Databricks Analytics Data Detection
Learn how to detect analytics data in Databricks environments. Follow step-by-step guidance for SOC 2 compliance and data governance.
Why It Matters
The core goal is to identify every location where analytics data is stored within your Databricks environment, so you can remediate unintended exposures before they become breaches. Scanning for analytics data in Databricks is a priority for organizations subject to SOC 2, as it helps you prove you've discovered and accounted for all business intelligence assets—mitigating the risk of shadow data spreading across unauthorized locations.
A thorough scan delivers immediate visibility into your analytics data landscape, laying the foundation for automated policy enforcement and ongoing compliance.
Prerequisites
Permissions & Roles
- Databricks admin or service principal
- catalogs/read, schemas/read, tables/read privileges
- Ability to install Databricks CLI or Terraform
External Tools
- Databricks CLI
- Cyera DSPM account
- API credentials
Prior Setup
- Databricks workspace provisioned
- Unity Catalog enabled
- CLI authenticated
- Networking rules configured
Introducing Cyera
Cyera is a modern Data Security Posture Management (DSPM) platform that discovers, classifies, and continuously monitors your sensitive data across cloud services. By leveraging advanced AI and machine learning algorithms for pattern recognition and semantic analysis, Cyera automatically identifies analytics data including dashboards, reports, metrics, and business intelligence assets in Databricks, ensuring you maintain complete visibility over your data analytics landscape and meet SOC 2 audit requirements in real time.
Step-by-Step Guide
Ensure Unity Catalog is enabled in your account and create a service principal with the minimum required privileges to access analytics tables and metadata.
In the Cyera portal, navigate to Integrations → DSPM → Add new. Select Databricks, provide your host URL and service principal details, then define the scan scope to include analytics workspaces and BI tables.
Configure webhooks or streaming exports to push analytics data discovery results into your business intelligence platforms or governance dashboards. Link findings to existing data catalog systems like Apache Atlas or AWS Glue.
Review the initial detection report, prioritize tables containing business metrics and KPIs, and adjust detection rules to identify dashboard data, reporting datasets, and analytical models. Schedule recurring scans to maintain visibility across your analytics pipeline.
Architecture & Workflow
Databricks Unity Catalog
Source of metadata for analytics tables, views, and notebooks
Cyera Connector
Pulls metadata and samples analytics data for classification
Cyera AI Engine
Applies ML models for analytics data pattern detection
Reporting & Governance
Analytics dashboards, alerts, and data lineage tracking
Data Flow Summary
Best Practices & Tips
Performance Considerations
- Focus scans on active analytics workspaces
- Use incremental discovery for large datasets
- Prioritize business-critical dashboards and reports
Analytics Data Classification
- Define patterns for KPIs, metrics, and dashboard data
- Classify by business unit and sensitivity level
- Tag analytics models and ML training datasets
Common Pitfalls
- Missing analytics data in external storage mounts
- Overlooking temporary analytical results
- Failing to track data lineage in ML pipelines