piperider 0.20.1

PiperRider CLI

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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

1. 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]'

2. Initialize PipeRider: Go to your dbt project, and initialize PipeRider.

   piperider init
   

3. Run PipeRider: Collect profiling statistics by using

   dbt build
   piperider run
   

4. 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
   

5. 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, 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)

Run Report

Comparison Report

Comparison Summary in a PR

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: