data-linter 1.1.1

data linter

Data Linter

A python package to to allow automatic validation of data as part of a Data Engineering pipeline. It is designed to automate the process of moving data from Land to Raw-History as described in the ETL pipline guide

The validation is based on the goodtables package, from the fine folk at Frictionless Data. More information can be found at their website.

Installation

pip install data_linter

Usage

This package takes a yaml based config file written by the user (see example below), and validates data in the specified Land bucket against specified metadata. If the data conforms to the metadata, it is moved to the specified Raw bucket for the next step in the pipeline. Any failed checks are passed to a separate bucket for testing. The package also generates logs to allow you to explore issues in more detail.

To run the validation, at most simple you can use the following:

from data_linter import run_validation

config_path = "config.yaml"

run_validation(config_path)

Example config file

land-base-path: s3://land-bucket/my-folder/  # Where to get the data from
fail-base-path: s3://fail-bucket/my-folder/  # Where to write the data if failed
pass-base-path: s3://pass-bucket/my-folder/  # Where to write the data if passed
log-base-path: s3://log-bucket/my-folder/  # Where to write logs
compress-data: true  # Compress data when moving elsewhere
remove-tables-on-pass: true  # Delete the tables in land if validation passes
all-must-pass: true  # Only move data if all tables have passed
fail-unknown-files:
    exceptions:
        - additional_file.txt
        - another_additional_file.txt

# Tables to validate
tables:
    table1:
        required: true  # Does the table have to exist
        pattern: null  # Assumes file is called table1
        metadata: meta_data/table1.json
        linter: goodtables

    table2:
        required: true
        pattern: ^table2
        metadata: meta_data/table2.json

How to update

We have tests that run on the current state of the poetry.lock file (i.e. the current dependencies). We also run tests based on the most up to date dependencies allowed in pyproject.toml. This allows us to see if there will be any issues when updating dependences. These can be run locally in the tests folder.

When updating this package, make sure to change the version number in pyproject.toml and describe the change in CHANGELOG.md.

If you have changed any dependencies in pyproject.toml, run poetry update to update poetry.lock.

Once you have created a release in GitHub, to publish the latest version to PyPI, run:

poetry build
poetry publish -u <username>

Here, you should substitute for your PyPI username. In order to publish to PyPI, you must be an owner of the project.

Process Diagram

How logic works