Diptiman Raichaudhuri

Data engineers around the world have embraced python as the language of choice for designing and developing data engineering pipelines. For streaming data, python DSLs serve the purpose of writing complex business logic in a fluent, readable and efficient way. Apache Flink Table API python transforms enable data streaming engineers to write sophisticated stream transforms such as tumbling, hopping windows and group by key aggregations with pure pythonic fluent DSLs. Join this session to learn the beauty and ease of use of writing python Table API transformations on streaming data with Kafka as the source. This session would also show a live demo of writing python Table API aggregations on streaming data with Kafka. The audience would come out of this session armed with the knowledge of writing complex streaming data transformations using their popular language of choice, python and understand how to construct streaming data pipelines using Apache Flink Table API.

Are you struggling to come to terms with Flink SQL WINDOW functions for processing your stream ? Are you new to Flink SQL ? If answers to both these questions are ‘Yes’, then, join my session on getting introduced to WINDOW functions on a data stream, with live examples. No presentations please ! ! Followed by live coding of Flink SQL WINDOW operations on real world streaming data and get your hands dirty ! ! We will start by understanding the syntax of a WINDOW function in general and then dive deeper into Flink Table Valued Functions (TVF) with Flink 1.20. Then we’ll understand how TUMBLE, HOP WINDOW functions operate using live SQL examples. Next, we will build an end-to-end demo with data streams generated by Kafka and apply Flink SQL WINDOW operations on the data stream to transform and aggregate data. You come out of my session with an enhanced knowledge about data stream WINDOW functions using Flink SQL and will be able to run the example to align it closer to your use case. !

Apache Flink has steadily established itself as the leader in stream processing technologies. With thousands of users implementing simple to advanced streaming use cases, the future of the flink community looks bright.

In this lightning talk, Diptiman would present the techniques used to execute text-2-cypher analytical query generations with the help of modern large language models like GPT-4o, Claude 3 etc .