The text explores the complexities and hidden costs associated with data streaming technologies like Apache Kafka and Apache Flink, which are increasingly adopted by businesses for real-time data processing. While streaming offers significant advantages such as personalized recommendations and instant fraud detection, its economic viability is questioned due to the often-overlooked costs of engineering effort, infrastructure usage, and operational overhead. The 2025 Data Streaming Report underscores streaming as a strategic investment among IT leaders, with platforms like Confluent Cloud offering more predictable and lower total cost of ownership compared to self-managed solutions. The discussion highlights the importance of understanding the total cost of ownership, including infrastructure, operations, engineering, governance, and opportunity costs, to optimize streaming architectures effectively. The text also contrasts the cost efficiency of streaming versus batch processing, suggesting that while batch processing might appear cheaper initially, streaming can deliver long-term value by reducing latency and operational risks. Additionally, it critiques micro-batching for its increased latency and complexity, advocating Apache Flink as a better alternative for real-time processing. Real-world case studies, such as those from Citizens Bank and Notion, demonstrate substantial cost savings and productivity gains through optimized streaming strategies.