you with the foundational
knowledge and practical skills to master real-time data stream processing. This course covers
everything from setting up clusters to optimizing performance, designing topologies, and integrating
with other big data tools. By the end of the course, you will be equipped to build robust, low-latency
data processing systems with Apache Storm, enhancing your career prospects in industries like
finance, healthcare, e-commerce, and telecommunications. Enroll today and start mastering Apache
Storm to advance your career in real-time analytics and stream processing!