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PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed locally or shared. You can compare two previously generated reports or use a single command to compare the differences between the current branch and the main branch. The latter is designed specifically for code review scenarios. In our pull requests on GitHub, we not only want to know which files have been changed, but also the impact of these changes on the data. PipeRider can easily generate comparison reports with a single command to provide this information.
Features
- Easy to connect your datasource
- Generate profiling statistics of your models to get a high-level overview of your data
- Compare target branch changes with the main branch in a HTML report
- Post a quick summary of the data changes to your PR, so others can be confident too
- Easy to install: Leveraging dbt's configuration settings, PipeRider can be installed within 2 minutes
- Valuable insights: various profiling statistics displayed in the HTML report give fast insights into your data
Project Samples
