Easily Represent Kafka Topics as Apache Iceberg® or Delta Lake Tables

“Access to real-time data is critical for customers to make fast and accurate decisions. With Confluent’s Tableflow now available on Microsoft Azure, customers can stream Kafka events to OneLake as Apache Iceberg or Delta Lake tables and query them instantly via Microsoft Fabric and popular 3rd party engines using OneLake Table APIs, cutting complexity and speeding up decisions.”

“Access to real-time, trustworthy data is a key factor in scaling AI initiatives faster, and a crucial requirement for analytical platforms. With Tableflow, users can seamlessly represent Kafka topics and their associated schemas as Apache Iceberg™ tables in just a few clicks to deliver the AI-powered insights that today’s businesses need. Through a native integration with Snowflake Open Catalog, a managed service for Apache Polaris™(incubating), our joint customers can access real-time operational data for AI, analytics, and collaboration easier, and with more unified governance, than ever before. We are thrilled to help customers bridge the gap between operational and analytical estates with a solution that leverages open standards."

“Beyond the initial product credits, access to subject matter experts through office hours helped us on multiple occasions to confidently adopt more of the Confluent product suite, which has allowed us to be more nimble while also providing value to our customers more quickly.”

'At Attune, delivering real-time insights from smart building IoT data is central to our mission. With just a few clicks, Confluent Tableflow lets us materialize key Kafka topics into trusted, analytics-ready tables. This gives our business accurate visibility into customer engagement, device behavior, and per-tenant platform utilization—directly from the live data streams. The Confluent Data Streaming Platform also helps us quickly identify and resolve data quality issues like duplicate sensor events, saving time and improving efficiency. Tableflow has simplified our data architecture while opening new possibilities for how we leverage data."

"Apache Flink is becoming a prominent stream processing framework in this shift towards real-time insights. Flink and Apache Kafka are commonly used together for real-time data processing, but differing data formats and inconsistent schemas can cause integration challenges and hinder the quality of streaming data for downstream systems and consumers. A fully managed, unified Kafka and Flink platform with integrated monitoring, security, and governance capabilities can provide organizations with a seamless and efficient way to ensure high-quality and consistent data streams to fuel real-time applications and use cases, while reducing operational burdens and costs."