Wrapper for Great Expectations to fit the requirements of the Gemeente Amsterdam.
Project description
Introduction
This repository contains functions that will ease the use of Great Expectations. Users can input data and data quality rules and get results in return.
DISCLAIMER: The package is in MVP phase
Getting started
Install the dq suite on your compute, for example by running the following code in your workspace:
pip install dq-suite-amsterdam
import dq_suite
Load your data in dataframes, give them a table_name, and create a list of all dataframes:
df = spark.read.csv(csv_path+file_name, header=True, inferSchema=True) #example using csv
df.table_name = "showcase_table"
dfs = [df]
- Define 'dfs' as a list of dataframes that require a dq check
- Define 'dq_rules' as a JSON as shown in dq_rules_example.json in this repo
- Define a name for your dq check, in this case "showcase"
results, brontabel_df, bronattribute_df, dqRegel_df = dq_suite.df_check(dfs, dq_rules, "showcase")
Known exceptions
The functions can run on Databricks using a Personal Compute Cluster or using a Job Cluster. Using a Shared Compute Cluster will results in an error, as it does not have the permissions that Great Expectations requires.
Updates
Version 0.1: Run a DQ check for a dataframe
Version 0.2: Run a DQ check for multiple dataframes
Version 0.3: Refactored I/O
Version 0.4: Added schema validation with Amsterdam Schema per table
Version 0.5: Export schema from Unity Catalog
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