You’re a data engineer who needs to ensure data privacy. Which governance tools should you use?

In any database, whether it is an operational system or a central data warehouse, there are different user groups with varying privileges. It is crucial to assign a role to each user group and specify the read/write privileges associated with that role. Afterwards, data masking should be applied based on the assigned privileges.

1. Protection of Sensitive Data: Data masking shields sensitive information by replacing or obscuring original data with realistic but fictitious values. 2. Privacy Safeguarding: It ensures confidentiality during testing, development, or sharing processes, preventing exposure of personally identifiable information (PII). 3. Authorization Maintenance: Allows authorized users access to data for testing or analytics without compromising sensitive details. 4. Realistic but Secure Data: Retains data realism for analysis while ensuring that sensitive data cannot be exploited or traced back to individuals. 5. Compliance Adherence: Helps comply with data protection regulations (such as GDPR or HIPAA) by safeguarding sensitive information.

To ensure data privacy, utilize governance tools such as Apache Ranger, Collibra, and IBM Guardium. These tools offer access controls, data classification, and auditing features, enabling comprehensive management and protection of sensitive data.

Implement privacy-centric governance tools like OneTrust, Privacera, and Varonis. These platforms empower data engineers to enforce privacy policies, manage access, and monitor data usage, ensuring compliance with regulations and safeguarding sensitive information.

?? Sometimes you must have the original sensitive personal data separate from anonymized version data because legal reasons; so keep the connection among those version with other data is important: data privacy vault can be a get option to do it. ? In general; it's based in how data modeling is design and management this versions of data with internal processes to generate those versions.

For Data Governance with Unified Data Analytics Databricks has also launched Catalog named as "Unity Catalog" which ease the help in building Data Mesh Architecture, Data Governance & also ease the project management by providing different workspace feature in which under the hood Unity Catalog will be used.

?To keep secure that privacy data has a high-level in data quality; you need to check it based in contextual information; for example, phone numbers change structure based in country. Then you need to condition your data quality rules based in this point sometimes; and tool need to support it; by side generic data quality rules must be applied. ?? Programming tools related in this topic like "great expectation" can be a option to check.

As a data engineer responsible for building an end-to-end data management framework and ensuring a robust privacy structure, consider the following: - Establish robust control and visibility over the changing data landscape in your organization. - Implement the right data access policies for effective data governance. - Apply suitable data masking methods to enhance privacy and security. - Classify data based on industry standards for streamlined management. - Utilize a comprehensive data catalog to provide full visibility into organizational data. - Implement an automated data discovery process integrated with your data observability framework

For Data Governance with Unified Data Analytics Databricks has also launched Catalog named as "Unity Catalog" which ease the help in building Data Mesh Architecture, Data Governance & also ease the project management by providing different workspace feature in which under the hood Unity Catalog will be used.

Compliance is the Key: Adherence to privacy laws such as GDPR, HIPAA, CCPA, and other regional or industry-specific regulations is critical in data governance mechanisms. Non-compliance can lead to severe regulatory actions, fines, and damage to the organization's reputation. Thus, it is also important to choose tools that are designed with privacy regulations in mind and provide features to help meet compliance requirements. Regularly update policies and practices to align with evolving data privacy legislation worldwide.