Systems | Development | Analytics | API | Testing

Confluent recently released its 2025 Data Streaming Report: Moving the Needle on AI Adoption, Speed to Market, and ROI. The report found that data streaming is delivering business value with 44% of IT leaders, driving up to 5x or more return on their data streaming investments. Explore the 2025 Data Streaming Report That said, as companies continue to expand their data streaming use cases, many struggle with nontechnical hurdles around scaling, setting up operations, and hitting organizational silos.

The advancement and widespread availability of new artificial intelligence (AI) capabilities—through platforms like the Databricks Data Intelligence Platform and Mosaic AI—has completely reset expectations for engineering teams across every industry. Business now moves at a new pace, demanding rapid delivery of intelligent, real-time applications—instead of slowly stitched-together systems solving problems defined and scoped months prior.

In the age of AI, the hunger for fresh, reliable data to power machine learning (ML) models and real-time analytics is insatiable. Yet, organizations frequently hit roadblocks when trying to bridge their operational data in motion, typically flowing through Apache Kafka, with their data at rest in data lakehouses. On one side, you have the data streaming platform, the central nervous system managing the real-time flow of business events.

Users of Snowflake and other data lakes and data warehouses need real-time data for artificial intelligence (AI) and analytical workloads—but they struggle to get that data into their lakes and warehouses. In response to this ubiquitous challenge, Confluent developed Tableflow.

The consumer group is a cornerstone of Apache Kafka, enabling scalable and fault-tolerant data consumption by allowing multiple consumer instances to share the workload of reading from topic partitions. The consumer rebalance protocol is the mechanism that coordinates which partitions are assigned to which consumers within a group.

Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.

Your teams want the immediate insights of stream processing with the scale and historical context of batch processing—but traditional data infrastructure forces you to resort to disparate tooling or manual workarounds to bridge that gap. This quarter’s release, coming to you live from Current London, brings new features in Confluent Cloud that fundamentally change this dynamic by seamlessly unifying stream and batch processing.

We’re excited to announce the General Availability (GA) of the Confluent fully managed V2 connector for Apache Kafka for Azure Cosmos DB! This release marks a major milestone in our mission to simplify real-time data streaming from and to Azure Cosmos DB using Apache Kafka. The V2 connector is now production-ready and available directly from the Confluent Cloud connector catalog.