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You're three hours into debugging a stalled Kafka consumer. The lag is climbing. Customers are complaining. Your logging doesn't show anything useful, and changing the log level requires a deployment approval that won't come until tomorrow morning. Sound familiar? If you're operating Apache Kafka at scale, that sinking feeling when a consumer group stops progressing, and you're left playing detective with insufficient clues.

Devoxx Poland 2025 felt different. Not because of revolutionary new frameworks or another "this changes everything" moment, but because of what didn't happen. The conference had an unusual dose of pragmatism, skepticism, and – dare we say it – common sense. Maybe it's because developers are asking the right questions: "Does this solve a problem?" and "What happens when this inevitably breaks?" Here's what emerged from the sessions we watched, and the people we spoke to.

Let's talk about a problem that might sound simple but gets complex quickly: copying data from one Kafka cluster to another. As our Kafka usage grows, many of us find ourselves managing multiple clusters and needing to share data between them. Or worst still, sharing data to an external cluster. During a London meetup, we explored why this happens, what existing solutions offer, and why we decided to build our own Kafka replicator. Here's what we learned.

Current 2025 featured two days of engineers figuring out how streaming tech needs to evolve in an AI-driven world. Gone are the days when talks focused on basic Kafka setup. This year, everyone was tackling complex integrations, developer happiness, and practical AI implementation. Still, the event drew a range of people, with plenty of new faces stopping by the Lenses.io booth – clear evidence that Kafka and data streaming continue to attract newcomers.

Lenses 6.0 (code-named Panoptes after the Greek mythical character with many eyes) is generally available. It brings a multi-Kafka developer experience and complete autonomy to streaming data.

You didn’t become a developer to spend hours hunting down missing messages, or debugging consumer issues. Yet here we are. Valuable dev time evaporates as you wrestle with Apache Kafka, or wait for a central team to unblock you, when you should be finding, prepping, and shipping streaming data in minutes. Lenses Community Edition tackles these everyday frustrations.

New York City, US - February 12, 2025 - Lenses.io, a data streaming innovation leader whose software helps developers power the world’s largest businesses, today announces the development of an enterprise grade and vendor-agnostic Kafka-to-Kafka replicator. It will enable organizations to share streaming data across different domains, in order to keep up with real-time data demands as AI adoption grows.

As organizations adopt specialized streaming technologies, real-time data is increasingly distributed across multiple domains, clouds, and locations. With the rapid evolution of streaming-dependent technologies, companies need the ability to pivot between providers almost instantly to spread risk and maintain performance (just look at the January DeepSeek-R1 LLM news).

Buckle up, we’re past the AI hype. Now, it’s about making intelligent systems that act on our behalf. In 2025, AI isn’t just a tool– it’s becoming our core way of operating, powered by real-time data. How we stream, manage and monetize that data will define the next generation of business. Here, we zoom into four examples of what autonomous real-time intelligence could look like in the coming year.

The need to democratize and share data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating real-time data as a product, and adopting Data Mesh practices, is the way forward. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.