This article discusses Grab's approach to optimizing CPU provisioning for Flink applications using machine learning. It highlights the limitations of reactive autoscaling and proposes a predictive model that forecasts workload demands to improve resource allocation and reduce inefficiencies.

The article discusses the integration of natural language processing (NLP) with Apache Kafka, highlighting how Kafka can enhance data querying capabilities through NLP techniques. It emphasizes the importance of transforming and querying streaming data in a way that is intuitive for users, enabling better insights and decision-making from real-time data streams.

ShareChat transitioned from open-source Kafka to WarpStream to optimize their machine learning logging and handle their highly elastic workloads more efficiently. By adopting WarpStream's stateless architecture, ShareChat achieved significant cost savings and improved scalability, eliminating inter-AZ networking fees and reducing operational complexities associated with Kafka. The article details their testing results, showing WarpStream's advantages in throughput and cost-effectiveness compared to traditional Kafka setups.