Fionce Siow and Ryan Warrier discuss the challenges of troubleshooting issues in data pipelines using engines like Apache Spark and managed platforms like Databricks or Amazon EMR. The main challenge is that these systems process large volumes of data in parallel, making it difficult to manually correlate relevant information from logs, infrastructure metrics, and job performance to find the root cause of failures. Datadog Data Jobs Monitoring (DJM) helps solve this problem by enabling teams to quickly detect and debug failing or long-running jobs while offering insights into job cost and optimization opportunities. DJM provides a unified view of all Spark and Databricks jobs and clusters across accounts and environments, allowing teams to identify issues with their data processing workloads and dive deeper to troubleshoot without relying on manual processes. It also enables teams to pinpoint and resolve job issues faster, reduce costs by optimizing overprovisioned clusters and inefficient jobs, and get a full view of how their data processing infrastructure is performing.