Hi @IM_01 ,

Yes, you can store your rules in a delta table. For the sake of example this table is filled with static rules, but of course you can insert to that table in "dynamic" way.

CREATE OR REPLACE TABLE
  rules
AS SELECT
  col1 AS name,
  col2 AS constraint,
  col3 AS tag
FROM (
  VALUES
  ("website_not_null","Website IS NOT NULL","validity"),
  ("fresh_data","to_date(updateTime,'M/d/yyyy h:m:s a') > '2010-01-01'","maintained"),
  ("social_media_access","NOT(Facebook IS NULL AND Twitter IS NULL AND Youtube IS NULL)","maintained")
)

 Then you can read those rules in following way:

from pyspark import pipelines as dp
from pyspark.sql.functions import expr, col

def get_rules(tag):
  """
    loads data quality rules from a table
    :param tag: tag to match
    :return: dictionary of rules that matched the tag
  """
  df = spark.read.table("rules").filter(col("tag") == tag).collect()
  return {
      row['name']: row['constraint']
      for row in df
  }

@dp.table
@dp.expect_all_or_drop(get_rules('validity'))
def raw_farmers_market():
  return (
    spark.read.format('csv').option("header", "true")
      .load('/databricks-datasets/data.gov/farmers_markets_geographic_data/data-001/')
  )

@dp.table
@dp.expect_all_or_drop(get_rules('maintained'))
def organic_farmers_market():
  return (
    spark.read.table("raw_farmers_market")
      .filter(expr("Organic = 'Y'"))
  )

Check following page for more details:

Expectation recommendations and advanced patterns | Databricks on AWS