Managing complex data ecosystems with numerous sources and constant updates is challenging for data engineering teams. They often face unpredictable but common issues like cloud vendor outages, broken connections to data sources, late-arriving data, or even data quality issues at the source. Other times, they have to deal with sudden business rule changes that impact the entire data orchestration.
The result? Downstream data is stale, inaccurate, or incomplete. While backfilling - rerunning jobs with historical data - is a common need and solution to this, traditional manual and ad hoc backfills are tedious, error-prone, and don't scale, hindering efficient resolution of common data quality issues.
In short, backfill runs in Lakeflow Jobs helps you:
- Ensure that you have the most complete and up-to-date datasets
- Simplify and accelerate access to historical data with an intuitive, no-code interface
- Improve data engineering productivity by eliminating the need for manual data searches and backfill processes
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