Ken Krugler

Enrichment Techniques: - Review of options for enrichment - joins, async I/O, broadcast, parallel partial Scalability: - Advanced techniques for feature engineering on streaming data with Flink - Techniques for handling data skew efficiently Hands-on Lab, Apply Enrichment Techniques: - Add enrichments to the eCommerce workflow - Optimize using scalability techniques

Exactly Once: - Deep Dive into how Flink provides exactly-once support - Differences between exactly once and exactly once end-to-end Failure Handling: - How Flink recovers when a failure occurs - Techniques for optimizing recovery - How to test for exactly once Hands-on Lab, Add Exactly-once Support: - Switch the eCommerce workflow into exactly-once mode - Write tests to validate exactly-once...

Flink SQL Deep Dive: - Advanced functionalities of Flink SQL for stream processing (UDFs, Table Functions) - Windowing and state management in Flink SQL queries Flink Table API Mastery: - Advanced features of the Flink Table API for a unified approach - Interoperability between Flink SQL and Table API for complex data transformations Hands-on Lab, Implement eCommerce workflow using Flink SQL -...

State Management & Fine Tuning: - Advanced state management techniques (State Partitioning, RocksDB integration) - Optimizing state size and access patterns for performance Serialization Optimization: - Techniques for reducing overhead for serialization Hands-on Lab, Optimizing State and Serialization: - Improve the performance of the eCommerce workflow by optimizing RocksDB configuration and i...

Deep Dive into Time: - Event Time, Processing Time, Ingestion Time Deep Dive into Windowing: - Advanced Windowing Operations (Session Windows, Tumbling/Sliding Window with Triggers) Hands-on Lab, Building a Complex Flink Workflow: - Create a workflow to generate analytics results from real-time eCommerce data

Segmenting workflows: - When and how to split up workflows Flink & Paimon: - Using Paimon as a data source and data sink - Using Paimon as a bridge between workflows Hands-on Lab, Add additional analytics workflow to eCommerce pipeline, using Paimon as the bridge.

- Course Recap & Q&A - Resources for further exploration of advanced Apache Flink topics - Introduction to Ververica's Developer Community and Advanced Support Options

Enrichment Techniques: - Review of options for enrichment - joins, async I/O, broadcast, parallel partial Scalability: - Advanced techniques for feature engineering on streaming data with Flink - Techniques for handling data skew efficiently Hands-on Lab, Apply Enrichment Techniques: - Add enrichments to the eCommerce workflow - Optimize using scalability techniques

Exactly Once: - Deep Dive into how Flink provides exactly-once support - Differences between exactly once and exactly once end-to-end Failure Handling: - How Flink recovers when a failure occurs - Techniques for optimizing recovery - How to test for exactly once Hands-on Lab, Add Exactly-once Support: - Switch the eCommerce workflow into exactly-once mode - Write tests to validate exactly-once...

Flink SQL Deep Dive: - Advanced functionalities of Flink SQL for stream processing (UDFs, Table Functions) - Windowing and state management in Flink SQL queries Flink Table API Mastery: - Advanced features of the Flink Table API for a unified approach - Interoperability between Flink SQL and Table API for complex data transformations Hands-on Lab, Implement eCommerce workflow using Flink SQL -...

State Management & Fine Tuning: - Advanced state management techniques (State Partitioning, RocksDB integration) - Optimizing state size and access patterns for performance Serialization Optimization: - Techniques for reducing overhead for serialization Hands-on Lab, Optimizing State and Serialization: - Improve the performance of the eCommerce workflow by optimizing RocksDB configuration and i...

Deep Dive into Time: - Event Time, Processing Time, Ingestion Time Deep Dive into Windowing: - Advanced Windowing Operations (Session Windows, Tumbling/Sliding Window with Triggers) Hands-on Lab, Building a Complex Flink Workflow: - Create a workflow to generate analytics results from real-time eCommerce data

Segmenting workflows: - When and how to split up workflows Flink & Paimon: - Using Paimon as a data source and data sink - Using Paimon as a bridge between workflows Hands-on Lab, Add additional analytics workflow to eCommerce pipeline, using Paimon as the bridge.

- Course Recap & Q&A - Resources for further exploration of advanced Apache Flink topics - Introduction to Ververica's Developer Community and Advanced Support Options