Implement Delta Lake data management patterns including GDPR, PII handling, and data lifecycle.
Use when implementing data retention, handling GDPR requests,
or managing data lifecycle in Delta Lake.
Trigger with phrases like "databricks GDPR", "databricks PII",
"databricks data retention", "databricks data lifecycle", "delete user data".
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Implement Delta Lake data management patterns including GDPR, PII handling, and data lifecycle.
Use when implementing data retention, handling GDPR requests,
or managing data lifecycle in Delta Lake.
Trigger with phrases like "databricks GDPR", "databricks PII",
"databricks data retention", "databricks data lifecycle", "delete user data".
allowed-tools
Read, Write, Edit, Bash(databricks:*)
version
1.0.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
compatible-with
claude-code, codex, openclaw
tags
["saas","databricks","databricks-data"]
Databricks Data Handling
Overview
Implement GDPR compliance, PII masking, data retention, and row-level security in Delta Lake with Unity Catalog. Covers data classification tagging, right-to-deletion workflows, automated retention enforcement, column-level masking functions, and subject access request (SAR) reporting.
Prerequisites
Unity Catalog enabled
Understanding of data classification requirements (GDPR, CCPA, HIPAA)
Admin access for tags and masking functions
Instructions
Step 1: Classify and Tag Data
Use Unity Catalog tags to classify tables and columns for automated compliance enforcement.
-- Tag tables with classification and retentionALTER TABLE prod_catalog.silver.customers
SET TAGS ('data_classification'='PII', 'retention_days'='730');
ALTER TABLE prod_catalog.silver.orders
SET TAGS ('data_classification'='CONFIDENTIAL', 'retention_days'='365');
ALTER TABLE prod_catalog.gold.metrics
SET TAGS ('data_classification'='INTERNAL', 'retention_days'='1825');
-- Tag PII columnsALTER TABLE prod_catalog.silver.customers
ALTERCOLUMN email SET TAGS ('pii_type'='email');
ALTER TABLE prod_catalog.silver.customers
ALTERCOLUMN phone SET TAGS ( );
prod_catalog.silver.customers
full_name TAGS ( );
'pii_type'
=
'phone'
ALTER TABLE
ALTER
COLUMN
SET
'pii_type'
=
'name'
Step 2: GDPR Right-to-Deletion
Delete all user data across PII-tagged tables with audit logging.
from pyspark.sql import SparkSession
from datetime import datetime
spark = SparkSession.builder.getOrCreate()
classGDPRHandler:
"""Handle GDPR deletion requests across all PII-tagged tables."""def__init__(self, catalog: str):
self.catalog = catalog
deffind_pii_tables(self) -> list[str]:
"""Find all tables tagged as PII."""
result = spark.sql(f"""
SELECT table_catalog, table_schema, table_name
FROM {self.catalog}.information_schema.table_tags
WHERE tag_name = 'data_classification' AND tag_value = 'PII'
""").collect()
return [f"{r.table_catalog}.{r.table_schema}.{r.table_name}"for r in result]
defprocess_deletion(self, user_id: str, request_id: str, dry_run: bool = True) -> dict:
"""Delete user data from all PII tables. Returns audit record."""
pii_tables = self.find_pii_tables()
audit = {
"request_id": request_id,
"user_id": user_id,
"timestamp": datetime.utcnow().isoformat(),
"dry_run": dry_run,
"tables_processed": [],
}
for table in pii_tables:
# Check if table has a user_id-like column
cols = [c.name for c in spark.table(table).schema]
user_col = next((c for c in cols if c in ("user_id", "customer_id", "account_id")), None)
ifnot user_col:
continue
count = spark.sql(
f"SELECT COUNT(*) AS cnt FROM {table} WHERE {user_col} = '{user_id}'"
).first().cnt
if count > 0andnot dry_run:
spark.sql(f"DELETE FROM {table} WHERE {user_col} = '{user_id}'")
audit["tables_processed"].append({
"table": table,
"column": user_col,
"rows_affected": count,
"action": "DELETED"ifnot dry_run else"WOULD_DELETE",
})
# Log audit recordifnot dry_run:
spark.createDataFrame([audit]).write.mode("append").saveAsTable(
f"{self.catalog}.compliance.gdpr_audit_log"
)
return audit
# Usage
gdpr = GDPRHandler("prod_catalog")
# Always dry-run first
report = gdpr.process_deletion("user-12345", "GDPR-2024-001", dry_run=True)
for t in report["tables_processed"]:
print(f" {t['table']}: {t['rows_affected']} rows {t['action']}")
Step 3: Automated Data Retention
classRetentionEnforcer:
"""Delete data older than retention policy set via table tags."""def__init__(self, catalog: str):
self.catalog = catalog
defenforce(self, dry_run: bool = True) -> list[dict]:
"""Process all tables with retention_days tag."""
tagged = spark.sql(f"""
SELECT table_catalog, table_schema, table_name, tag_value AS retention_days
FROM {self.catalog}.information_schema.table_tags
WHERE tag_name = 'retention_days'
""").collect()
results = []
for row in tagged:
table = f"{row.table_catalog}.{row.table_schema}.{row.table_name}"
retention_days = int(row.retention_days)
# Find date column (prefer created_at, event_date, order_date)
cols = [c.name for c in spark.table(table).schema]
date_col = next(
(c for c in cols if c in ("created_at", "event_date", "order_date", "timestamp")),
None,
)
ifnot date_col:
continue
expired = spark.sql(f"""
SELECT COUNT(*) AS cnt FROM {table}
WHERE {date_col} < current_timestamp() - INTERVAL {retention_days} DAYS
""").first().cnt
if expired > 0andnot dry_run:
spark.sql(f"""
DELETE FROM {table}
WHERE {date_col} < current_timestamp() - INTERVAL {retention_days} DAYS
""")
# Clean up deleted files
spark.sql(f"VACUUM {table} RETAIN 168 HOURS")
results.append({
"table": table, "retention_days": retention_days,
"expired_rows": expired, "action": "DELETED"ifnot dry_run else"WOULD_DELETE",
})
return results
# Schedule as a daily Databricks job
enforcer = RetentionEnforcer("prod_catalog")
for r in enforcer.enforce(dry_run=True):
print(f" {r['table']}: {r['expired_rows']} rows > {r['retention_days']} days {r['action']}")
Step 4: Column-Level PII Masking
-- Create masking functions for different PII typesCREATEOR REPLACE FUNCTION prod_catalog.compliance.mask_email(val STRING)
RETURN IF(IS_ACCOUNT_GROUP_MEMBER('pii-readers'), val,
CONCAT(LEFT(val, 1), '***@', SUBSTRING_INDEX(val, '@', -1)));
CREATEOR REPLACE FUNCTION prod_catalog.compliance.mask_phone(val STRING)
RETURN IF(IS_ACCOUNT_GROUP_MEMBER('pii-readers'), val,
CONCAT('***-***-', RIGHT(val, 4)));
CREATEOR REPLACE FUNCTION prod_catalog.compliance.mask_name(val STRING)
RETURN IF(IS_ACCOUNT_GROUP_MEMBER('pii-readers'), val,
CONCAT(LEFT(val, 1), REPEAT('*', LENGTH(val) -1)));
-- Apply masks to columnsALTER TABLE prod_catalog.silver.customers
ALTERCOLUMN email SET MASK prod_catalog.compliance.mask_email;
ALTER TABLE prod_catalog.silver.customers
ALTERCOLUMN phone SET MASK prod_catalog.compliance.mask_phone;
ALTER TABLE prod_catalog.silver.customers
ALTERCOLUMN full_name SET MASK prod_catalog.compliance.mask_name;
-- Test: non-privileged users see masked data-- email: j***@company.com-- phone: ***-***-1234-- name: J****
Step 5: Row-Level Security
-- Restrict data access by department/regionCREATEOR REPLACE FUNCTION prod_catalog.compliance.region_filter(region STRING)
RETURN IF(IS_ACCOUNT_GROUP_MEMBER('global-admins'), true,
region IN (SELECT allowed_region
FROM prod_catalog.compliance.user_region_access
WHERE user_email =current_user()));
ALTER TABLE prod_catalog.gold.sales
SETROWFILTER prod_catalog.compliance.region_filter ON (region);
-- Analysts only see data for their assigned regions
Step 6: Subject Access Request (SAR)
defgenerate_sar_report(catalog: str, user_id: str) -> dict:
"""Generate a GDPR Subject Access Request report."""
gdpr = GDPRHandler(catalog)
pii_tables = gdpr.find_pii_tables()
report = {"user_id": user_id, "generated_at": datetime.utcnow().isoformat(), "data": {}}
for table in pii_tables:
cols = [c.name for c in spark.table(table).schema]
user_col = next((c for c in cols if c in ("user_id", "customer_id")), None)
ifnot user_col:
continue
rows = spark.sql(f"SELECT * FROM {table} WHERE {user_col} = '{user_id}'").toPandas()
ifnot rows.empty:
report["data"][table] = rows.to_dict(orient="records")
return report
# Generate and export
sar = generate_sar_report("prod_catalog", "user-12345")
print(f"Found data in {len(sar['data'])} tables")
Output
Data classification tags on tables and PII columns
GDPR deletion workflow with dry-run and audit logging
Automated retention enforcement via tagged policies
Column masking functions for email, phone, name
Row-level security restricting access by region/department
SAR report generation for compliance requests
Error Handling
Error
Cause
Solution
VACUUM fails
Retention below 7 days
Set minimum RETAIN 168 HOURS
DELETE times out
Very large table
Partition deletes across multiple runs
Mask function error
Column type mismatch
Ensure mask function signature matches column type
Missing user_id column
Non-standard schema
Maintain a table-to-user-column mapping
Row filter performance
Complex subquery
Materialize user permissions as a small lookup table
Examples
Quick Compliance Check
-- Find all PII-tagged tables and their masking statusSELECT t.table_name, t.tag_value AS classification,
COUNT(c.column_name) AS masked_columns
FROM prod_catalog.information_schema.table_tags t
LEFTJOIN prod_catalog.information_schema.column_tags c
ON t.table_name = c.table_name AND c.tag_name ='pii_type'WHERE t.tag_name ='data_classification'AND t.tag_value ='PII'GROUPBY t.table_name, t.tag_value;
