Managing Spark Jobs in a Pipeline
Before delving into the details of managing Spark jobs within ADF or Azure Synapse pipelines, it is essential to know the significance of leveraging Spark for Big Data processing tasks. Apache Spark is a powerful open-source distributed computing framework that provides high-performance processing capabilities for large-scale Data Analytics and machine learning workloads. By incorporating Spark into ADF or Synapse pipelines, you can use its parallel processing capabilities to efficiently process vast amounts of data.
Managing Spark jobs in a pipeline involves the following two aspects:
- Managing the attributes of the pipeline’s runtime that launches the Spark activity: Managing the Spark activity pipeline attributes is no different than managing any other activities in a pipeline. The managing and monitoring screens you saw in Figure 7.7, Figure 7.8, Figure 7.9, Figure 7.10, Figure 7.11, and Figure 7.12 are the same for any Spark activity...
