With the enforcement of the General Data Protection Regulation (GDPR), organizations collecting personal data are now required to provide user data access, auditing, rectification, and right to erasure. Ensuring these capabilities in large-scale data lakes is not trivial.

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is a modern lakehouse technology that provides record-level operations, metadata tracking, and incremental querying — making it an excellent choice for building GDPR-compliant data pipelines.

This guide explores how to use Hudi to meet GDPR requirements while maintaining scalability and performance in your big data architecture.

Key GDPR Requirements for Data Engineers

GDPR Obligation What It Means for Data Pipelines
Right to Erasure (Article 17) Ability to delete specific user records on request
Right to Rectification Ability to update user data across datasets
Right to Access Ability to retrieve personal data quickly
Audit and Data Provenance Track data access and modification history
Data Minimization Store only what’s necessary, and manage schema evolution

Hudi’s architecture allows developers to meet these requirements while optimizing for storage and compute.

Using Hudi’s Record-Level Deletes for Right to Erasure

GDPR requires that users have the ability to request deletion of their data — also known as right to be forgotten.

With Hudi, you can perform record-level deletions using the primary key.

val deleteDF = spark.read.json("/requests/delete.json")  // contains record keys

deleteDF.write.format("hudi")
.option("hoodie.table.name", "user_data")
.option("hoodie.datasource.write.operation", "delete")
.option("hoodie.datasource.write.recordkey.field", "user_id")
.option("hoodie.datasource.write.precombine.field", "ts")
.mode(SaveMode.Append)
.save("/user/hudi/user_data")

The deleted records are physically removed during compaction in Copy-On-Write (COW) or marked for deletion in Merge-On-Read (MOR).

Performing Rectification (Update) Requests

GDPR also requires users to be able to correct inaccurate data.

Use Hudi’s upsert operation:

val correctedDF = spark.read.json("/requests/rectify.json")

correctedDF.write.format("hudi")
.option("hoodie.datasource.write.operation", "upsert")
.option("hoodie.datasource.write.recordkey.field", "user_id")
.option("hoodie.datasource.write.precombine.field", "ts")
.mode(SaveMode.Append)
.save("/user/hudi/user_data")

Only records with higher timestamps (via precombine.field) will overwrite existing records.

Data Access and Retrieval for Subject Access Requests

When users request their stored data, you must provide complete records efficiently.

Use Hudi’s incremental query capabilities to scan relevant records:

SELECT * FROM hudi_user_data
WHERE user_id = 'user_1234';

Alternatively, run point lookups using Hudi’s Spark or Presto integrations.

Enabling Auditing and Data Provenance

Hudi provides commit metadata, instant timestamps, and file-level history which can be leveraged for auditing purposes.

Enable metadata tracking:

hoodie.metadata.enable=true
hoodie.metadata.async.enable=true

You can also use Hive metastore or DeltaStreamer audit logs to build an audit trail of when and how user data was modified.

Store metadata snapshots in a secure location and integrate with Apache Atlas or other data governance tools.

Schema Evolution for Data Minimization

Hudi supports schema-on-write with evolution — meaning you can drop unnecessary fields over time while maintaining backward compatibility.

For example, to drop deprecated personal fields:

  1. Update the schema in your ingestion pipeline.
  2. Use the write.schema.evolution.enable=true setting.
hoodie.write.schema.evolution.enable=true
hoodie.avro.schema.validate=true

This helps implement data minimization and ensures only necessary data is stored long term.

Automating GDPR Pipelines

Use Apache Airflow, Nifi, or custom schedulers to automate GDPR compliance workflows:

  • Periodic deletion requests from a secure DB
  • Ingest and rectify correction files
  • Log every change in a data catalog

You can also define DAGs that track:

  • Delete -> Archive -> Compact
  • Rectify -> Update Hive table
  • Access request -> Email / API return

Partitioning Strategies for Easier Erasure

Partitioning by identifiable fields (e.g., region, date, user type) makes GDPR operations faster:

.option("hoodie.datasource.write.partitionpath.field", "region")

Combine with bucket index or global bloom index for efficient lookup and delete.

Best Practices for GDPR Compliance with Hudi

  • Use Copy-On-Write for cleaner record deletion
  • Enable metadata tables for efficient access
  • Store all GDPR requests in an auditable format
  • Separate hot and cold data via time-based partitioning
  • Perform regular compaction and archival for deleted records
  • Ensure secure access via Kerberos, Ranger, or Lake Formation

Conclusion

Apache Hudi provides a strong foundation for building GDPR-compliant data pipelines in modern data lakes. With built-in support for record-level deletions, upserts, schema evolution, and incremental queries, Hudi enables organizations to meet data privacy obligations while maintaining performance and scalability.

By integrating Hudi with a governance-aware pipeline and following best practices, you can confidently support GDPR and other privacy regulations in your big data infrastructure.