Course Outline
Course Syllabus: Day 1
• Foundations of data streaming concepts
• Comparing batch versus real-time processing models
• Essentials of event-driven architecture
• Industry-specific use cases and applications
• Landscape of the current streaming ecosystem
Day 2
• Design patterns for streaming architectures
• Core principles of distributed messaging systems
• Roles of producers and consumers
• Mechanics of topics, partitions, and data flow
• Strategies for effective data ingestion
Day 3
• Stream processing principles and available frameworks
• Distinguishing event time from processing time
• Windowing techniques and their practical applications
• Stateful stream processing workflows
• Basics of fault tolerance and checkpointing
Day 4
• Data transformation within streaming pipelines
• ETL and ELT patterns in real-time contexts
• Schema management and evolutionary strategies
• Stream joins and data enrichment processes
• Overview of cloud-native streaming services
Day 5
• Monitoring and observability practices for streaming systems
• Fundamentals of security and access control
• Performance tuning and optimization strategies
• Comprehensive review of end-to-end pipeline design
• Applied real-world scenarios, including fraud detection and IoT data processing
Testimonials (2)
A journey through the Spark world: a very intense course. DSL, spark sql, partitioning vs bucketing for me.
Georgiana Elisabeta
Course - Apache Spark Fundamentals
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
