Security & Governance Flashcards

Why is data governance important in a lakehouse environment?

Answer: To enforce data policies and control access

Data governance is important in a lakehouse environment to establish and enforce policies around data quality, security, privacy, and access. It ensures that data is managed responsibly, complies with regulatory requirements, and is trustworthy for all users, thereby mitigating risks and maximizing data value.

What is the purpose of row-level security in lakehouse systems?

Answer: Controls which rows users can access

Row-level security (RLS) in lakehouse systems allows administrators to define fine-grained access policies that control which specific rows of data a user can view or interact with, based on their identity or attributes. This ensures that sensitive information is only accessible to authorized individuals, enhancing data privacy and compliance.

Which feature supports auditing and compliance tracking?

Answer: Audit logging

Audit logging is a critical feature that supports auditing and compliance tracking by recording all significant actions performed on the data, such as data access, modifications, and administrative operations. These logs provide an immutable trail of activity, essential for demonstrating compliance with regulatory requirements and for forensic analysis.

How is encryption used in a lakehouse architecture?

Answer: Secures data at rest and in transit

Encryption in a lakehouse architecture is used to protect sensitive data from unauthorized access. It secures data both when it is stored (at rest) on storage systems and when it is being moved between systems (in transit), providing a comprehensive layer of security against breaches and ensuring data confidentiality.

Which component ensures secure user authentication?

Answer: IAM system

An Identity and Access Management (IAM) system is responsible for ensuring secure user authentication by verifying user identities and authorizing their access to resources within the lakehouse. It manages user credentials, roles, and permissions, providing a robust security framework to control who can access what data and functionalities.

What is a common practice for minimizing data exposure?

Answer: Data masking

Data masking is a common practice for minimizing data exposure by replacing sensitive, real data with realistic, but fictitious, data. This allows non-production environments (like development or testing) to use data that mimics production data's structure and characteristics without exposing actual confidential information.