Confluent announces Stream Governance Additions

October 6, 2022

Confluent, Inc., the data streaming pioneer, today announced a new tier of capabilities for Stream Governance, the industry’s only fully managed governance suite for Apache Kafka and data in motion. With Stream Governance Advanced, organizations can resolve issues within complex pipelines easier with point-in-time lineage, discover and understand topics faster with business metadata, and enforce quality controls globally with Schema Registry. With more teams able to safely and confidently access data streams, organizations can build critical applications faster.

“Businesses heavily rely on real-time data to make fast and informed decisions, so it’s paramount that the right teams have quick access to trustworthy data,” said Chad Verbowski, Senior Vice President of Engineering, Confluent. “With Stream Governance, organizations can understand the full scope of streams flowing across their business so they can quickly leverage that data to power endless use cases.”

Stream Governance Advanced: Point-in-Time Lineage Insights, Sophisticated Data Cataloging, and Global Quality Controls

Data streaming use cases are rapidly growing as real-time data powers more of the business. This has caused a proliferation of data that holds endless business value if teams are able to confidently share it across the organization. Building on the suite of features initially introduced with Stream Governance Essentials, Stream Governance Advanced delivers more ways to easily discover, understand, and trust data in motion. With scalable quality controls in place, organizations can democratize access to data streams across teams while achieving always-on data integrity and regulatory compliance.

New capabilities include:

Point-in-time playbacks for Stream Lineage: Troubleshooting complex data streams is now faster and easier with the ability to understand where, when, and how data streams have changed over time. Point-in-time lineage provides a look back into a data stream’s history over a 24-hour period or within any one-hour window over a seven-day range. Teams can now see what happened on, for example, Thursday at 5pm, when support tickets started coming in. Paired with the new ability to search across lineage graphs for specific objects such as client IDs or topics, point-in-time lineage makes it easier to identify and resolve issues in order to keep mission-critical services up for customers and new projects on track for deployment.
Business metadata for Stream Catalog: Improve data discovery with the ability to build more contextual, detail-rich catalogs of data streams. Alongside previously available tagging of objects, business metadata gives individual users the ability to add custom, open-form details represented as key-value pairs to entities they create such as topics. These details, from users who know the platform best, are critical to enabling self-service access to data for the larger organization. While tagging has allowed users to flag a topic as “sensitive,” business metadata allows that user to add more context, such as which team owns the topic, how it is being used, who to contact with questions about the data, or any other details necessary.

Exploring the catalog is now even easier with GraphQL API, giving users a simple, declarative method to specify and get the exact data they need while enabling a better understanding of data relationships on the platform.

Globally available Schema Registry for Stream Quality: By more than doubling the global availability of Schema Registry to 28 regions, teams have more flexibility to manage schemas directly alongside their Kafka clusters in order to maintain strict compliance requirements and data sovereignty. Additionally, Schema Registry is even more resilient with an increased 99.95% uptime SLA, giving businesses the confidence they need that quality controls for Kafka will always be in place as more groups start working with the technology.

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