Systems | Development | Analytics | API | Testing

Shifting Left: How Data Contracts Underpin People, Processes, and Technology

The divide between operational and analytical systems has long resulted in data inconsistencies, unreliability, and redundancies. Without a single, unified source of truth, teams interpret information in their own ways—often after the fact. This can lead to downstream data discrepancies, issues, and distrust. Meanwhile, changes to upstream data structures create ripple effects, breaking downstream systems and requiring manual intervention to fix issues.

Ep 5 - The Secret to Data Streaming Success: Speaking the Same Language

Want your real-time data streaming initiative to stick? Success hinges on more than pipelines—it’s about people, governance, and business impacts. Jeffrey Johnathon Jennings (J3), managing principal at signalRoom, shares how to bring it all together. In this episode, J3 shares how he’s used impactful proofs of concepts to demonstrate value early, then scaled effectively through shift left with governance and stronger cross-team collaboration.

How the Singapore Government is Building Agility to Enhance Citizen Services with IMDA's Tech Acceleration Lab and the Government Commercial Cloud+

Supply chain disruption. Pandemics and medical crises. Climate change. Events that were once “black swans” are now near-everyday occurrences for citizens worldwide—and governments are facing growing pressure to provide effective public services in response. In the past, the ability to access, process, and apply data to make informed decisions about complex issues was crucial to creating effective public services.

Optimizing Serverless Stream Processing with Confluent Freight Clusters and AWS Lambda

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

Powering AI Agents with Real-Time Data Using Anthropic's MCP and Confluent

Model Context Protocol (MCP), introduced by Anthropic, is a new standard that simplifies artificial intelligence (AI) integrations by providing a secure, consistent way to connect AI agents with external tools and data sources. When we saw MCP’s potential, we immediately started exploring how we could bring real-time data streaming into the mix. With our long history of supporting open source and open standards, building an MCP server was a natural fit.

New with Confluent Platform 7.9: Oracle XStream CDC Connector, Client-Side Field Level Encryption (EA), Confluent for VS Code, and More

At Confluent, we’re committed to building the world's leading data streaming platform, which gives you the ability to stream, connect, process, and govern all of your data, and make it available wherever it’s needed—however it’s needed—in real time. Today, we're excited to announce the release of Confluent Platform 7.9! This release builds upon Apache Kafka 3.9, reinforcing our core capabilities as a data streaming platform.