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Before Airflow, the management of data pipelines and workflows was usually done manually using scripts and cron jobs. This approach required significant development effort, as it involved writing code for each task and schedule it accordingly. Additionally, there was no centralized platform for monitoring and managing workflows, making it difficult to track and troubleshoot issues. The lack of automation and visibility made it challenging to reliably process data at-scale and resulted in high maintenance costs.