Serious about rETL? You need data observability too | Census

The modern data platform and its use cases are complex, leading to broken dashboards and "data downtime" - a period of time when data is partial, erroneous, missing, or inaccurate. Data observability, an organization's ability to understand the health of its data across the entire system, can reduce data downtime by providing automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues. By connecting to existing stacks without modifying pipelines or writing new code, data observability solutions monitor data at rest with minimal configuration and reduce time to detection and resolution. Data health analytics also play a crucial role in measuring performance, setting SLAs, and optimizing resources. However, data observability is different from testing or monitoring, as it provides end-to-end coverage, scalability, and lineage for impact analysis. When combined with reverse ETL, data observability can make data more actionable, reducing the likelihood of severe events like data downtime and fostering a culture of innovation and data-driven excellence.

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