Ensuring that a data pipeline is efficient and minimizes the time and resources required for data processing is important for reducing costs and improving performance. Implementing a pipeline that uses parallel processing and optimization techniques, such as data partitioning, indexing, and query optimization, can help minimize the time and resources required for data processing. Implementing a pipeline that processes all the data in a single step or assuming that the data processing can be done in a sequential manner without any optimization can result in increased processing time and resource consumption. Focusing only on optimizing performance for the current data volumes without considering the efficiency of the processing may not be sustainable as data volumes grow or become more complex.