Composable Data Processing with Apache Spark - Scaling Development and Error Handling

Explore a 28-minute conference talk on composable data processing with Apache Spark, presented by Databricks. Learn about the challenges of scaling Spark development and the consequences of isolated Spark apps. Discover SIP, an extensible plugin framework used in Adobe's Experience Platform for data processing, which addresses issues of resiliency, scalability, monitoring, and error handling. Dive deep into SIP's detailed error reporting and its improved user experience. Gain insights into parsing errors, conversions, and implementation challenges in the Adobe Data Platform context.

Intro
Adobe Data Platform
Implementation Challenges
Parsing Errors
Conversions