How to Anonymize Vehicle Data with Databricks Delta Live Tables (DLT)

[rosinaKazakova]

In the age of connected cars and telematics, organizations collect massive volumes of vehicle data—GPS coordinates, VINs, driver IDs, diagnostic readings, and more. While this data is a goldmine for analytics, it often contains personally identifiable information (PII) and sensitive details that must be protected for privacy, compliance, and ethical use.

Databricks Delta Live Tables (DLT) offers a powerful way to not only process vehicle data at scale but also to anonymize sensitive fields in a repeatable, auditable pipeline. In this post, we’ll walk through a pattern for anonymizing vehicle data using DLT, ensuring privacy without sacrificing analytical value.

Why Anonymize Vehicle Data?

Vehicle data can reveal:

• VINs (Vehicle Identification Numbers) — which can be linked to registration records.
  
  
• Driver or Owner Identifiers — such as license numbers or internal IDs.
  
  
• Precise GPS coordinates — that can track individuals’ movements.
  
  
• Timestamps and trip patterns — which can be linked back to a single vehicle.
  
  

Regulations like GDPR, CCPA, and other data protection laws require that organizations store and process such data in a way that prevents identification unless explicitly needed. Anonymization allows you to use the data for analytics while protecting individuals.

The Role of Delta Live Tables (DLT)

Delta Live Tables in Databricks provides:

• Declarative ETL pipelines in Python or SQL.
  
  
• Built-in data quality checks.
  
  
• Automatic lineage and versioning.
  
  
• Simple orchestration for streaming and batch data.
  
  

This makes DLT ideal for building privacy-preserving data pipelines.

Example: Anonymizing VIN and GPS Data

Let’s imagine you have an incoming stream of raw telematics data like this:

vin	timestamp	latitude	longitude	speed_kmh
1HGCM82633A…	2025-08-10T14:33:05	37.7749	-122.4194	68
2HGES16555H…	2025-08-10T14:34:01	34.0522	-118.2437	52

We’ll anonymize:

• VIN → Replace with a hashed value.
  
  
• Coordinates → Obfuscate by rounding to the nearest 0.1 degree (reducing precision).
  
  
• Timestamps → Shift or truncate to reduce identifiability.
  
  

Step 1: Create a Raw Ingestion Table

import dlt
from pyspark.sql.functions import col

@dlt.table(
    comment="Raw incoming vehicle telemetry data"
)
def raw_vehicle_data():
    return (
        spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "json")
        .load("/mnt/data/vehicle/raw/")
    )

Step 2: Apply Anonymization Transformations

from pyspark.sql.functions import sha2, concat_ws, round as spark_round, to_date

@dlt.table(
    comment="Anonymized vehicle telemetry data"
)
def anonymized_vehicle_data():
    df = dlt.read_stream("raw_vehicle_data")
    
    return (
        df.withColumn("vin_hash", sha2(col("vin"), 256))
          .drop("vin")
          .withColumn("latitude", spark_round(col("latitude"), 1))
          .withColumn("longitude", spark_round(col("longitude"), 1))
          .withColumn("date", to_date(col("timestamp")))
          .drop("timestamp")
    )

Here’s what happens:

• sha2(vin, 256) replaces VIN with a cryptographic hash.
  
  
• round(latitude, 1) and round(longitude, 1) obfuscate exact locations.
  
  
• to_date(timestamp) removes time granularity, keeping only the date.
  
  

Step 3: Add Data Quality Expectations

DLT allows you to enforce rules so that anonymization works consistently.

@Dlt.expect_all({
    "valid_latitude": "latitude BETWEEN -90 AND 90",
    "valid_longitude": "longitude BETWEEN -180 AND 180",
    "vin_hash_not_null": "vin_hash IS NOT NULL"
})
@dlt.table(
    comment="Validated and anonymized vehicle telemetry"
)
def validated_vehicle_data():
    return dlt.read("anonymized_vehicle_data")

Step 4: Store in a Privacy-Preserving Zone

Finally, write the anonymized data to a secure Delta table for downstream analytics.

CREATE LIVE TABLE vehicle_analytics
AS SELECT * FROM validated_vehicle_data;

Downstream analysts can work with the vehicle_analytics table without having access to raw PII.

Benefits of This Approach

• Automated & Reproducible — DLT’s declarative pipelines ensure consistent anonymization.
  
  
• Auditable — Data lineage shows how and when anonymization was applied.
  
  
• Streaming & Batch — Works for real-time and historical data.
  
  
• Regulatory Compliance — Reduces risk of privacy violations.
  
  

Final Thoughts

By leveraging Databricks Delta Live Tables, you can create privacy-by-design pipelines for vehicle data. This protects your organization legally, ethically, and reputationally—while keeping the data useful for analysis.

If you’re handling sensitive mobility or telematics datasets, consider building your ingestion and transformation flow directly in DLT with anonymization as a first-class step, not an afterthought.