Stream Processing in Big Data Environments Training
Catalogue index: IFS/87
product- line 1
Features
Description
InfoShare Academy is a leading IT academy offering comprehensive educational programs in new technologies for companies. Since 2015, we have been supporting organizations in developing technology teams through dedicated courses in Machine Learning, DevOps, Data Engineering, Python, UX/UI Design, AWS, and Kubernetes. Our training is based on practical skills and real business cases. We collaborate with over 300 industry practitioners, ensuring that our programs are tailored to current market needs. We specialize in reskilling and upskilling employees. With us, you will build effective teams implementing new technologies that will accelerate innovation and strengthen your company's competitiveness in the market. Check out our training offerings designed for companies, created to develop your employees' competencies in the IT field.
An intensive, practical course on data stream processing aimed at IT professionals looking to master advanced techniques for handling Big Data streams. The training combines solid theory with intensive practical workshops, offering participants comprehensive learning opportunities in modern streaming technologies.
- Software engineers involved in data processing
- Big Data system architects
- Python developers looking to enhance their skills in stream processing
- Data analysts interested in modern information processing techniques
- Specialists from industries such as finance, telecommunications, e-commerce, which require real-time data processing
- You will learn:
- Advanced techniques for real-time data stream processing
- Practical use of streaming tools in Big Data environments
- Designing scalable and efficient streaming solutions
- Implementing advanced stream processing algorithms using Python
Day 1: Introduction to Stream Processing
Module 1: Theory of Data Stream Processing
Characteristics and specifics of data stream processing
Key challenges and paradigms in stream processing
Comparison of different approaches to data stream handling
Module 2: Python Tools and Libraries
Overview of streaming libraries: Apache Kafka, Apache Flink, Apache Spark Streaming
Setting up the development environment for stream processing
Installation and configuration of selected tools
Practical workshop: First steps with streams
Creating a simple stream processing system
Implementing basic stream operations
Handling data sources and transformations
Day 2: Advanced Stream Processing Techniques
Module 3: Architecture of Distributed Streaming Systems
Design principles for distributed stream processing systems
Partitioning and scaling strategies
Reliability and resilience mechanisms
Module 4: Real-time Stream Processing
Advanced stream transformation techniques
Aggregations and stateful operations in streams
Handling delays and early/late events
Implementing complex business scenarios
Day 3: Practical Applications and Projects
Module 5: Case studies and industrial projects
Analysis of real use cases of stream processing
Designing mini-business projects
Solving stream processing issues
Module 6: Advanced techniques and optimization
Techniques for monitoring stream performance
Optimizing resource consumption
Final workshop: Group project
Independently creating a data stream processing system
Presentation and discussion of solutions
Expert feedback
24 hours/3 days
- Certificate of completion
- Monthly access to the training recording (for online format)
- Customization of the training program to client needs