cuallee

Meaning good in Aztec (Nahuatl), pronounced: QUAL-E

This library provides an intuitive API to describe data quality checks initially just for PySpark dataframes v3.3.0. And extended to pandas, snowpark, duckdb, daft and more. It is a replacement written in pure python of the pydeequ framework.

I gave up in deequ as after extensive use, the API is not user-friendly, the Python Callback servers produce additional costs in our compute clusters, and the lack of support to the newest version of PySpark.

As result cuallee was born

This implementation goes in hand with the latest API from PySpark and uses the Observation API to collect metrics at the lower cost of computation. When benchmarking against pydeequ, cuallee uses circa <3k java classes underneath and remarkably less memory.

Support

cuallee is the data quality framework truly dataframe agnostic.

Provider	API	Versions
	`snowpark`	`1.11.1`, `1.4.0`
	`pyspark` & `spark-connect`	`3.5.x`, `3.4.0`, `3.3.x`, `3.2.x`
	`bigquery`	`3.4.1`
	`pandas`	`2.0.2`, `1.5.x`, `1.4.x`
	`duckdb`	`1.0.0`, ~~`0.10.2`~~,~~`0.9.2`~~,~~`0.8.0`~~
	`polars`	`1.0.0`, ~~`0.19.6`~~
	`daft`	`0.2.24`, ~~`0.2.19`~~

_{Logos are trademarks of their own brands.}

Install

pip install cuallee

Checks

The most common checks for data integrity validations are completeness and uniqueness an example of this dimensions shown below:

from cuallee import Check, CheckLevel # WARN:0, ERR: 1

# Nulls on column Id
check = Check(CheckLevel.WARNING, "Completeness")
(
    check
    .is_complete("id")
    .is_unique("id")
    .validate(df)
).show() # Returns a pyspark.sql.DataFrame

Important

A new version of the validate output is currently under construction.

Dates

Perhaps one of the most useful features of cuallee is its extensive number of checks for Date and Timestamp values. Including, validation of ranges, set operations like inclusion, or even a verification that confirms continuity on dates using the is_daily check function.

# Unique values on id
check = Check(CheckLevel.WARNING, "CheckIsBetweenDates")
df = spark.sql(
    """
    SELECT
        explode(
            sequence(
                to_date('2022-01-01'),
                to_date('2022-01-10'),
                interval 1 day)) as date
    """)
assert (
    check.is_between("date", ("2022-01-01", "2022-01-10"))
    .validate(df)
    .first()
    .status == "PASS"
)

Membership

Other common test is the validation of list of values as part of the multiple integrity checks required for better quality data.

df = spark.createDataFrame([[1, 10], [2, 15], [3, 17]], ["ID", "value"])
check = Check(CheckLevel.WARNING, "is_contained_in_number_test")
check.is_contained_in("value", (10, 15, 20, 25)).validate(df)

Regular Expressions

When it comes to the flexibility of matching, regular expressions are always to the rescue. cuallee makes use of the regular expressions to validate that fields of type String conform to specific patterns.

df = spark.createDataFrame([[1, "is_blue"], [2, "has_hat"], [3, "is_smart"]], ["ID", "desc"])
check = Check(CheckLevel.WARNING, "has_pattern_test")
check.has_pattern("desc", r"^is.*t$") # only match is_smart 33% of rows.
check.validate(df).first().status == "FAIL"

Anomalies

Statistical tests are a great aid for verifying anomalies on data. Here an example that shows that will PASS only when 40% of data is inside the interquartile range

df = spark.range(10)
check = Check(CheckLevel.WARNING, "IQR_Test")
check.is_inside_interquartile_range("id", pct=0.4)
check.validate(df).first().status == "PASS"

+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+
|id |timestamp          |check|level  |column|rule                         |value|rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+
|1  |2022-10-19 00:09:39|IQR  |WARNING|id    |is_inside_interquartile_range|10000|10  |4         |0.6      |0.4           |PASS  |
+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+

Workflows (Process Mining)

Besides the common citizen-like checks, cuallee offers out-of-the-box real-life checks. For example, suppose that you are working SalesForce or SAP environment. Very likely your business processes will be driven by a lifecycle:

Order-To-Cash
Request-To-Pay
Inventory-Logistics-Delivery
Others. In this scenario, cuallee offers the ability that the sequence of events registered over time, are according to a sequence of events, like the example below:

import pyspark.sql.functions as F
from cuallee import Check, CheckLevel

data = pd.DataFrame({
   "name":["herminio", "herminio", "virginie", "virginie"],
   "event":["new","active", "new", "active"],
   "date": ["2022-01-01", "2022-01-02", "2022-01-03", "2022-02-04"]}
   )
df = spark.createDataFrame(data).withColumn("date", F.to_date("date"))

# Cuallee Process Mining
# Testing that all edges on workflows
check = Check(CheckLevel.WARNING, "WorkflowViolations")

# Validate that 50% of data goes from new => active
check.has_workflow("name", "event", "date", [("new", "active")], pct=0.5)
check.validate(df).show(truncate=False)

+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+
|id |timestamp          |check             |level  |column                   |rule        |value               |rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+
|1  |2022-11-07 23:08:50|WorkflowViolations|WARNING|('name', 'event', 'date')|has_workflow|(('new', 'active'),)|4   |2.0       |0.5      |0.5           |PASS  |
+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+

Assertions

[2024-09-28] ✨ New feature! Return a simple true|false as a unified result for your check

import pandas as pd
from cuallee import Check
df = pd.DataFrame({"X":[1,2,3]})
# .ok(dataframe) method of a check will call validate and then verify that all rules are PASS
assert Check().is_complete("X").ok(df)

Controls

Simplify the entire validation of a dataframe in a particular dimension.

import pandas as pd
from cuallee import Control
df = pd.DataFrame({"X":[1,2,3], "Y": [10,20,30]})
# Checks all columns in dataframe for using is_complete check
Control.completeness(df)

`cuallee` VS `pydeequ`

In the test folder there are docker containers with the requirements to match the tests. Also a perftest.py available at the root folder for interests.

# 1000 rules / # of seconds

cuallee: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 162.00
pydeequ: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 322.00

Catalogue

Check	Description	DataType
`is_complete`	Zero `nulls`	agnostic
`is_unique`	Zero `duplicates`	agnostic
`is_primary_key`	Zero `duplicates`	agnostic
`are_complete`	Zero `nulls` on group of columns	agnostic
`are_unique`	Composite primary key check	agnostic
`is_composite_key`	Zero duplicates on multiple columns	agnostic
`is_greater_than`	`col > x`	numeric
`is_positive`	`col > 0`	numeric
`is_negative`	`col < 0`	numeric
`is_greater_or_equal_than`	`col >= x`	numeric
`is_less_than`	`col < x`	numeric
`is_less_or_equal_than`	`col <= x`	numeric
`is_equal_than`	`col == x`	numeric
`is_contained_in`	`col in [a, b, c, ...]`	agnostic
`is_in`	Alias of `is_contained_in`	agnostic
`not_contained_in`	`col not in [a, b, c, ...]`	agnostic
`not_in`	Alias of `not_contained_in`	agnostic
`is_between`	`a <= col <= b`	numeric, date
`has_pattern`	Matching a pattern defined as a `regex`	string
`is_legit`	String not null & not empty `^\S$`	string
`has_min`	`min(col) == x`	numeric
`has_max`	`max(col) == x`	numeric
`has_std`	`σ(col) == x`	numeric
`has_mean`	`μ(col) == x`	numeric
`has_sum`	`Σ(col) == x`	numeric
`has_percentile`	`%(col) == x`	numeric
`has_cardinality`	`count(distinct(col)) == x`	agnostic
`has_infogain`	`count(distinct(col)) > 1`	agnostic
`has_max_by`	A utilitary predicate for `max(col_a) == x for max(col_b)`	agnostic
`has_min_by`	A utilitary predicate for `min(col_a) == x for min(col_b)`	agnostic
`has_correlation`	Finds correlation between `0..1` on `corr(col_a, col_b)`	numeric
`has_entropy`	Calculates the entropy of a column `entropy(col) == x` for classification problems	numeric
`is_inside_interquartile_range`	Verifies column values reside inside limits of interquartile range `Q1 <= col <= Q3` used on anomalies.	numeric
`is_in_millions`	`col >= 1e6`	numeric
`is_in_billions`	`col >= 1e9`	numeric
`is_t_minus_1`	For date fields confirms 1 day ago `t-1`	date
`is_t_minus_2`	For date fields confirms 2 days ago `t-2`	date
`is_t_minus_3`	For date fields confirms 3 days ago `t-3`	date
`is_t_minus_n`	For date fields confirms n days ago `t-n`	date
`is_today`	For date fields confirms day is current date `t-0`	date
`is_yesterday`	For date fields confirms 1 day ago `t-1`	date
`is_on_weekday`	For date fields confirms day is between `Mon-Fri`	date
`is_on_weekend`	For date fields confirms day is between `Sat-Sun`	date
`is_on_monday`	For date fields confirms day is `Mon`	date
`is_on_tuesday`	For date fields confirms day is `Tue`	date
`is_on_wednesday`	For date fields confirms day is `Wed`	date
`is_on_thursday`	For date fields confirms day is `Thu`	date
`is_on_friday`	For date fields confirms day is `Fri`	date
`is_on_saturday`	For date fields confirms day is `Sat`	date
`is_on_sunday`	For date fields confirms day is `Sun`	date
`is_on_schedule`	For date fields confirms time windows i.e. `9:00 - 17:00`	timestamp
`is_daily`	Can verify daily continuity on date fields by default. `[2,3,4,5,6]` which represents `Mon-Fri` in PySpark. However new schedules can be used for custom date continuity	date
`has_workflow`	Adjacency matrix validation on `3-column` graph, based on `group`, `event`, `order` columns.	agnostic
`is_custom`	User-defined custom `function` applied to dataframe for row-based validation.	agnostic
`satisfies`	An open `SQL expression` builder to construct custom checks	agnostic
`validate`	The ultimate transformation of a check with a `dataframe` input for validation	agnostic

Controls `pyspark`

Check	Description	DataType
`completeness`	Zero `nulls`	agnostic
`information`	Zero nulls `and` cardinality > 1	agnostic
`intelligence`	Zero nulls, zero empty strings and cardinality > 1	agnostic
`percentage_fill`	`% rows` not empty	agnostic
`percentage_empty`	`% rows` empty	agnostic

ISO Standard

A new module has been incorporated in cuallee==0.4.0 which allows the verification of International Standard Organization columns in data frames. Simply access the check.iso interface to add the set of checks as shown below.

Check	Description	DataType
`iso_4217`	currency compliant `ccy`	string
`iso_3166`	country compliant `country`	string

df = spark.createDataFrame([[1, "USD"], [2, "MXN"], [3, "CAD"], [4, "EUR"], [5, "CHF"]], ["id", "ccy"])
check = Check(CheckLevel.WARNING, "ISO Compliant")
check.iso.iso_4217("ccy")
check.validate(df).show()
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+
| id|          timestamp|        check|  level|column|           rule|               value|rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+
|  1|2023-05-14 18:28:02|ISO Compliant|WARNING|   ccy|is_contained_in|{'BHD', 'CRC', 'M...|   5|       0.0|      1.0|           1.0|  PASS|
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+

Snowflake Connection

In order to establish a connection to your SnowFlake account cuallee relies in the following environment variables to be avaialble in your environment:

SF_ACCOUNT
SF_USER
SF_PASSWORD
SF_ROLE
SF_WAREHOUSE
SF_DATABASE
SF_SCHEMA

Spark Connect

Just add the environment variable SPARK_REMOTE to your remote session, then cuallee will connect using

spark_connect = SparkSession.builder.remote(os.getenv("SPARK_REMOTE")).getOrCreate()

and convert all checks to select as opposed to Observation API compute instructions.

Databricks Connection

By default cuallee will search for a SparkSession available in the globals so there is literally no need to ~~SparkSession.builder~~. When working in a local environment it will automatically search for an available session, or start one.

DuckDB

For testing on duckdb simply pass your table name to your check et voilà

import duckdb
conn = duckdb.connect(":memory:")
check = Check(CheckLevel.WARNING, "DuckDB", table_name="temp/taxi/*.parquet")
check.is_complete("VendorID")
check.is_complete("tpep_pickup_datetime")
check.validate(conn)

   id            timestamp check    level                column         rule value      rows  violations  pass_rate  pass_threshold status
0   1  2022-10-31 23:15:06  test  WARNING              VendorID  is_complete   N/A  19817583         0.0        1.0             1.0   PASS
1   2  2022-10-31 23:15:06  test  WARNING  tpep_pickup_datetime  is_complete   N/A  19817583         0.0        1.0             1.0   PASS

Roadmap

100% data frame agnostic implementation of data quality checks. Define once, run everywhere

~~[x] PySpark 3.5.0~~
~~[x] PySpark 3.4.0~~
~~[x] PySpark 3.3.0~~
~~[x] PySpark 3.2.x~~
~~[x] Snowpark DataFrame~~
~~[x] Pandas DataFrame~~
~~[x] DuckDB Tables~~
~~[x] BigQuery Client~~
~~[x] Polars DataFrame~~
~~[*] Dagster Integration~~
~~[x] Spark Connect~~
~~[x] Daft~~
[-] PDF Report
Metadata check
Help us in a discussion?

Whilst expanding the functionality feels a bit as an overkill because you most likely can connect spark via its drivers to whatever DBMS of your choice. In the desire to make it even more user-friendly we are aiming to make cuallee portable to all the providers above.

Authors

canimus / Herminio Vazquez / 🇲🇽
vestalisvirginis / Virginie Grosboillot / 🇫🇷

Contributors

Guidelines

Contributing to cuallee

Documentation

https://canimus.github.io/cuallee/

Paper

cuallee has been published in the Journal of Open Source Software

Vazquez et al., (2024). cuallee: A Python package for data quality checks across multiple DataFrame APIs. Journal of Open Source Software, 9(98), 6684, https://doi.org/10.21105/joss.06684

If you use cuallee please consider citing this work. Citation

License

Apache License 2.0 Free for commercial use, modification, distribution, patent use, private use. Just preserve the copyright and license.

Made with ❤️ in Utrecht 🇳🇱
Maintained over ⌛ from Ljubljana 🇸🇮
Extended 🚀 by contributions all over the 🌎

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README.md

README.md

cuallee

Support

Install

Checks

Dates

Membership

Regular Expressions

Anomalies

Workflows (Process Mining)

Assertions

Controls

`cuallee` VS `pydeequ`

Catalogue

Controls `pyspark`

ISO Standard

Snowflake Connection

Spark Connect

Databricks Connection

DuckDB

Roadmap

Authors

Contributors

Guidelines

Documentation

Paper

License

Files

README.md

Latest commit

History

README.md

File metadata and controls

cuallee

Support

Install

Checks

Dates

Membership

Regular Expressions

Anomalies

Workflows (Process Mining)

Assertions

Controls

cuallee VS pydeequ

Catalogue

Controls pyspark

ISO Standard

Snowflake Connection

Spark Connect

Databricks Connection

DuckDB

Roadmap

Authors

Contributors

Guidelines

Documentation

Paper

License

`cuallee` VS `pydeequ`

Controls `pyspark`