Data observability is an integral part of the DataOps process. It helps to reduce errors, the elimination of unplanned work, and the reduction of cycle time. It allows enterprises to see workloads, data sources, and user actions in order to keep operations predictable and cost-effective without limiting their technology choices.
Observability is defined as a holistic approach that involves monitoring, tracking, and triaging incidents to prevent system downtime. It is centered on three central pillars (metrics, logs, and traces), data engineers can refer to five pillars of data observability. These include,
Freshness: Data pipelines can fail for a million different reasons, but one of the most common causes is a lack of freshness. Freshness is the notion of “is my data up to date? Are there gaps in time where my data has not been updated?
Distribution: What is the quality of my data at the field level? Is my data within expected ranges?
Volume: The amount of data in a database is one of the most critical measurements for whether your data intake meets expected thresholds.
Schema: Fields are often added, removed, changed etc. So having a solid audit of your schema is an excellent way to think about the health of your data as part of this Data Observability framework.
Lineage: Lineage gives the full picture of your data landscape, including upstream sources, downstream, and who interacts with your data at which stages.
Observability is a new practice and critical competency in an ever-changing big data world. DQLabs.ai i uses AI to use for various use cases around DataOps / Data Observability.