Batch processing and stream processing are two common approaches to data processing, each with its own strengths and weaknesses. Batch processing is suitable for large, finite datasets that require complex computations or heavy analysis, such as e-commerce order processing and billing. It is efficient when performed properly but can be monolithic in nature, complex to manage, and expensive to build. On the other hand, stream processing is ideal for real-time analytics and data generation with high velocity, requiring split-second decisioning and visibility. Stream processing faces challenges such as implementing at scale, providing resiliency against imperfections, and distributing processing across multiple compute engines. A hybrid approach combining batch and stream data processing can address these limitations, but requires careful consideration of infrastructure needs. Ultimately, the choice between batch and stream processing depends on individual business requirements.