Systems | Development | Analytics | API | Testing

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.

Summarize with AI: ChatGPT Claude Google AI Mode Grok Perplexity In today’s data-driven world, business intelligence and analytics play a huge role in better understanding your customers, improving your operations, and making actionable business decisions. While there’s no doubt about the value of implementing a BI solution, many ERP users face the same challenges around the quality and credibility of their data.

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.

Enterprise data storage comprises a patchwork of systems: ERP databases, CRM platforms, spreadsheets, cloud apps, and legacy files. These systems do their own jobs well individually, but collectively they create a fragmented landscape. For anyone tasked with building a migration, an integration, or even a simple report, the first challenge is not moving data. It’s understanding what exists and how it all connects.

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Key Takeaways Enterprise data teams spend millions on warehouse infrastructure while still designing schemas the way they did in 1995—one entity at a time, one relationship at a time, hoping the model survives its first encounter with production data. The irony runs deep: organizations racing to deploy real-time analytics are bottlenecked by modeling processes that take six to eight weeks before a single pipeline runs. Data warehouses succeed or fail on design.

In Europe, trust is everything, and the bar is set by law. GDPR, the AI Act, NIS2, DORA, and the Data Act shape how data and AI must operate. Leaders need to show where data lives, who can touch it, and how it moves, and they want cloud speed and flexibility without giving up control, so sovereignty and transparency must be built in from day one.