Event Store Rebrands as Kurrent, Launches $12M Funding Round and Kurrent Enterprise Edition
Overview
Kurrent, a startup that aims to unify streaming data systems and databases, has announced a name change from Event Store to better reflect its goals. The company has also secured $12 million in funding and launched its Kurrent Enterprise Edition.
The Problem
The gap between systems of record and streaming data systems has long been a source of pain and additional work for developers. This pain stems from the use of state-based data models via databases like PostgreSQL, versus event-driven streaming data systems, such as Apache Kafka. Database-centric organizations often resort to invasive solutions, such as using change data capture (CDC) tools or fast in-memory databases, which come with limitations. Similarly, companies coming from the "streaming-first" side of the equation may install additional data stores, such as Kafka’s KStore, but this adds data-integration complexity and additional points of failure.
Kurrent’s Solution
Kurrent, formerly Event Store, has developed a novel approach centered around its native event format. The company claims that its data platform combines the immutable storage of a database with the real-time capabilities of a streaming system, allowing application developers to give fresh, streaming data the historical context needed to deliver business insights in real-time.
How it Works
According to Kurrent CEO Kirk Dunn, the platform originates and aggregates data from multiple sources, curates it, and preserves its integrity in an immutable, globally ordered log. The platform then streams fine-grained event data directly to the points of need, eliminating the trade-offs between static storage and dynamic streaming.
Benefits
By storing data in a globally ordered, immutable log, Kurrent preserves both state and context throughout the lifecycle of the data. This event-native approach enables downstream systems to construct meaningful insights and tell complete stories, providing high-context information for both real-time and historical use cases.
Use Cases
Kurrent’s system can replace the need for both traditional databases and data streaming systems, although it can also work with either type of system if a company has already built applications around them. The company envisions its platform being used in various scenarios, including bolstering existing Kafka implementations, building new real-time applications, and implementing generative AI and agentic AI systems.
Funding and Partnerships
Crane Venture Partners, the venture capital firm that led the $12 million funding round, believes that Kurrent’s event-native approach will unlock new capabilities necessary for overcoming the long-standing difficulty in integrating state- and event-based systems. Krishna Visvanathan, co-founder and partner at Crane Venture Partners, says, "Kurrent solves this challenge by delivering context-rich data events, giving businesses the complete picture they need to understand and act on their opportunities."
Conclusion
Kurrent’s rebranding and funding announcement mark a significant milestone in the company’s journey to unify streaming data systems and databases. With its event-native approach, Kurrent aims to fill a critical gap in the modern data stack, solving a pervasive problem and positioning the company at the forefront of the enterprise AI revolution.
FAQs
Q: What is Kurrent’s event-native approach?
A: Kurrent’s event-native approach combines the immutable storage of a database with the real-time capabilities of a streaming system, allowing for the preservation of both state and context throughout the lifecycle of the data.
Q: What are the benefits of Kurrent’s approach?
A: Kurrent’s event-native approach enables downstream systems to construct meaningful insights and tell complete stories, providing high-context information for both real-time and historical use cases.
Q: What are the use cases for Kurrent’s system?
A: Kurrent’s system can replace the need for both traditional databases and data streaming systems, although it can also work with either type of system if a company has already built applications around them. The company envisions its platform being used in various scenarios, including bolstering existing Kafka implementations, building new real-time applications, and implementing generative AI and agentic AI systems.
