Systems | Development | Analytics | API | Testing

As we head into the RSA Conference this year, the conversation on the show floor is going to be different. Yes, artificial intelligence (AI) will be everywhere. But if you listen closely to the C-suite discussions happening behind closed doors, the real buzz isn't just about the newest detection algorithm. It’s about data gravity and the unprecedented data explosion driven by AI-fueled bad actors.

While winning in artificial intelligence (AI) is critical to the future of business, old-school analytics—visualizations, dashboards, and infrequent reports—are still core to an organization's data needs. Behind the scenes, this analytics ecosystem remains heavily hydrated by batch-based ELT data integration. For a long time, this made perfect sense, as data sources were fewer, data volumes were manageable, and analytics consumers were limited.

The most effective way to adopt streaming machine learning (ML) is not by rebuilding your entire platform but by adding a single, high-value inference step to your existing data flow. This incremental approach allows you to transition from batch-based processing to real-time decision-making without the risk of a "big bang" migration, ensuring that your microservices architecture remains agile and responsive. What Is Streaming ML? ML in streaming is the practice of.

Apache Kafka powers massive, mission-critical data streams at enterprises worldwide. But in many organizations, those streams still behave like dumb pipes: raw JSON or bytes flowing between services, limited governance, weak contracts between teams, and data that’s hard to reuse for analytics or artificial intelligence (AI).

Federal agencies must perform a high-stakes balancing act: Modernize legacy systems, break down data silos, and deliver real-time citizen services—all while operating under strict security and compliance requirements with constrained budgets and staff. Today, we're announcing that Confluent Cloud for Government (CCG) is now available on the FedRAMP Marketplace, with FedRAMP Moderate authorization achieved through the competitive FedRAMP 20x Pilot program.

Data infrastructure growth has a direct, measurable relationship with energy consumption. As organizations ingest more events, retain more data, and deploy more always-on services, infrastructure energy use increases—often faster than business value. For streaming systems, this effect can be amplified by long-running clusters, peak-based sizing, and duplicated pipelines. Sustainability in this context is not about environmental reporting or corporate commitments.

At Confluent, our mission is to provide the world’s most secure and scalable data streaming platform. So we’re aware and planning for a future where the threat of a large-scale, cryptographically relevant quantum computer is able to break the public key cryptographic algorithms in use today. In fact, the Quantum-Safe Working Group of the Cloud Security Alliance set April 14, 2030, as the deadline by which companies should have their post-quantum infrastructure in place.

Queues for Kafka is now in General Availability (GA) on Confluent Cloud and is coming soon to Confluent Platform, coinciding with the Apache Kafka 4.2 release. This milestone brings production-ready queue semantics and elastic consumer scaling natively to Kafka through KIP-932, enabling organizations to consolidate their messaging infrastructures while gaining elastic consumer scaling and per-message processing controls. Get started.

As data architectures evolve, we are seeing a fundamental shift from systems designed to report on the past to systems designed to influence the future. At the heart of this shift are two critical, interconnected concepts: As organizations pursue more data-driven decision making, the gap between insight and action has become a competitive constraint. Together, real-time decisioning and autonomous data systems represent the evolution of real-time data systems—where insight flows directly into action.