Systems | Development | Analytics | API | Testing

How to Build a Custom Kafka Connector - A Comprehensive Guide

In today’s data-driven world, seamless data integration is crucial to ensuring the smooth operation of modern systems. With the growing complexity of distributed data platforms, businesses and developers are seeking efficient ways to move, process, and transform data. Apache Kafka has become the de facto standard for real-time data streaming, and Kafka Connect plays a key role in facilitating the integration of Kafka with various data sources and sinks.

Confluent Connect: FY'25 Launch Highlights - Unlocking Data & Powering AI Pipelines

Dive into the biggest breakthroughs for the Confluent Connect ecosystem in 2025! This year, we made moving data easier than ever, from modernizing legacy systems with the Oracle XStream CDC Premium Connector to empowering developers with Custom SMTs and Custom Connectors on Google Cloud. Discover the over 10 new connectors we launched, including Snowflake Source, Azure Cosmos DB v2, and Neo4j Sink, plus the release of Confluent Hub 2.0. Learn how Confluent Cloud connectors are breaking down silos and building bridges for your next-gen AI and data modernization projects.

Why Managing Your Apache Kafka Schemas Is Costing You More Than You Think

For developers building event-driven systems, schemas are essential for using schemas to define data contracts between producers and consumers in Apache Kafka, ensuring every message can be correctly interpreted. But when schema management is handled manually or through do-it-yourself (DIY) solutions, organizations face escalating expenses that compound as their deployments scale.

Load Testing Kafka #speedscale #kafka #loadtesting

Message brokers are a critical component of modern distributed systems, facilitating asynchronous communication between services. Load testing message broker integrations requires special considerations since the interaction patterns differ from traditional HTTP-based APIs. Speedscale provides specialized tooling to help you load test applications that integrate with message brokers by.

Apache Kafka Monitoring Is Costing You More Than You Think

For organizations that rely on Apache Kafka, monitoring capabilities aren’t just a "nice-to-have"—it's a fundamental requirement for reliable performance in production and business continuity. However, the true cost of monitoring Kafka is often misunderstood. It’s not a single line item on a bill but a collection of hidden expenses that silently drain your engineering budget and inflate your total cost of ownership (TCO).

2026 Data & AI Predictions: What Trends Will Shape the Future?

We recently released our 2026 Confluent Predictions Report, outlining bold ideas and trends that are shaping the future of data, AI, and real-time systems. And stay tuned for an upcoming episode of the Life Is But a Stream web show that will air early in the new year. Join the conversation as host Joseph Morais sits down with Sanjeev Mohan, independent analyst at SanjMo, for an exciting roundtable discussion breaking down those predictions. Are they forecasts? Are they trends? And which ones will matter most as we move forward into 2026?

The $11 Billion Question: What the acquisition of Confluent by IBM means

What’s remarkable is how long Confluent competed at the highest level. Creating a category and type of application is hard; transitioning to cloud and surviving against hyper scalers is even harder. That alone is a huge achievement. Some see this as a pressured exit. But another way to look at it is as a strategic purchase by IBM to strengthen its position in enterprise data movement and integration.

Inside Life Is But A Stream: A Year in Review (Real-Time Data and AI) | Confluent Podcast

Technology leaders share how they process streaming events end-to-end in sub-second latency, secure sensitive data through immutable and governed pipelines, power secured real-time systems in regulated environments, and enable multi-agent AI systems built on fresh, continuously streaming data.