Confluent Blog

This post introduces the VISTA Framework, a structured approach to prioritizing AI opportunities. Inspired by project management models such as RICE (Reach, Impact, Confidence, and Effort), VISTA focuses on four dimensions: Business Value, Implementation Speed, Scalability, and Tolerance for Risk

In this blog, Confluent's Chief Product Officer, Shaun Clowes, explores strategies to foster effective async collaboration—reduce burnout, boost productivity, and make distributed work actually work.

For AI agents to transform enterprises with autonomous problem-solving, adaptive workflows, and scalability, they need event-driven architecture (EDA) powered by streaming data.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team.

By combining Google A2A’s structured protocol with Kafka’s powerful event streaming capabilities, we can shift from brittle, point-to-point integrations to a dynamic ecosystem where agents publish insights, subscribe to context, and coordinate in real time. Observability, auditability, and...

This blog post demonstrates using Tableflow to easily transform Kafka topics into queryable Iceberg tables. It uses UK Environment Agency sensor data as a data source, and shows how to use Tableflow with standard SQL to explore and understand the data.

Learn how Confluent champion Tejal Bhatt helps customers on their data streaming journey as part of the Dedicated Solutions Engineering team.

Building multi-agent systems at scale requires something most AI platforms overlook: real-time, observable, fault-tolerant communication, and governance. That's why we build on Confluent data streaming platform…

The efficient management of exponentially growing data is achieved with a multipronged approach based around left-shifted (early-in-the-pipeline) governance and stream processing.

The guide covers Kafka consumer offsets, the challenges with manual control, and the improvements introduced by KIP-1094. Key enhancements include tracking the next offset and leader epoch accurately. This ensures consistent data processing, better reliability, and performance.

Learn how data streaming unlocks shift-left data integration—a key enabler for adopting and building next-generation technologies like generative AI— for the government agency.

An Apache Flink® job not producing results often indicates an issue with watermarks, which are necessary for handling out-of-order message processing in time-based aggregations from Kafka. Watermarks balance data loss and latency by defining how long to wait for late messages. Incorrectly setting...

Discover how Confluent enables secure inter-organizational data sharing to maximize data value, strengthen partnerships, and meet regulatory requirements in real time.

This blog explores how to integrate Confluent Tableflow with Trino and use Jupyter Notebooks to query Apache Iceberg tables. Learn how to set up Kafka topics, enable Tableflow, run Trino with Docker, connect via the REST catalog, and visualize data using Pandas. Unlock real-time and historical an...