Our latest engineering blog introduces LakeChime, a service that simplifies data event handling in modern data lakes. LakeChime allows our team to efficiently process only the necessary data changes, reducing resource usage and improving processing times. Learn more! https://lnkd.in/gKWt-N_g
LinkedIn has a massive plan to adopt the #Iceberg table format. Today we are revealing one piece of the puzzle: LakeChime, a data trigger service that is compatible with Hive and modern table formats such as Iceberg. LakeChime supports two main data trigger semantics: partition triggers and snapshot triggers, uniformly across Hive and Iceberg. We show how we integrate LakeChime within our data lake, and present a case study for integrating LakeChime with #Airflow to deliver an end-to-end user experience for incremental compute with #Spark. Enjoy reading the blog post! With Sandeep Dhillon, Nagarathnam muthusamy, Limian (Raymond) Zhang, Zhe Liu, Zhuo Wang, Trevor DeVore, Swathi Koundinya, Abhishek Tiwari, and Kamal D..