Data availability, usability, integrity, and security are words that we sometimes hear a lot. But what do they actually look like when put into practice? That’s where data governance comes in. This becomes especially tricky when working with real-time data architectures.

Tushar Thole (Senior Manager, Engineering, Trust & Security, Confluent) focuses on delivering features for software-defined storage, software-defined networking (SD-WAN), security, and cloud-native domains. In this episode, he shares the importance of real-time data governance and the product portfolio—Stream Governance, which his team has been building to fostering the collaboration and knowledge sharing necessary to become an event-centric business while remaining compliant within an ever-evolving landscape of data regulations. 

With the increase of data volume, variety, and velocity, data governance is mandatory for trustworthy, usable, accurate, and accessible data across organizations, especially with distributed data in motion. 

When it comes to choosing a tool to govern real-time distributed data, there is often a paradox of choice. Some tools are built for handling data at rest, while open source alternatives lack features and are not managed services that can be integrated with the Apache Kafka® ecosystem natively. 

To solve governance use cases by delivering high-quality data assets, Tushar and his team have been taking Confluent Schema Registry, considered the de facto metadata management standard for the ecosystem, to the next level. This approach to governance allows organizations to scale Kafka operations for real-time observability with security and quality. 

The fully managed, cloud-native Stream Governance framework is based on three key workflows: 

• Stream catalog: Search and discover data in a self-service fashion
• Stream lineage: Understand the complex data relationships with interactive, end-to-end maps of event streams 
• Stream quality: Deliver trusted, high-quality event streams to the organization 

Tushar also shares use cases around data governance and sheds light on the Stream Governance roadmap. 

EPISODE LINKS

• Stream Governance – How it Works
• Data Mess to Data Mesh | Jay Kreps
• Demo: Stream Governance
• Data Governance for Real Time Data
• Watch the video version of this podcast
• Kris Jenkins Twitter
• Streaming Audio Playlist 
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Live demo: Intro to Event-Driven Microservices with Confluent
• Use PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)