In today's fast-paced data-driven world, the demand for real-time data solutions often challenges the capabilities of existing systems, necessitating efficient strategies like batching to improve performance. Streaming platforms like Redpanda capitalize on modern hardware advancements to optimize resource utilization and manage growing data complexity. Batching involves grouping multiple requests into one, reducing the fixed costs associated with individual requests and enhancing system efficiency by minimizing network calls and improving compression ratios. However, batching introduces additional latency as data is buffered before being sent, which can be a downside for time-sensitive applications. Effective batching in Redpanda is influenced by several factors, including message rate, batch size, linger time, partitioning, and client memory. While larger batch sizes can reduce CPU load and latency during high request rates, they require careful tuning to balance performance and latency. This article explores the foundational aspects of batching and its application in Redpanda, with a promise of further insights into observability and tuning in the next installment.