ADVANCING DATA PROCESSING PIPELINES: FROM BATCH TO REAL-TIME

The design of data processing pipelines has undergone a significant transformation, evolving from traditional batch-oriented workloads to highly responsive real-time and streaming architectures.Each type of pipeline batch, micro-batch, near real-time, realtime, and streaming offers unique strengths and trade-offs in terms of latency, scalability, cost, and complexity.As organizations continue to rely on data driven decision making, selecting the appropriate pipeline architecture becomes critical to balancing operational efficiency with analytical accuracy. This paper explores the characteristics, use cases, and enabling technologies of different pipeline models, presenting a comparative view that highlights how they fit into modern data engineering practices. The conversation covers how we've movedfrom traditional batch systems to real-time data processing.It also highlights the realworld challenges that data engineers face when building data pipelines tailored to different business needs.