Stream Processing Training in Big Data Environments
Level
AdvancedDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedStream Processing Training in Big Data Environments
An intensive, practical course on stream data processing designed for IT professionals who want to master advanced techniques of handling Big Data streams. The training combines solid theory with hands-on workshops, offering participants a comprehensive learning experience in modern streaming technologies.
What You Will Learn
- Advanced real-time stream data processing techniques
- Practical use of streaming tools in Big Data environments
- Designing scalable and efficient streaming solutions
- Implementing advanced stream processing algorithms with Python
Who is this training for?
Software engineers working with data processing-
Big Data system architects
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Python developers seeking to expand skills in stream processing
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Data analysts interested in modern information processing techniques
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Professionals in finance, telecommunications, and e-commerce, where real-time data processing is essential
Training Program
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Day 1: Introduction to Stream Processing
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Module 1: Theory of Stream Data Processing
- Characteristics and specifics of stream data processing
- Key challenges and paradigms in stream processing
- Comparison of different approaches to stream handling
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Module 2: Python Tools and Libraries
- Overview of streaming libraries: Apache Kafka, Apache Flink, Apache Spark Streaming
- Setting up a development environment for stream processing
- Installing and configuring selected tools
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Practical Workshop: First Steps with Streams
- Creating a simple stream processing system
- Implementing basic stream operations
- Handling data sources and transformations
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Day 2: Advanced Stream Processing Techniques
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Module 3: Architecture of Distributed Streaming Systems
- Design principles for distributed stream processing systems
- Partitioning and scaling strategies
- Ensuring reliability and resilience
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Module 4: Real-time Stream Processing
- Advanced transformation techniques
- Aggregations and stateful stream operations
- Handling delays and early/late events
- Implementing complex business scenarios
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Day 3: Practical Applications and Projects
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Module 5: Case Studies and Industry Projects
- Analysis of real-world stream processing scenarios
- Designing mini business projects
- Solving stream processing challenges
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Module 6: Advanced Techniques and Optimization
- Stream performance monitoring techniques
- Resource optimization strategies
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Final Workshop: Group Project
- Building a complete stream processing system
- Presentation and discussion of solutions
- Expert feedback
