Systems | Development | Analytics | API | Testing

A new Forrester Total Economic Impact (TEI) study commissioned by Confluent, an IBM company and data streaming platform, found that a representative composite organization achieved 137% ROI, USD 2.1 million in net present value and a 10-month payback period. Those are meaningful results. The ROI is notable. What's more interesting is where the value came from. Modernization is often framed as an infrastructure problem. Leaders compare platform costs, migration and operating costs.

Trust is foundational to how Confluent designs, operates, and governs its products. Confluent's Trust Principles shape everything from how Confluent Cloud is engineered to how it is monitored and audited. Each principle is backed by specific, independently verified controls.

Every organization that adopts a cloud platform takes on a shared responsibility: the assurance that its vendors are secure, compliant, and resilient. This is where third-party risk assessments (TPRAs) come in with a systematic evaluation of a vendor's security posture, operational resilience, and compliance standards. TPRAs are a cornerstone of modern security and procurement programs because they provide structured, verifiable evidence of a vendor's security posture.

Modern Security Operations Centers (SOCs) are transitioning towards agentic architectures. Traditionally, SOCs relied on rigid, rule-based automation and high human effort. We built an Agentic SOC powered by AI agents that don't just follow rigid, hardcoded rules, but also autonomously investigate, reason, and act like human analysts.

Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.

A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.

In the last few months, Confluent has released over 70 new features to our data streaming platform, ranging from new connectors to better networking connectivity to entirely new ways to build streaming applications. We are not slowing down. Confluent’s DSP can do more, across every pillar, in ways that are more powerful, more complete, and more connected than ever before.

Platform teams want to maximize the value of data in motion, but separate workstreams for stream processing, monitoring, KRaft migrations, and data governance create friction as environments grow. As a result, teams spend more time managing operational overhead and less time building. Today, we’re excited to announce Confluent Platform (CP) 8.3.0, built on Apache Kafka 4.3.0, reinforcing our core capabilities as a data streaming platform.

Data streaming has moved from a niche pattern used by a handful of internet-scale companies to the default backbone for event-driven architectures, real-time analytics, and now AI pipelines. What started as log aggregation at LinkedIn has become the plumbing for fraud detection, IoT telemetry, microservices communication, and retrieval-augmented generation. Three names dominate that conversation today.