Streaming SQL in Data Mesh

Streaming SQL in Data Mesh

摘要

Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale.

In our last blog post, we introduced “Data Mesh” — A Data Movement and Processing Platform. When a user wants to leverage Data Mesh to move and transform data, they start by creating a new Data Mesh pipeline. The pipeline is composed of individual “Processors” that are connected by Kafka topics. The Processors themselves are implemented as Flink jobs that use the DataStream API.

Since then, we have seen many use cases (including Netflix Graph Search) adopt Data Mesh for stream processing. We were able to onboard many of these use cases by offering some commonly used Processors out of the box, such as Projection, Filtering, Unioning, and Field Renaming.