Databricks have just announced their Series J funding round for $10B. They also announced a 60% year over year growth. This means a lot more Data is going into Databricks, and it is used for even more use cases; from traditional Business Intelligence, Data Engineering and Data Science to more cutting-edge Gen AI and Machine Learning.
However, securing your data, tracing lineage but also allowing the right people the right access is now more critical than ever. In this blog we investigate best practices, review how Databricks supports secure operations, and look at different types of use cases for various organizations
Understanding Databricks Data Security
Key components of Databricks’ data security offering include:
- Identity and Access Management (IAM): Controls user and service principal access to data and compute resources.
- Encryption: Safeguards data in transit and at rest with advanced encryption protocols.
- Data Governance: Databricks Unity Catalog enables centralized access control and metadata management, ensuring consistent data usage policies across all Databases.
- Monitoring and Auditing: Logs and alerts for tracking data access and activity levels provide transparency and compliance capabilities.
5 Best Practices for Data Security in Databricks
Here we discuss our recommended best practices for setting up your Data Security in Databricks, whether you’re at the start of a new Databricks deployment or whether you’re a seasoned Databricks user with many megastores.
1. Centralize Governance with Unity Catalog
Most importantly we strongly recommend using Unity Catalog to manage permissions at the table, row and/or column level to ensure only authorized users can access sensitive data. Unity Catalog should also be used to set up and lineage tracking for regulatory compliance and debugging.
2. Implement Fine-Grained Access Controls
Use Databricks attribute-based access control (ABAC) (currently in private preview) for dynamic policies which are much easier to control and customized. Integrate with an IAM provider such as AWS IAM, Azure Active Directory, or GCP Identity for federated authentication.
3. Ensure Encryption at All Levels
Enable encryption for all data at rest using customer-managed keys (CMKs) when possible. Databricks supports encryption using industry-standard AES-256 for data at rest and TLS 1.2+ for data transfer, ensuring compliance with GDPR, HIPAA, and other regulations.
4. Automate Monitoring and Alerts
Configure automated monitoring tools to detect anomalous data access patterns or potential breaches.
5. Adopt a Zero-Trust Approach
Regularly audit permissions and reduce over-privileged accounts. Enforce multi-factor authentication (MFA) for all user accounts by default.
Wrapping Up
Databricks is a scalable, secure and collaborative data platform. Using our best practices and correctly leveraging the advanced security features you can unlock value from you data while maintaining compliance and mitigating risks.
Data security is not just a technical check box – it’s a strategic must have. By incorporating security into everyday work, you can ensure that your data assets are secure and trustworthy. For more details reach out to us at ALTR.com
