Metadata-Version: 2.1
Name: piperider-nightly
Version: 0.21.0.20230307
Summary: PiperRider CLI
Home-page: https://github.com/InfuseAI/piperider
Author: InfuseAI Dev Team
Author-email: dev@infuseai.io
Project-URL: Bug Tracker, https://github.com/InfuseAI/piperider/issues
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: snowflake
Provides-Extra: postgres
Provides-Extra: bigquery
Provides-Extra: redshift
Provides-Extra: duckdb
Provides-Extra: csv
Provides-Extra: parquet
Provides-Extra: dev
License-File: LICENSE

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<p align="left">
  <a href="https://docs.piperider.io/" alt="documentation site" title="Piperider Documentation"> Docs </a> |
  <a href="https://github.com/orgs/InfuseAI/projects/1/views/1" alt="product roadmap" title="Planned Features/Changes"> Roadmap </a> |
  <a href="https://discord.com/invite/5zb2aK9KBV"> Discord </a> |
  <a href="https://blog.infuseai.io/data-reliability-automated-with-piperider-7a823521ef11"> Blog </a> 
</p>




# 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](https://docs.getdbt.com/docs/get-started/connection-profiles) 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

1. **Install PipeRider**

    ```bash
    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]'`    |


1. **Initialize PipeRider**: Go to your dbt project, and initialize PipeRider.
    
    ```bash
    piperider init
    ```

1. **Run PipeRider**: Collect profiling statistics by using 

    ```
    dbt build
    piperider run
    ```

1. **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

   ```bash
   git switch feature/pr-branch
   dbt build
   piperider run
   piperider compare-reports --last
   ```

1. **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 run` to view the profiling statistics of a single data source, even in an environment that doesn't use dbt
* Leverage dbt-defined `metrics` to 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](https://docs.getdbt.com/reference/node-selection/syntax), [dbt test result](https://docs.getdbt.com/docs/build/tests) 


# Example Report Demo

We use the example project [git-repo-analytics](https://github.com/InfuseAI/git-repo-analytics) to demonstrate how to use piperider+dbt+duckdb to analyze [dbt-core](https://github.com/dbt-labs/dbt-core) repository. Here is the generated result (daily update)

[Run Report](https://piperider-github-readme.s3.ap-northeast-1.amazonaws.com/single-run-report/index.html)

[Comparison Report](https://piperider-github-readme.s3.ap-northeast-1.amazonaws.com/comparison-report/index.html)

[Comparison Summary in a PR](https://github.com/InfuseAI/git-repo-analytics/pull/19)

# 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](DEVELOP.md) and the [contributing guildlines](CONTRIBUTING.md) to get started.

**We love chatting with our users! [Let us know](mailto:product@infuseai.io) if you have any questions, feedback, or need help trying out PipeRider! :heart:**
