New Data Flow Execution Engine and Enterprise Metadata Sharing capabilities extends privacy protection and governance to a diverse set of data sources and workflows while simplifying management complexity. 

2.2 with Data Flow execution engine

Large organisations are increasingly required to handle data from wide range of streaming real-time sources, historical archives and third-party transfers that need to be seamlessly processed within a consistent set of privacy, secrecy and regulatory guidelines.

Privitar 2.2 represents a major milestone in the platform evolution with the addition of our Data Flow Execution engine that ensures consistent data de-identification and governance policies can be successfully applied across a diverse ecosystem of data processing infrastructure.

With this new update, Privitar now offers the deepest capability to deidentify data at rest and in-motion from Data Flow platforms such as Apache Kafka, NiFi, and Confluent. These data flows can be managed with the same granular capabilities as existing workflows from a variety of environments, including data warehouses, legacy systems, big data lakes, and cloud services.

This platform advancement provides significant benefits for organisations including:

  • A centralised or federated model for defining and enacting data governance policies that can be applied consistently throughout the organisation
  • A single management and reporting structure that applies to all data sources, workflows and projects across internal and cloud-based infrastructure
  • A proven method of auditing and documenting data protection and governance across the organisation to meet critical compliance requirements such as GDPR


2.2 with Enterprise Metadata Sharing

Alongside the introduction of the new Data Flow Execution engine, 2.2 adds Enterprise Metadata Sharing to address the need for more visibility and control over the data lifecycle to allow organisations to harvest, use and share datasets with the confidence that each activity is meeting privacy, security and governance standards.

Metadata Sharing allows the automated sharing data processing metadata by integrating with Enterprise Metadata Catalogues, such as Cloudera Navigator and Apache Atlas. The platform is now able to automatically record information about your data releases, such as intended recipient, approver and reason for sharing every time a new dataset is processed.

This platform advancement provides significant benefits for organisations including:

  • The ability to keep track of which data is safe to process, why and where it is coming from by visualising the entire data lineage.
  • A streamlined and automated data lifecycle management process through tag-based access controls.
  • Simplified compliance reporting through a unified approach that spans all data process across every department and use case


Download the 2.2 whitepaper

Discover you can optimise your data privacy strategy at enterprise-scale with Privitar, and plan and implement scalable and flexible data privacy execution and management.