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Kafka

4 data streaming trends for 2025

Buckle up, we’re past the AI hype. Now, it’s about making intelligent systems that act on our behalf. In 2025, AI isn’t just a tool– it’s becoming our core way of operating, powered by real-time data. How we stream, manage and monetize that data will define the next generation of business. Here, we zoom into four examples of what autonomous real-time intelligence could look like in the coming year.

Queues in Apache Kafka: Enhancing Message Processing and Scalability

In the world of data processing and messaging systems, terms like "queue" and "streaming" often pop up. While they might sound similar, they serve different purposes, and can significantly impact how your system handles data. Let’s break down the differences in a straightforward way.

Introducing Confluent's JavaScript Client for Apache Kafka

From humble beginnings, Apache Kafka steadily rose to prominence and now sits as the backbone of data streaming for thousands of organizations worldwide. From its robust API, cloud-native implementations like Confluent Cloud, and synergy with other technologies like Apache Flink, Kafka has grown to cover many use cases across a broad range of industries.

Are You Misconfiguring Producer Retries? | Kafka Developer Mistakes

Producer retries in Apache Kafka can make or break message delivery, especially during broker events like updates or failures. Use the idempotent producer, and configure delivery timeouts, in order to avoid common pitfalls that lead to lost messages or broken ordering.

Are You Using the Wrong Partition Key? | Kafka Developer Mistakes

Picking the wrong partition key in Apache Kafka? That’s a fast track to performance headaches—think unbalanced loads, slowdowns, and broken message ordering. Choosing the right partitioning strategy keeps your data flowing smoothly and avoids hot partitions.

Why Short-Lived Connections Are Killing Your Performance! | Kafka Developer Mistakes

Constantly starting and stopping Apache Kafka producers and consumers? That’s a recipe for high resource usage and inefficiency. Short-lived connections are heavy on resources, and can slow down your whole cluster. Keep them running to boost performance, cut latency, and get the most out of your Kafka setup.

Why Relying on Default Settings Can Cost You! | Kafka Developer Mistakes

Default settings in Apache Kafka work when you’re getting started, but aren't suited for production. Sticking with defaults, like a seven-day retention policy, or a replication factor of one, can cause storage issues, or data loss in case of failure. Learn why optimizing retention periods, replication factors, and partitions, is crucial for better Kafka performance and reliability.