At Confluent, we’re committed to building the world's leading data streaming platform that gives you the ability to stream, connect, process, and govern all your data, and makes it available wherever it’s needed, however it’s needed, in real time. Today, we're excited to announce the release of Confluent Platform 7.8. This release builds upon Apache Kafka 3.8, reinforcing our core capabilities as a data streaming platform.
At Confluent, we continuously strive to showcase the power of our data streaming platform through real-world applications, exemplified by our Customer Zero initiative. In part 1 of this blog, we present the latest use case of Customer Zero that harnesses the capabilities of generative AI, data streaming, and real-time predictions to enhance lead scoring for sales, helping our team prioritize high-value prospects and address complex challenges within our organization.
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.
Earlier this year, we unveiled our vision for Tableflow to feed Apache Kafka streaming data into data lakes, warehouses, or analytical engines with a single click. Since then, many customers have been exploring, experimenting with, and providing valuable feedback on Tableflow Early Access. Our teams have worked tirelessly to incorporate this feedback and are excited to bring Tableflow Open Preview to you in the near future.
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.
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.
Querying databases comes with costs—wall clock time, CPU usage, memory consumption, and potentially actual dollars. As your application scales, optimizing these costs becomes crucial. Materialized views offer a powerful solution by creating a pre-computed, optimized data representation. Imagine a retail scenario with separate customer and product tables. Typically, retrieving product details for a customer's purchase requires cross-referencing both tables.
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.