Issue with Data Masking Persistence After Table Overwrite in Databricks

[Akshay_Petkar]

I have implemented data masking on my Databricks table, and it works well when I append new rows, as the masking and unmasking function as expected. However, when I overwrite the table, the unmasking does not seem to work correctly, and the data remains masked even when unmasking should apply.

Is there a specific way to handle data masking when overwriting tables in Databricks to ensure unmasked values persist correctly after an overwrite? Any insights or workarounds would be greatly appreciated!

[agallard]

Hi @Akshay_Petkar,

The issue with data masking not persisting correctly after an overwrite in Databricks likely stems from how the overwrite operation interacts with masked data and user-defined functions for masking/unmasking. In Databricks, overwrite operations replace the entire data in a table, which can disrupt the application of masking functions. Here are some strategies to ensure that data masking functions correctly even after an overwrite:

1. Apply Data Masking Logic During Overwrite

When overwriting a table in Databricks, ensure that the masking logic is applied at the time of the overwrite. This may involve integrating the masking function directly into the DataFrame transformation before the overwrite operation.

from pyspark.sql import functions as F

# Assuming you have a masking function
def mask_data(df):
    return df.withColumn("sensitive_column", F.expr("masking_function(sensitive_column)"))

# Load your new data and apply masking before overwriting
new_data = spark.read.format("delta").load("path_to_new_data")
masked_data = mask_data(new_data)

# Overwrite the table with masked data
masked_data.write.mode("overwrite").format("delta").saveAsTable("your_table_name")

This approach ensures that when the table is overwritten, the data is already masked, preventing issues with unmasking later.

2. Use Views to Mask Data Instead of Direct Column Masking

If the masking logic is applied as part of a view (e.g., using SQL views in Databricks), you can separate the physical data from the masking logic. This way, the actual data in the table is overwritten, but the masking/unmasking logic remains in the view and is always applied to the latest data.

CREATE OR REPLACE VIEW masked_view AS
SELECT
    masking_function(sensitive_column) AS sensitive_column,
    other_columns
FROM your_table_name

By querying masked_view instead of the table directly, you ensure that any new data (whether appended or overwritten) is automatically masked as per the view’s logic.

3. Apply Row-Level or Column-Level Security Policies (If Using Unity Catalog)

If you’re using Unity Catalog in Databricks, you can leverage row-level or column-level security controls to handle masking at a policy level. Unity Catalog allows you to set policies that control access to certain columns based on user roles, ensuring that masked data is consistently handled according to access permissions, even after an overwrite.

ALTER TABLE your_table_name
ALTER COLUMN sensitive_column
SET MASKING POLICY masking_policy_for_sensitive_column

4. Reapply Masking Policy or Function After Overwrite

If the table is overwritten without applying masking logic, you can reapply the masking function immediately after the overwrite to ensure the data is correctly masked.

# Overwrite the table with new data
new_data.write.mode("overwrite").format("delta").saveAsTable("your_table_name")

# Reapply the masking function to ensure consistency
masked_df = mask_data(spark.table("your_table_name"))
masked_df.write.mode("overwrite").format("delta").saveAsTable("your_table_name")

This ensures the masking logic is re-established each time the table is overwritten, so users querying the table will see the masked data.

5. Use Delta Lake’s MERGE Operation Instead of Overwrite

If feasible, use Delta Lake’s MERGE operation instead of overwrite. With MERGE, you can upsert rows while retaining control over the application of masking logic on specific rows, reducing the need to reapply masking policies for the entire table.

new_data = spark.read.format("delta").load("path_to_new_data")
masked_data = mask_data(new_data)

# Use Delta MERGE to upsert
from delta.tables import DeltaTable

delta_table = DeltaTable.forName(spark, "your_table_name")
delta_table.alias("target").merge(
    masked_data.alias("source"),
    "target.id = source.id"  # Your matching criteria
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()

MERGE helps avoid complete replacement of data and preserves existing masking logic, providing a safer way to handle data updates.

These methods will help ensure that data masking remains consistent and that unmasking functions as expected after overwriting in Databricks.

Let me know if you need further details on any of these solutions!

Regards!