In the 2.0 release, Flink introduces several innovative features that address key challenges in real-time data processing and align with the growing demands of modern applications, including AI-driven workflows.
• The Disaggregated State Management architecture enables more efficient resource utilization in cloud-native environments, ensuring high-performance real-time processing while minimizing resource overhead.
• The introduction and refinement of Materialized Tables empower users to focus on business logic without needing to understand the complexities of stream processing or the differences between stream and batch execution modes, simplifying development and enhances productivity for users across various domains. Optimizations in Batch Execution mode provide a cost-effective alternative for scenarios where near-real-time or non-real-time processing is sufficient, expanding Flink’s versatility for diverse use cases.
• Additionally, the deep integration with Apache Paimon strengthens the Streaming Lakehouse architecture, making Flink a leading solution for real-time data lake use cases.
• As AI and LLMs continue to gain prominence, the demand for scalable, real-time data processing solutions grows. Flink 2.0’s advancements in performance, resource efficiency, and ease of use position it as a strong foundation for AI workflows, ensuring that Flink remains at the forefront of real-time data processing innovations.

6 billion rows synced per month
Data from 2,500 cloud accounts (1,900 AWS accounts + 600 Azure subnoscriptions)
Configuration from 800+ Kubernetes clusters managing around 400,000 pods
6-7 million rows of real-time data at any given moment
4 TB of new data ingested monthly