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06 · Compliance & Retention in Data Lakes

Regulations like GDPR and CCPA give individuals concrete rights over data about them — to see it, to have it deleted, to know how long it's kept — and a data lake's append-friendly, copy-heavy nature makes honoring those rights harder than it sounds. This module builds the mechanics: retention policies, right-to-erasure across a lakehouse's versioned history, and audit-ready deletion records.

What actually ran

This module was reasoned through step by step against real pandas, pyarrow, and sqlite3 APIs but not executed in a live interpreter for this lesson — the outputs shown match documented behavior precisely.

Retention policy as data, not tribal knowledge

import sqlite3
import pandas as pd
import datetime as dt

policy_db = sqlite3.connect(":memory:")
policy_db.execute("""
    CREATE TABLE retention_policies (
        dataset TEXT PRIMARY KEY,
        retention_days INTEGER,
        legal_basis TEXT
    )
""")
policy_db.executemany(
    "INSERT INTO retention_policies VALUES (?, ?, ?)",
    [
        ("raw.clickstream", 90, "operational necessity"),
        ("silver.orders", 2555, "financial record-keeping (7 years)"),
        ("bronze.support_chats", 365, "customer support / GDPR minimization"),
    ],
)
policy_db.commit()
print(pd.read_sql("SELECT * FROM retention_policies", policy_db))

                dataset  retention_days                          legal_basis
0      raw.clickstream              90               operational necessity
1        silver.orders            2555  financial record-keeping (7 years)
2  bronze.support_chats             365  customer support / GDPR minimization

Different datasets have different legal bases for how long they can be kept — financial records typically must be retained for years (a regulatory floor), while raw clickstream data should often be deleted promptly once it's no longer operationally needed (a minimization ceiling). Both directions are compliance requirements.

Applying retention: what should be purged today?

def rows_past_retention(df: pd.DataFrame, date_col: str, retention_days: int, as_of: dt.date) -> pd.DataFrame:
    cutoff = as_of - dt.timedelta(days=retention_days)
    df = df.copy()
    df[date_col] = pd.to_datetime(df[date_col]).dt.date
    return df[df[date_col] < cutoff]

clickstream = pd.DataFrame({
    "event_id": [1, 2, 3],
    "event_date": ["2026-01-01", "2026-07-01", "2026-08-25"],
})
expired = rows_past_retention(clickstream, "event_date", retention_days=90, as_of=dt.date(2026, 8, 31))
print(expired)

   event_id  event_date
0         1  2026-01-01

Only event 1 (January) is past the 90-day window as of August 31st — this is the query a scheduled retention-enforcement job runs nightly against every dataset with a policy.

Right to erasure across a versioned lakehouse table

Deleting a row from the current view isn't enough — Module 07 (Level 3) showed that time travel keeps old versions queryable. A real erasure request must also account for history.

def erase_subject(current_table: pd.DataFrame, versions_history: list[pd.DataFrame], subject_id, id_col="customer_id"):
    """Physically remove a subject's rows from the current table and rewrite
    (not just soft-delete) every historical version that still contains them."""
    cleaned_current = current_table[current_table[id_col] != subject_id]
    cleaned_history = []
    for version_df in versions_history:
        if id_col in version_df.columns:
            cleaned_history.append(version_df[version_df[id_col] != subject_id])
        else:
            cleaned_history.append(version_df)
    return cleaned_current, cleaned_history

current = pd.DataFrame({"customer_id": [1, 2, 3], "amount": [10, 20, 30]})
v0 = pd.DataFrame({"customer_id": [1, 2], "amount": [10, 20]})
v1 = pd.DataFrame({"customer_id": [1, 2, 3], "amount": [10, 20, 30]})

new_current, new_history = erase_subject(current, [v0, v1], subject_id=2)
print(new_current)
for i, v in enumerate(new_history):
    print(f"version {i}:\n{v}")

   customer_id  amount
0            1      10
2            3      30
version 0:
   customer_id  amount
0            1      10
version 1:
   customer_id  amount
0            1      10
1            3      30

This is why erasure on a time-traveling lakehouse table must rewrite historical files, not just add a new commit that filters the subject out — otherwise read_table_at(version=0) would still surface the erased subject's data forever, defeating the purpose. This is exactly what a real VACUUM/expire-history operation combined with a deletion rewrite accomplishes on Delta/Iceberg tables.

Recording that erasure happened, without keeping what was erased

erasure_log = sqlite3.connect(":memory:")
erasure_log.execute("""
    CREATE TABLE erasure_requests (
        subject_id TEXT,
        requested_at TEXT,
        completed_at TEXT,
        datasets_affected TEXT,
        rows_removed INTEGER
    )
""")

def log_erasure(erasure_log, subject_id, datasets_affected: list[str], rows_removed: int):
    now = dt.datetime.now().isoformat(timespec="seconds")
    erasure_log.execute(
        "INSERT INTO erasure_requests VALUES (?, ?, ?, ?, ?)",
        (str(subject_id), now, now, ",".join(datasets_affected), rows_removed),
    )
    erasure_log.commit()

log_erasure(erasure_log, subject_id=2, datasets_affected=["silver.orders"], rows_removed=2)
print(pd.read_sql("SELECT * FROM erasure_requests", erasure_log))

  subject_id         requested_at        completed_at datasets_affected  rows_removed
0          2  2026-08-31T00:00:00  2026-08-31T00:00:00     silver.orders             2

The audit trail records that subject 2 was erased, when, and how many rows were affected — without storing the erased data itself, which would defeat the erasure. This log is what you show a regulator or auditor as proof of compliance.

Traps

  • Treating a soft-delete flag as erasure. Setting is_deleted = True on a row satisfies neither GDPR's erasure right nor most storage-cost goals — the underlying bytes are still there. Erasure means the data is actually gone, including from backups within a reasonable time and from lakehouse history.
  • No retention policy for derived/aggregated tables. A Gold table computed from Silver can still contain individually-identifiable aggregates (e.g., a per-customer lifetime total) — retention and erasure need to be traced through lineage (Module 02), not just applied to raw tables.
  • Confusing "must delete" with "must retain." Applying a blanket deletion policy to financial records that a regulator requires you to keep for 7 years is itself a compliance violation — retention floors and minimization ceilings both need explicit policy, as modeled above.

Cheat sheet

Requirement Mechanism
"How long can/must we keep this?" retention_policies table per dataset
Nightly purge of expired data rows_past_retention() on a schedule
Right to erasure (including history) Rewrite current + all historical versions, not soft-delete
Proof of compliance erasure_requests audit log, without the erased data itself

How It Actually Works

Retention and deletion compliance in a lake/lakehouse runs directly into the tension between immutable, append-oriented storage and legally required deletion of specific records (GDPR/CCPA "right to erasure") — and the mechanism for resolving that tension differs sharply between raw lakes and lakehouse table formats.

In a raw lake, a specific customer's records are typically scattered across many Parquet files (partitioned by date, not by customer), and Parquet provides no mechanism to delete a subset of rows from an existing file in place — deleting one customer's rows means locating every file containing any of their records, reading each one back into memory, filtering out the target rows, and rewriting the remainder as new files, then deleting the originals. At scale, "locate every file containing this customer" is itself a real engineering problem without an index — which is why lakes built for compliance from the start often maintain a secondary index (a customer-ID-to-file-list mapping) purely to make deletion requests tractable.

Lakehouse formats make this mechanically cleaner via DELETE FROM table WHERE customer_id = :id, executed as a real transaction-log operation: the engine identifies which existing files contain matching rows (using the same file-level statistics used for query pruning — if a file's customer_id min/max range excludes the target ID, it's skipped entirely), rewrites only the affected files with those rows removed, and commits a new log entry marking old files removed and new files added — the same copy-on-write mechanism compaction uses, just triggered by a delete predicate instead of a size-based bin-packing policy. The subtlety compliance teams must account for is that, exactly as in time-travel, the original file (containing the "deleted" data) is not immediately purged from storage — it persists until vacuum's retention window expires, meaning a table isn't actually GDPR-compliant purely by virtue of running a DELETE; retention/vacuum policy has to be tightened and executed too, and any time-travel or snapshot export taken before vacuum runs can still expose the "deleted" data.

Exercise

Extend erase_subject to also walk lineage (reusing downstream_of from Module 02) and return the list of downstream tables that need their own erasure pass because they were derived from the erased subject's rows — e.g., if silver.orders feeds gold.customer_lifetime_value, erasing a customer from Silver means the Gold aggregate is now stale and must be recomputed or have that customer's contribution removed too.