Docs | Discord | Blog

Code review for data in dbt

PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence.

How it works:

Easy to connect your datasource -> PipeRider leverages the connection profiles in your dbt project to connect to the data warehouse
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

Core concepts

Easy to install: Leveraging dbt's configuration settings, PipeRider can be installed within 2 minutes
Fast comparison: by collecting profiling statistics (e.g. uniqueness, averages, quantiles, histogram) and metric queries, comparing downstream data impact takes little time, speeding up your team's review time
Valuable insights: various profiling statistics displayed in the HTML report give fast insights into your data

Quickstart

Install PipeRider
```
pip install piperider[<connector>]
```
You can find all supported data source connectors here.
Add PipeRider tag on your model: Go to your dbt project, and add the PipeRider tag on the model you want to profile.
```
--models/staging/stg_customers.sql
{{ config(
   tags=["piperider"]
) }}

select ...
```
and show the models would be run by piperider
```
 dbt list -s tag:piperider --resource-type model
```
Run PipeRider
```
piperider run
```

To see the full quick start guide, please refer to PipeRider documentation

Features

Model profiling: PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more.
Metric queries: PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report.
HTML report: PipeRider generates a static HTML report each time it runs, which can be viewed locally or shared.
Report comparison: 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.
CI integration: The key to CI is automation, and in the code review process, automating this workflow is even more meaningful. PipeRider can easily integrate into your CI process. When new commits are pushed to your PR branch, reports can be automatically generated to provide reviewers with more confidence in the changes made when reviewing.

Example Report Demo

We use the example project git-repo-analytics to demonstrate how to use piperider+dbt+duckdb to analyze dbt-core repository. Here is the generated result (daily update)

Run Report

Comparison Report

Comparison Summary in a PR

PipeRider Cloud (beta)

PipeRider Cloud allows you to upload reports and share them with your team members. For information on pricing plans, please refer to the pricing page.

PipeRider Compare Action

PipeRider provides the PipeRider Compare Action to quickly integrate into your Github Actions workflow. It has the following features:

Automatically generates a report comparing the PR branch to the main branch
Uploads the report to GitHub artifacts or PipeRider cloud
Adds a comment to the pull request with a comparison summary and a link to the report.

You can refer to example workflow yaml and the example pull request.

Development

See setup dev environment and the contributing guildlines to get started.

We love chatting with our users! Let us know if you have any questions, feedback, or need help trying out PipeRider! ❤️

qrtt1 / piperider-lab