Snowpark Python: Best Practices for 2025

Snowpark Python has been Generally Available for a while now, and in 2025, it’s the de-facto standard for data engineering on Snowflake. We’ve moved past “how to write a stored procedure” to “how to write efficient enterprise-grade pipelines.”

Writing Python that runs inside Snowflake’s secure sandbox requires a different mindset than writing Python for your laptop. Here are the best practices defining high-performance Snowpark in 2025.

1. Vectorized UDFs are Mandatory#

If you are writing a standard User-Defined Function (UDF) that processes one row at a time, you are leaving massive performance on the table. The overhead of serializing/deserializing data between the SQL engine and the Python runtime for every row is a killer.

2025 Standard: Use Vectorized UDFs (batch API).

# The Old Way (Slow)
from snowflake.snowpark.functions import udf

@udf(name="add_one", is_permanent=True, stage_location="@my_stage")
def add_one(x: int) -> int:
    return x + 1

# The 2025 Way (Fast)
import pandas as pd
from snowflake.snowpark.functions import pandas_udf

@pandas_udf(name="add_one_vec", is_permanent=True, stage_location="@my_stage")
def add_one_vec(df: pd.Series) -> pd.Series:
    return df + 1

Vectorization allows Snowflake to send batches of rows (typically thousands) to your function at once, leveraging libraries like Pandas or NumPy for SIMD speed.

2. Lazy Execution is Your Friend#

Snowpark DataFrames are lazily evaluated, just like Spark. Nothing happens until you call an action like .collect(), .save_as_table(), or .count().

Anti-pattern: Calling .collect() to debug intermediate steps. Best Practice: Use .show() for debugging, which limits the data fetched, or keep everything lazy until the final write. Pulling data back to the client (collect()) is often the bottleneck in what should be a server-side operation.

3. Manage Your Imports#

When you create a Stored Procedure, you often zip up local code. Be careful what you include.

• Don’t upload massive libraries: If a library is available in the Snowflake Anaconda channel, use packages=['numpy', 'pandas'] rather than uploading wheel files.
• Prune your directory: Don’t recursively upload your entire project folder if you only need one utility file.

# Efficient registration
session.sproc.register(
    func=my_handler,
    packages=["snowflake-snowpark-python", "pandas"],
    imports=["./src/utils.py"] # Only import what you need
)

4. Local Testing Framework#

In 2025, deploying to prod to test is unacceptable. The snowflake-snowpark-python library has excellent local testing capabilities.

You can create a collaborative session that mocks the Snowflake backend for many operations. Use pytest for your UDF logic before you ever deploy it to the cloud.

from snowflake.snowpark import Session

def test_transformation():
    # Create a local dataframe (if using local testing framework)
    df = session.create_dataframe([[1, 2], [3, 4]], schema=["a", "b"])
    res = my_logic(df)
    assert res.count() == 2

5. Warehouse Sizing for Memory-Intensive Ops#

Snowpark Standard Warehouses (the default) are memory-constrained for Python processes compared to Snowpark-Optimized Warehouses.

If you are doing:

• Machine Learning training (sklearn, xgboost)
• Heavy logical manipulation in Pandas buffers

…you typically need a Snowpark-Optimized warehouse. The cost per credit is the same, but they consume 16x credits per hour? No, they consume the same credits per hour but have different memory/cache ratios? Correction: Snowpark-optimized warehouses cost 1.5x credits per hour compared to standard warehouses. Use them only when you hit memory limits (MemoryError).

Conclusion#

Snowpark Python bridges the gap between the flexibility of Python and the scale of Snowflake. By sticking to vectorization, minimizing data movement, and utilizing the Anaconda channel, you ensure your pipelines are robust and cost-effective.