
Prefect
Automation
Give Python workflows scheduling and a run history
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Full sizeDagster organizes data work around assets such as tables, files and reports. Define what produces each asset and how it depends on the others, then use the interface to see what ran and where an upstream change matters. A data team can use this view to understand why a report is stale before rerunning the whole pipeline. The open source project also has a hosted commercial offering. Its asset approach asks teams to name and describe the data they want to maintain, which can take planning when migrating existing scripts. Try a small set of related tables first and check how runs, schedules and data quality checks map onto the way your team already investigates problems.
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