Data Cleaning with Snowpark

Data cleaning is 80% of our job for a reason: raw data is messy. While SQL is powerful, regex statements can quickly become unreadable nightmares. Python, with its rich ecosystem of string manipulation libraries, has always been better at this.

With Snowpark, we can bring that Pythonic elegance to our Data Cloud pipelines without moving the data.

The Scenario: Messy Address Data#

Imagine a table with a raw string column full_address that mixes street, city, and zip code in random formats.

SQL Approach: Detailed REGEXP_SUBSTR logic that breaks every time a new edge case appears. Snowpark Approach: Use a Python UDF with standard library string parsing or even a specialized library like usaddress (if pulled from Anaconda).

1. Missing Value Imputation#

Imputing missing values in SQL usually involves complex COALESCE or window functions (LAG/LEAD). In Snowpark (via the Pandas API), it’s intuitive.

# Utilizing the Snowpark Pandas API (Standard in 2025)
import modin.pandas as pd
import snowflake.snowpark.modin.plugin

def clean_sales_data(session, table_name):
    df = session.table(table_name).to_pandas() # Returns a Snowpark Pandas DataFrame (Lazy)

    # Forward fill missing dates
    df['transaction_date'] = df['transaction_date'].ffill()

    # Fill numeric nulls with mean
    mean_val = df['amount'].mean()
    df['amount'] = df['amount'].fillna(mean_val)

    return df

python

Note: The Snowpark Pandas API translates these operations into optimized SQL queries under the hood.

2. Advanced String Cleaning#

For logic that cannot be expressed in SQL, use a Vectorized UDF.

from snowflake.snowpark.functions import pandas_udf
from snowflake.snowpark.types import StringType
import pandas as pd

@pandas_udf(name='clean_phone', is_permanent=True, stage_location='@deploy', packages=['pandas'])
def clean_phone(series: pd.Series) -> pd.Series:
    # Logic that uses Python's strong string methods
    return series.str.replace(r'\D+', '', regex=True).apply(lambda x: x[-10:] if x else None)

python

3. Deduplication with Fuzzy Matching#

SQL equality (=) is binary. Python allows for fuzzy matching. While computationally expensive, you can implement Levenshtein distance checks for small-to-medium datasets directly in a Stored Procedure to merge customer records.

Performance Note#

Always prefer native Snowpark functions (functions.col(), functions.trim()) over Python UDFs if the logic exists in SQL. Use Python for what only Python can do (complex logic, loops, libraries).

Conclusion#

Snowpark gives us the best of both worlds: the scale of Snowflake for the heavy lifting and the expressiveness of Python for the intricate cleaning logic.