Apache Kafka has become a standard for streaming data, but its proliferation across enterprises often leads to increased complexity and cost due to siloed deployments by independent teams. This blog post from Confluent addresses the challenges faced by a global enterprise with over 40 Kafka clusters, highlighting issues such as lack of common strategy, repetitive DevOps work, and operational inefficiencies. By adopting a centralized data streaming strategy, the enterprise reduced its Kafka DevOps workforce from 50 to 15, achieving significant cost savings and enhanced focus on business problems. The consolidation of Kafka clusters also led to considerable infrastructure cost reductions while maintaining high reliability and performance. The post discusses the benefits and trade-offs of centralization versus decentralization, emphasizing the importance of aligning cluster strategies with data strategies while considering factors like cost, value, business needs, and technical requirements. Confluent suggests that a centralized platform can improve service quality, standardize processes, and mitigate risks, but the optimal strategy depends on the specific needs and structure of each organization.