Highlights:
- The latest updates to Confluent Cloud for Apache Flink aim to simplify and accelerate the development of real-time AI applications.
- Along with Flink updates, Confluent announced Tableflow enhancements for real-time data access in analytics and AI.
A leading provider of streaming data software, Confluent Inc., has unveiled new AI and analytics features aimed at streamlining real-time data processing and enhancing enterprise intelligence.
The updates include enhancements to Confluent Cloud for Apache Flink, introducing seamless AI model inference, vector search, and built-in machine learning capabilities. Additionally, improvements to Tableflow enhance access to operational data in real-time analytics environments, with expanded support for Apache Iceberg and Delta Lake.
The latest enhancements to Confluent Cloud for Apache Flink aim to simplify the development of real-time AI applications. With the addition of Flink Native Inference, teams can now run any open-source AI model directly in Confluent Cloud, eliminating workflow complexities.
A new Flink search feature consolidates data access across multiple vector databases, simplifying discovery and retrieval through a unified interface. Additionally, built-in machine learning functions integrate AI-driven capabilities, such as forecasting and anomaly detection, directly into Flink SQL.
Confluent presents these innovations as transforming how businesses leverage AI for real-time customer engagement and decision-making.
“Building real-time AI applications has been too complex for too long, requiring a maze of tools and deep expertise just to get started. With the latest advancements in Confluent Cloud for Apache Flink, we’re breaking down those barriers — bringing AI-powered streaming intelligence within reach of any team,” says Shaun Clowes, Confluent’s Chief Product Officer.
Tableflow
In addition to the new features in Confluent Cloud for Apache Flink, Confluent introduced enhancements to Tableflow, its data integration solution that provides real-time access to operational data in open table formats for advanced analytics and AI applications.
The latest update makes Apache Iceberg support generally available, enabling teams to seamlessly convert Apache Kafka topics into Iceberg tables for integration with data warehouses and analytics engines. By removing the need for complex preprocessing, the service minimizes operational overhead and provides a reliable, real-time source of truth for AI-driven applications.
Confluent introduced an early access program for Delta Lake, extending Tableflow’s compatibility with Databricks’ open-format storage layer. This integration enhances AI workflows and accelerates decision-making by ensuring a unified view of real-time data across both operational and analytical applications.
Other improvements include support for bring-your-own-storage options and deeper integrations with catalog providers such as AWS Glue and Snowflake Open Catalog, offering enterprises enhanced flexibility and governance over their data.
“With Tableflow, we’re bringing our expertise of connecting operational data to the analytical world. Now, data scientists and data engineers have access to a single, real-time source of truth across the enterprise, making it possible to build and scale the next generation of AI-driven applications,” said Clowes.
