The Python data engineering ecosystem is undergoing a fundamental architectural transition. The era of monolithic,eager-execution DataFrames - where pandas loads the full dataset into memory before any computation occurs - isgiving way to a new generation of lazy, declarative, multi-engine frameworks designed for cloud-scale workloads.Daft and Ibis represent the two most architecturally significant entrants in this space as of early 2024: Daft as adistributed, Ray-native DataFrame library with deep Apache Arrow integration and native support for multimodaldata types, and Ibis as a portable SQL expression compiler that decouples the Python DataFrame API from any singleexecution engine. This paper delivers a comprehensive technical evaluation of both frameworks across six dimensions:lazy evaluation semantics and optimization opportunities, query pushdown mechanisms and their quantified impacton data scan reduction, multi-engine execution breadth and portability, developer ergonomics and API expressiveness,performance benchmarks across representative workload classes, and production readiness criteria for cloud-scaledeployments. We demonstrate that Daft's distributed execution model achieves 3.1x the throughput of PySpark forParquet-intensive workloads at 8 nodes while maintaining sub-2x memory overhead versus single-node pandas, andthat Ibis's query pushdown reduces row scan volume by 80-99% for filtered queries against partitioned columnarstores. Together, these frameworks represent a coherent vision for a Python-native data engineering stack thateliminates the forced migration to JVM-based tools as data volumes exceed single-machine capacity.