Systems | Development | Analytics | API | Testing

As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact—smarter automation, better customer experiences, and massive cost takeout—AI use cases are only as powerful as the data they’re running on.

Business intelligence (BI) dashboards contain a wealth of information that companies need to succeed in competitive environments. Marketing and sales departments might rely on pipeline progression dashboards while product managers and engineering teams use them to track product usage and performance KPIs.

Security standards continue to evolve, and organizations need infrastructure that evolves along with them. TLS 1.3 brings stronger encryption to streaming infrastructure for data in transit while ensuring alignment with strict security and compliance requirements. We’re excited to announce that TLS 1.3 is currently available as an opt-in feature on Confluent Cloud Dedicated clusters and all other Confluent Cloud APIs, including Stream Governance, Metrics, and Control Plane.

Building real-time applications in the cloud means making the right infrastructure choices early. And for many engineering teams leveraging Amazon Web Services (AWS), Apache Kafka is a key component of their streaming architectures. While open source Kafka provides a powerful foundation, operating it at scale in production often means navigating challenges when provisioning, scaling, securing, and integrating across environments. That’s where Confluent Cloud comes in.

We are thrilled to announce that Confluent Cloud for Government is now available on the FedRAMP Marketplace with FedRAMP 20x Low authorization, allowing both private and public sector customers to leverage our cloud-native data streaming platform to power their mission-critical applications. This milestone marks the successful completion of Phase One of our FedRAMP 20x Pilot program.

In today’s cost-conscious climate, every line item on your cloud bill is under the microscope. That makes now the ideal time to rethink your data streaming strategy. For many teams, using a hyperscaler-hosted Apache Kafka service feels like the easy choice—one vendor, one bill, no additional contracts, and minimal setup. It makes sense early on, especially for small-scale projects or basic use cases. But, as your Kafka usage grows and becomes mission-critical, that simplicity comes at a cost.

The Apache Kafka community is proud to announce the release of Apache Kafka 4.1.0. This blog post highlights the many new features and improvements included in this release. For a full list of changes, be sure to check the release notes. Queues for Kafka (KIP-932) is now in preview. It's still not ready for production, but you can start evaluating and testing it. See the preview release notes for more details. This release also introduces a new Streams Rebalance Protocol (KIP-1071) in early access.

Based in Delhi, India, Vineet Singh has worked as a Senior Software Engineer at Confluent for the past three years, and he has contributed to various parts of Confluent’s core data streaming engine. Now he’s part of the team that makes Apache Kafka cloud-native, serverless, and able to power robust and scalable solutions for Confluent Cloud customers. Learn more about Vineet’s experience and growth at Confluent and how his team and environment have set him up for success.

Data streaming equips modern organizations to rapidly ingest and understand new information and use it to solve real-world problems at scale. For some of these real-time insights—critical operational cues that demand a timely response—delivering that information directly to your team’s inbox is the best way to act on it.