Systems | Development | Analytics | API | Testing

Migrating from self-managed Apache Kafka connectors to fully managed connectors has been a persistent challenge for data teams working on Confluent Cloud. While Confluent-managed connectors deliver enterprise-grade features, seamless upgrades, and comprehensive support that add up to significant development and operations cost savings, the journey to get there often feels daunting and opaque.

I’m a Data Streaming Engineer and a developer advocate, which means I spend a lot of time thinking about the day-to-day experience of building applications with data streaming and stream processing. I muse about a world of data in motion where entire organizations have the governance needed to manage, discover, and understand the complex relationships between data streams.

In the evolving landscape of agentic systems, Confluent and Google Cloud together emerge as critical enablers, providing the real-time infrastructure that underpins efficient, reliable, and intelligent data flow. This powerful synergy addresses key challenges in agent-to-agent (A2A) communication, interaction with external resources, and the overall stability and observability of complex multi-agent environments.

In our previous blog post, we introduced two ways that Confluent Cloud can integrate with AWS Lambda. One option is using Lambda’s Event Source Mapping (ESM) for Apache Kafka, wherein Lambda creates a consumer group, consumes records off the provided topic, and triggers the Lambda function. The record is polled by the ESM, and the consumed record subsequently acts as the event data provided to (and processed by) the Lambda function.

Modern businesses don’t run in just one place. Your applications might live in Amazon Web Services (AWS), your analytics in Microsoft Azure, and critical systems on-premises. The challenge? Keeping all that data connected and flowing in real time—without adding complexity or risk. As more organizations adopt these multicloud strategies, the need for secure, private data replication has become critical.

It’s 3 a.m., and an alert fires: Your critical Kafka Streams application is lagging. The frantic troubleshooting begins. Is it a consumer group rebalance? You start searching through application logs across multiple pods. Is it a problem with the Apache Kafka cluster itself? You switch to your cluster monitoring dashboards to check broker health. Or is there a silent bottleneck hidden deep in your application code? Without the right instrumentation, you're flying blind.