GDPR and CCPA Compliance Best Practices Using InleData

Data has been around for centuries and it is even more powerful today. Companies collect all kinds of data about consumers, but the government stepped in to enforce stricter regulations on how they use that data.

Although companies try to comply with GDPR and CCPA, there’s still a lot more and a long way to go. Companies are scrambling to find a way to meet the requirements of these. A comprehensive solution that can help boost consumer confidence and improve data-based research would make it easier for these businesses as they transition into this new era.

This article tells you how you can leverage InleData for managing General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) compliance for Data Lake.

Delta Lake on the top of Data Lake provides structured data management while adding a transactional layer. 

This leads to simplifying your ability for locating and removing personal data which is personal information in response to CCPA or GDPR consumer requests.

The Problem

Your company may be managing thousands of terabytes of personal data in the cloud. The pressure to bring the datasets into compliance with GDPR and CCPA, but could be a daunting task for the larger datasets that are stored in Data Lakes because it takes time and resources to process all the data.

These challenges typically arise due to these factors:

1. Whether you have data petabyte in one location or spread across multiple, user data could be distributed and stored across different datasets.
2. The Ad-hoc queries for finding data can be quite expensive for some users since it needs entire table scans. With the approach of GDPR/CCPA compliance, it could result in weeks’ worth of operational and engineering effort.
3. A data lake cannot perform the row-level “Update” or “Delete” operations natively and are inherently append-only, meaning you must rewrite data partitions.
4. Data Lake does not provide the offerings of capabilities of ACID transactions or efficient methods for finding relevant data.
5. Data hygiene can be quite challenging in Data Lake as they are designed to support tolerance of partitions with eventual consistency. To assure clean data, rigorous practices are required.

This often leads to organizations ending up writing complicated and consuming a lot of time dealing with GDPR and CCPA. 

For instance, even if you are uploading portions of Data Lake within a Data Warehousing where deletion activities are done regarding the GDPR and CCPA compliance.

This can double complexity and minimizes the fidelity of data by producing several copies of data. 

How InleData Can Address this Challenge?

To solve the challenges that are listed above, the ultimate approach for a Data Lake a GDPR and CCPA compliant needs:

1. Pseudonymization, also known as the personal data reversible tokenization elements to keys cannot be identified externally. 
2. Pseudonyms must be linked with information storage and not identifiers.
3. Strict access maintenance and the use policies must be done by combining identifiers and pseudonyms.
4. Bucket policies or pipelines for raw data removal on timelines could help to comply with applicable law.
5. Structuring pipelines for locating and erasing identifiers.
6. The capabilities of ACID laid overhead of Data Lake, preventing readers from experiencing the adverse effects when delete or update actions are performed.
7. Support the high-performing pipelines. For instance, cleaning the data of 5TB in ten minutes.

Conclusion:

InleData is the next-gen tool to address the compliance requirements of GDPR and CCPA. This is because it adds transactional capabilities to Data Lakes. We hope you find this article useful. To know more, connect with us or request a demo.