What will you learn in Apache Storm Certification Training Course
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Grasp the fundamentals of real-time stream processing with Apache Storm.
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Architect and deploy Storm clusters using Zookeeper and Nimbus.
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Develop spouts and bolts to ingest and process data streams.
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Build and optimize topologies, including grouping and parallelism strategies.
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Implement windowing, triggers, and stateful computations for complex event processing.
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Integrate Storm with Kafka, Cassandra, and other data stores for end-to-end pipelines.
Program Overview
Module 1: Introduction & Environment Setup
⏳ 1 hour
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Topics: Overview of real-time analytics, Storm ecosystem, installation of Java, Storm, and Zookeeper.
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Hands-on: Set up a local Storm cluster and run the “Word Count” example topology.
Module 2: Storm Architecture & Components
⏳ 1.5 hours
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Topics: Nimbus, Supervisors, Workers, Zookeeper coordination, Storm UI.
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Hands-on: Explore cluster metrics in Storm UI and scale workers.
Module 3: Spouts and Bolts
⏳ 2 hours
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Topics: Defining spouts for data ingestion, implementing bolts for processing, anchoring and acknowledgements.
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Hands-on: Write custom spouts/bolts in Java or Python and test locally.
Module 4: Topology Design & Stream Grouping
⏳ 2 hours
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Topics: Stream groupings (shuffle, fields, all), parallelism hints, fault tolerance.
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Hands-on: Design and deploy a multi-stage topology with different groupings.
Module 5: Windowing & Triggers
⏳ 1.5 hours
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Topics: Time-based and count-based windows, sliding vs. tumbling, triggers.
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Hands-on: Implement a tumbling window to compute rolling metrics.
Module 6: Stateful Processing
⏳ 1.5 hours
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Topics: Maintaining state across tuples, checkpointing, state storage options.
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Hands-on: Build a stateful bolt to track running aggregates.
Module 7: Integration with External Systems
⏳ 2 hours
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Topics: Connecting Storm to Kafka for ingestion, Cassandra/HBase for storage.
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Hands-on: Ingest messages from Kafka and write results to Cassandra tables.
Module 8: Monitoring, Management & Optimization
⏳ 1 hour
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Topics: Metrics collection, tuning parallelism, latency vs. throughput trade-offs.
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Hands-on: Profile a topology, adjust parallelism, and measure performance improvements.
Module 9: Real-World Use Case & Capstone Project
⏳ 2 hours
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Topics: End-to-end real-time analytics pipeline for log processing or clickstream analysis.
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Hands-on: Deliver a complete Storm application that ingests, processes, and stores streaming data.
Get certificate
Job Outlook
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Real-time data engineers and streaming specialists are in high demand in finance, e-commerce, and IoT.
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Roles include Big Data Engineer, Stream Processing Engineer, and Real-Time Analytics Developer.
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Salaries typically range from $110K–$150K USD, with premium for cloud-native streaming expertise.
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Storm skills complement Kafka, Spark Streaming, and Flink knowledge for a competitive edge.
Explore More Learning Paths
Expand your knowledge in real-time data processing and big data analytics with these related courses and resources. These learning paths will help you build expertise in distributed systems and scalable data pipelines.
Related Courses
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Apache Kafka Certification Training
Learn to manage real-time data streams and create robust, scalable data pipelines for modern applications. -
Apache Cassandra Certification Training
Understand distributed database management and techniques for storing and retrieving high-volume structured data.
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