So You Want to Write a User-Defined Function (UDF) for Flink?

Apache Flink gained a lot of momentum during the past couple of years and is well-known across the data industry for its versatility. Developers and data engineers alike preferably pick higher-level programming abstractions such as Flink's Table API and Flink SQL whenever reasonably possible. And yet, specific requirements might cause the need for writing custom code and integrating with 3rd party libraries - Flink UDFs to the rescue!

  This lightning talk shares a "first-timer's experience" with implementing a non-trivial scalar function for encrypting and decrypting data directly within Apache Flink jobs. We are looking at selected challenges and some of the "pain points" that came along with the respective Java implementation.

  Join this session if you need to add custom processing logic to Apache Flink and plan to do so by writing your own user-defined functions. You'll walk away with a few practical and mostly generically applicable hints that should help mitigate some of the friction you might face.