Enhancing Data Flow with Python ETL Pipelines...

Enhancing Data Flow with Python ETL Pipelines and API Optimization Strategies

Author Name : Akash Balaji Mali, Vanitha Sivasankaran Balasubramaniam, Phanindra Kumar, Aravind Ayyagari, Prof. (Dr) Punit Goel, Om Goel

ABSTRACT The integration of Python-based Extract, Transform, Load (ETL) pipelines with API optimization strategies has emerged as a key approach to enhance data flow in modern organizations. This study explores the design and implementation of efficient Python ETL pipelines to streamline data extraction from multiple sources, perform transformations, and load the refined data into target systems. It highlights how APIs, when optimized, can facilitate seamless data exchange across disparate platforms, minimizing latency and improving throughput. Python's flexibility and extensive libraries, such as Pandas, PySpark, and SQLAlchemy, enable developers to build dynamic workflows, automate data processing, and ensure scalability. API optimization techniques, including pagination, caching, and rate-limiting, play a crucial role in mitigating bottlenecks and securing uninterrupted data transmission. Additionally, this research examines error-handling mechanisms, logging frameworks, and performance-tuning strategies that contribute to the reliability and robustness of the data flow architecture. The study concludes with insights into best practices for combining ETL pipelines with optimized APIs, empowering organizations to achieve faster decision-making through real-time data availability and enhanced analytics capabilities.