📄️ JAR Jobs
A JAR job is best suited for production-grade workloads that require compiled code, well-structured Spark logic, and reusable applications. It enables efficient execution of complex data processing tasks using pre-built artifacts.
📄️ Python
Python jobs provide flexibility for data transformations, analytics, automation, machine learning, and ETL pipelines.
📄️ Scala
These jobs enable rapid prototyping, quick transformations, testing logic, and performing Spark operations without compiling a JAR.
📄️ SQL
SQL jobs are designed for structured data processing, analytical queries, and scheduled transformations.
📄️ Thrift_SQL
Utilizes Apache Thrift to execute SQL commands on a database or distributed data system.
📄️ Yeedu Functions
Yeedu Functions allow users to run jobs through REST APIs, enabling event-driven, scalable, and fully managed execution.