piperider-nightly 0.19.0.20230215a30547

PiperRider CLI

Code review of data in dbt

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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:

1. Easy to connect your datasource -> PipeRider leverages the connection profiles in your dbt project to connect to the data warehouse
2. Generate profiling statistics of your models to get a high-level overview of your data
3. Compare local changes with the main branch in a HTML report
4. 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

Navigate to your dbt folder, and install pipeirder.

pip install piperider

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 initalize PipeRider.

piperider init

3. Run PipeRider

Collect profiling statistics by using

piperider run

4. Run PipeRider in another branch

Go to another branch to compare your local changes, by running

dbt build
piperider run --open

5. 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

piperider compare-reports --last

6. Add a markdown summary

You can add a Markdown summary of the data changes to your Pull Request, so that your reviewer can merge with confidence.

Markdown summaries and reports are stored in .piperider/comparisons/<timestamp>

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

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

Example Report Demo

See Generated Single-Run Report

See Comparison Report

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: