Etl pipeline на Python для аналізу даних агрегаторів у сфері авіаперевезень

Abstract

In the fast-growing aviation industry, effective data management and analysis are key factors for the success of airline aggregator companies. This article focuses on creating and using an ETL pipeline (Extract, Transform, Load) with the Python programming language to collect, process, and integrate flight data. The article explains the method of building ETL processes, including extracting data from SFTP servers, filtering, splitting records, and finding errors using the Pandas library. After that, the data is loaded into a database for analysis. Special attention is given to Apache Airflow, a tool for automating workflows, which helps manage tasks flexibly and scale the system when needed. The article also looks at the advantages of using such solutions to improve the accuracy and freshness of data. This allows aggregators to offer the best options to clients in real time. The practical steps of setting up ETL pipelines and how they help make business processes more efficient in the aviation industry are described.