Accelerating Data Science Initiatives with Databricks’ Rapid SQL Analytics and Privacera’s Centralized Data Access Governance. Databricks’ SQL Analytics helps data teams consolidate and simplify their data architectures. With SQL Analytics, data teams can perform BI and SQL workloads on the same multi-cloud lakehouse architecture enabling data scientists to perform advanced analytics on unstructured and large-scale data. This session will explore how Privacera’s advanced security, privacy, and governance capabilities seamlessly integrate with Databricks’ unified SQL Analytics approach to provide single pane visibility of data analytics from a centralized location. Attendees will learn how to: Rapidly access data to run high-fidelity analytics Implement a fully secure solution that ensures productivity, while controlling data access at fine-grained levels (row, column, and file) Easily enable consistent access policies across all systems and applications Support true data transparency across enterprises Comply with stringent industry and privacy regulations like GDPR, LGPD, HIPAA, CCPA, PCI DSS, RTBF, and more with rich auditing and reporting

1. Accelerating Data Science Initiatives with
Databricks’ SQL Analytics and Privacera’s
Centralized Data Access Governance
Don Bosco Durai
Co-Founder and CTO, Privacera

2. Agenda
▪ Backgrounds
▪ SQL Analytics Overview
▪ Security Challenges
▪ Privacera + SQL Analytics
Integration Overview
▪ Demo
▪ Key Benefits

3. About the Speaker
▪ Co-founder & CTO, Privacera
▪ Founding member of Apache
Ranger
▪ Strong security partner with
Databricks

4. Databricks Lakehouse - Persona Driven Approach
● Batch process v/s Real-time
● SQL Analytics simplifies usage of the
Lakehouse for Business users
● Data Scientist and Architects can
continue using ML Workflow Spark
Clusters
● Customizable security policies for
different use cases

5. Security and Compliance Challenges
Need consistent policies for the underlying dataset
Persona driven policies
Executives, Data Analyst, Data Scientist, Data Wranglers, etc.
Managing access permissions for different Personas
Can everyone run grant/revoke SQL commands?
What IAM role should be attached to the cluster?
Who can create a new cluster?

6. Security v/s Privacy Policies
▪ Preventing unauthorized usage of
systems
▪ Ensuring users don’t see the
incorrect information
▪ Creating boundaries to enforce
right action of the system
• “Data privacy may be defined as
the authorized, fair, and legitimate
processing of personal
information”
• Consent rights
• Do not share
Privacy
Security

7. Governance blind spot

8. Centralized Auditing and Reporting
● Centralize auditing
● Monitoring data access by classification
● Track usage by Purpose
● Generate attestation reports

9. Databricks Native Support for Security
Credential Passthrough
SQL Grant/Revoke Cluster Policies
IAM Roles for Cluster

10. Integration Overview
● Privacera extends Databrick’s native security
● Centrally manage policies for all Databricks workloads
● Consistent policies between Databricks SQL Analytics
and Python/SQL Workspaces
● Centralized collection of Audit Records
● Built on top of Apache Ranger
○ High Scalable
○ Support for multiple services and Clouds

11. Privacera - Traditional Databricks Workspaces Support
Fine Grained Access Control
SQL - Database, Table, Column
SQL - Dynamic Row Level Filtering
SQL - Dynamic Column Masking
S3/ADLS file level access control
Attribute Based Access Policies
Role Based Access Policies
Tag Based Policies
S3/ADLS file level access control
Attribute Based Access Policies
Role Based Access Policies
Tag Based Policies
• Scala Cluster
• Python/SQL Cluster

12. Databricks/Privacera - Ranger Plugin Architecture

13. SQL Analytics - Databricks + Privacera
Seamless integration
No Privacera deployment
No plugins and no init scripts
Privacera’s Policy Sync integration design
Security features identical to Databricks SQL Cluster

14. Privacera + SQL
Analytics Key Benefits
Privacera’s data access governance
platform seamlessly integrates with
SQL Analytics to provide:
● Fine-grained access control at row- and
column- levels with dynamic masking
● Single-pane visibility of data across
multiple cloud services in SQL Analytics
● Precise data usage with real-time
monitoring, logging, and audit trails
● Compliance with industry and privacy
regulations like GDPR, CCPA, HIPAA,
LGPD

15. Demo

16. Thank you!

17. Contact Me
SOCIAL
LinkedIn: https://www.linkedin.com/in/donboscodurai/
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