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Data observability refers to the ability of an organization to monitor, track, and make recommendations about what’s happening inside their data systems in order to maintain system health and reduce downtime. Its objective is to ensure that data pipelines are productive and can continue running with minimal disruption. Good observability can help organizations quickly identify and address common causes of data pipeline breakages, such as a lack of up-to-date data, a sudden drop in data volume, or a change in data schema. Strong observability should also include a clear view of data lineage, helping organizations understand which resources in their data pipeline are impacted when breakages occur. Unfortunately, observability is a challenge for many organizations, since most data pipelines are designed to transmit data, not monitor it.
How Alteryx Empowers Data Observability
Alteryx data engineering cloud platform allows for the creation of data pipelines with built-in observability features, supporting detailed job monitoring throughout each phase of the data preparation process. When breakages do occur, Alteryx provides a forward and backward look into data lineage, allowing users to observe how the current steps in their pipeline were created as well as identifying downstream dependencies.
Each time you run a job with Alteryx, you can monitor and track your data’s cleanliness with automated Data Quality Rules. These Data Quality Rules help ensure the accuracy, completeness, consistency, validity, and uniqueness of the data in your pipeline. When flaws are identified, data transformation and remediation suggestions are provided to help address any issues.