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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>]
PipeRider supports the following data connectors
connectors install snowflake pip install 'piperider[snowflake]'postgres pip install 'piperider[postgres]'bigquery pip install 'piperider[bigquery]'redshift pip install 'piperider[redshift]'parquet pip install 'piperider[parquet]'csv pip install 'piperider[csv]'duckdb pip install 'piperider[duckdb]' -
Initialize PipeRider: Go to your dbt project, and initialize PipeRider.
piperider init -
Run PipeRider: Collect profiling statistics by using
dbt build piperider run -
Compare your changes: You then can compare the branch of your new Pull Request against the main branch and explore the impact of your changes by opening the generated HTML comparison report
git switch feature/pr-branch dbt build piperider run piperider compare-reports --last
-
Post the markdown summary on the PR: You can post the markdown summary of the data changes to your Pull Request comment, so that your reviewer can merge with confidence.
Features
- Use PipeRider for exploratory data analysis by doing
piperider runto view the profiling statistics of a single data source, even in an environment that doesn't use dbt - Leverage dbt-defined
metricsto have a quick overview of the impact on your most important metrics - Include PipeRider into your CI process via PipeRider Cloud or self-hosted to be confident of every PRs that is submitted
- Benefit from dbt's features such as Slim CI, custom schema, custom database, node selection, dbt test result
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)
PipeRider Cloud (beta)
PipeRider Cloud offers a hosted version for HTML reports, including features such as alerts and historical trend watching. Get early beta access by signing up on our website: https://piperider.io
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! :heart:
1 maintainer
Metadata
1 maintainer
Release files for piperider-nightly 0.22.0.20230329
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| piperider-nightly-0.22.0.20230329.tar.gz | 3.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| piperider_nightly-0.22.0.20230329-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.8 MB
Release files / piperider-nightly-0.22.0.20230329.tar.gz
| Download URL | piperider-nightly-0.22.0.20230329.tar.gz |
|---|---|
| Size | 3.8 MB |
| Tags | Source |
|
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.16
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Release files / piperider_nightly-0.22.0.20230329-py3-none-any.whl
| Download URL | piperider_nightly-0.22.0.20230329-py3-none-any.whl |
|---|---|
| Size | 3.9 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.16
|
