Observing the data lifecycle with Datadog

Nicholas Thomson, Jonathan Morin, Ryan Warrier, and Jane Wang discuss the challenges of monitoring data pipelines and the importance of gaining complete visibility into data's health at every stage of the process. They argue that traditional methods of monitoring only check on data quality as a proxy for pipeline efficiency, leaving room for improvement. Datadog's suite of solutions, including Data Streams Monitoring (DSM) and Data Jobs Monitoring (DJM), enables teams to monitor the entire data lifecycle from end to end, providing insights into both pipeline performance and data itself. With DSM, users can track streaming data pipelines, application dependencies, and storage components in a unified map, gaining visibility across services, queues, and jobs. DJM provides alerting, troubleshooting, and optimization capabilities for Apache Spark and Databricks jobs, allowing teams to optimize data job provisioning, configuration, and deployment. Datadog also offers new capabilities to monitor the data itself, including detecting and alerting on freshness and volume issues, analyzing table usage based on query history, and understanding upstream and downstream dependencies with table-level lineage. By integrating these solutions, teams can gain end-to-end visibility into their data pipelines, troubleshoot issues more effectively, and save hours of cross-team troubleshooting.

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