- Platform Release 6.5
- Privacera Platform Release 6.5
- Enhancements and updates in Privacera Access Management 6.5 release
- Enhancements and updates in Privacera Discovery 6.5 release
- Enhancements and updates in Privacera Encryption 6.5 release
- Deprecation of older version of PolicySync
- Upgrade Prerequisites
- Supported versions of third-party systems
- Documentation changelog
- Known Issues 6.5
- Platform - Supported Versions of Third-Party Systems
- Platform Support Policy and End-of-Support Dates
- Privacera Platform Release 6.5
- Privacera Platform Installation
- About Privacera Manager (PM)
- Install overview
- Prerequisites
- Installation
- Default services configuration
- Component services configurations
- Access Management
- Data Server
- UserSync
- Privacera Plugin
- Databricks
- Spark standalone
- Spark on EKS
- Portal SSO with PingFederate
- Trino Open Source
- Dremio
- AWS EMR
- AWS EMR with Native Apache Ranger
- GCP Dataproc
- Starburst Enterprise
- Privacera services (Data Assets)
- Audit Fluentd
- Grafana
- Ranger Tagsync
- Discovery
- Encryption & Masking
- Privacera Encryption Gateway (PEG) and Cryptography with Ranger KMS
- AWS S3 bucket encryption
- Ranger KMS
- AuthZ / AuthN
- Security
- Access Management
- Reference - Custom Properties
- Validation
- Additional Privacera Manager configurations
- Upgrade Privacera Manager
- Troubleshooting
- How to validate installation
- Possible Errors and Solutions in Privacera Manager
- Unable to Connect to Docker
- Terminate Installation
- 6.5 Platform Installation fails with invalid apiVersion
- Ansible Kubernetes Module does not load
- Unable to connect to Kubernetes Cluster
- Common Errors/Warnings in YAML Config Files
- Delete old unused Privacera Docker images
- Unable to debug error for an Ansible task
- Unable to upgrade from 4.x to 5.x or 6.x due to Zookeeper snapshot issue
- Storage issue in Privacera UserSync & PolicySync
- Permission Denied Errors in PM Docker Installation
- Unable to initialize the Discovery Kubernetes pod
- Portal service
- Grafana service
- Audit server
- Audit Fluentd
- Privacera Plugin
- How-to
- Appendix
- AWS topics
- AWS CLI
- AWS IAM
- Configure S3 for real-time scanning
- Install Docker and Docker compose (AWS-Linux-RHEL)
- AWS S3 MinIO quick setup
- Cross account IAM role for Databricks
- Integrate Privacera services in separate VPC
- Securely access S3 buckets ssing IAM roles
- Multiple AWS account support in Dataserver using Databricks
- Multiple AWS S3 IAM role support in Dataserver
- Azure topics
- GCP topics
- Kubernetes
- Microsoft SQL topics
- Snowflake configuration for PolicySync
- Create Azure resources
- Databricks
- Spark Plug-in
- Azure key vault
- Add custom properties
- Migrate Ranger KMS master key
- IAM policy for AWS controller
- Customize topic and table names
- Configure SSL for Privacera
- Configure Real-time scan across projects in GCP
- Upload custom SSL certificates
- Deployment size
- Service-level system properties
- PrestoSQL standalone installation
- AWS topics
- Privacera Platform User Guide
- Introduction to Privacera Platform
- Settings
- Data inventory
- Token generator
- System configuration
- Diagnostics
- Notifications
- How-to
- Privacera Discovery User Guide
- What is Discovery?
- Discovery Dashboard
- Scan Techniques
- Processing order of scan techniques
- Add and scan resources in a data source
- Start or cancel a scan
- Tags
- Dictionaries
- Patterns
- Scan status
- Data zone movement
- Models
- Disallowed Tags policy
- Rules
- Types of rules
- Example rules and classifications
- Create a structured rule
- Create an unstructured rule
- Create a rule mapping
- Export rules and mappings
- Import rules and mappings
- Post-processing in real-time and offline scans
- Enable post-processing
- Example of post-processing rules on tags
- List of structured rules
- Supported scan file formats
- Data Source Scanning
- Data Inventory
- TagSync using Apache Ranger
- Compliance Workflow
- Data zones and workflow policies
- Workflow Policies
- Alerts Dashboard
- Data Zone Dashboard
- Data zone movement
- Workflow policy use case example
- Discovery Health Check
- Reports
- How-to
- Privacera Encryption Guide
- Overview of Privacera Encryption
- Install Privacera Encryption
- Encryption Key Management
- Schemes
- Encryption with PEG REST API
- Privacera Encryption REST API
- PEG API endpoint
- PEG REST API encryption endpoints
- PEG REST API authentication methods on Privacera Platform
- Common PEG REST API fields
- Construct the datalist for the /protect endpoint
- Deconstruct the response from the /unprotect endpoint
- Example data transformation with the /unprotect endpoint and presentation scheme
- Example PEG API endpoints
- /authenticate
- /protect with encryption scheme
- /protect with masking scheme
- /protect with both encryption and masking schemes
- /unprotect without presentation scheme
- /unprotect with presentation scheme
- /unprotect with masking scheme
- REST API response partial success on bulk operations
- Audit details for PEG REST API accesses
- Make encryption API calls on behalf of another user
- Troubleshoot REST API Issues on Privacera Platform
- Privacera Encryption REST API
- Encryption with Databricks, Hive, Streamsets, Trino
- Databricks UDFs for encryption and masking on PrivaceraPlatform
- Hive UDFs for encryption on Privacera Platform
- StreamSets Data Collector (SDC) and Privacera Encryption on Privacera Platform
- Trino UDFs for encryption and masking on Privacera Platform
- Privacera Access Management User Guide
- Privacera Access Management
- How Polices are evaluated
- Resource policies
- Policies overview
- Creating Resource Based Policies
- Configure Policy with Attribute-Based Access Control
- Configuring Policy with Conditional Masking
- Tag Policies
- Entitlement
- Service Explorer
- Users, groups, and roles
- Permissions
- Reports
- Audit
- Security Zone
- Access Control using APIs
- AWS User Guide
- Overview of Privacera on AWS
- Configure policies for AWS services
- Using Athena with data access server
- Using DynamoDB with data access server
- Databricks access manager policy
- Accessing Kinesis with data access server
- Accessing Firehose with Data Access Server
- EMR user guide
- AWS S3 bucket encryption
- Getting started with Minio
- Plugins
- How to Get Support
- Coordinated Vulnerability Disclosure (CVD) Program of Privacera
- Shared Security Model
- Privacera Platform documentation changelog
Privacera Plugin in Databricks
Databricks
Privacera provides two types of plugin solutions for access control in Databricks clusters. Both plugins are mutually exclusive and cannot be enabled on the same cluster.
Databricks Spark Fine-Grained Access Control (FGAC) Plugin
Recommended for SQL, Python, R language notebooks.
Provides FGAC on databases with row filtering and column masking features.
Uses privacera_hive, privacera_s3, privacera_adls, privacera_files services for resource-based access control, and privacera_tag service for tag-based access control.
Uses the plugin implementation from Privacera.
Databricks Spark Object Level Access Control (OLAC) Plugin
OLAC plugin was introduced to provide an alternative solution for Scala language clusters, since using Scala language on Databricks Spark has some security concerns.
Recommended for Scala language notebooks.
Provides OLAC on S3 locations which you are trying to access via Spark.
Uses privacera_s3 service for resource-based access control and privacera_tag service for tag-based access control.
Uses the signed-authorization implementation from Privacera.
Databricks cluster deployment matrix with Privacera plugin
Job/Workflow use-case for automated cluster:
Run-Now will create the new cluster based on the definition mentioned in the job description.
Job Type | Languages | FGAC/DBX version | OLAC/DBX Version |
|---|---|---|---|
Notebook | Python/R/SQL | Supported [7.3, 9.1 , 10.4] | |
JAR | Java/Scala | Not supported | Supported[7.3, 9.1 , 10.4] |
spark-submit | Java/Scala/Python | Not supported | Supported[7.3, 9.1 , 10.4] |
Python | Python | Supported [7.3, 9.1 , 10.4] | |
Python wheel | Python | Supported [9.1 , 10.4] | |
Delta Live Tables pipeline | Not supported | Not supported |
Job on existing cluster:
Run-Now will use the existing cluster which is mentioned in the job description.
Job Type | Languages | FGAC/DBX version | OLAC |
|---|---|---|---|
Notebook | Python/R/SQL | supported [7.3, 9.1 , 10.4] | Not supported |
JAR | Java/Scala | Not supported | Not supported |
spark-submit | Java/Scala/Python | Not supported | Not supported |
Python | Python | Not supported | Not supported |
Python wheel | Python | supported [9.1 , 10.4] | Not supported |
Delta Live Tables pipeline | Not supported | Not supported |
Interactive use-case
Interactive use-case is running a notebook of SQL/Python on an interactive cluster.
Cluster Type | Languages | FGAC | OLAC |
|---|---|---|---|
Standard clusters | Scala/Python/R/SQL | Not supported | Supported [7.3,9.1,10.4] |
High Concurrency clusters | Python/R/SQL | Supported [7.3,9.1,10.4 | Supported [7.3,9.1,10.4] |
Single Node | Scala/Python/R/SQL | Not supported | Supported [7.3,9.1,10.4] |