In today's complex data landscape, seamless data movement between sources and destinations is crucial for operational and analytical uses of data. Data movement supports various use cases such as business intelligence and decision support, predictive modeling, machine learning and AI, real-time exposure of data, business process automation and data-driven products. A high-performance system that can be deceptively complex to engineer is required to move data from sources to destinations. Key considerations include properly scoping and scaling an environment, ensuring availability, recovering from failures and rebuilding the system in response to changing data sources and business needs. Five critical features of modern data movement are automation, incremental updates, idempotence, schema drift handling, and pipeline and network performance. Automation ensures fully managed and automated data movement from the standpoint of the end user, while incremental updates provide real-time or streaming updates by identifying changes made to a previous state of the source. Idempotence ensures that if you apply the same data to a destination multiple times, you will get the same result, which is crucial for recovery from pipeline failures. Schema drift handling involves faithfully preserving original values and ensuring smooth passage from source to destination despite evolving application data models. Pipeline and network performance can be improved through algorithmic improvements, code optimization, architectural changes, and pipelining, with transformations being a key aspect of ELT architectures that alter raw data into usable structures called data models.