Zymr rebuilt the ETL pipeline using Python, Apache Airflow, and Pandas. We modularized ingestion, transformation, and load stages into task-based DAGs with robust error handling and retries. Python scripts were optimized for multi-threaded processing and performance tuning. The Airflow UI enabled centralized scheduling, monitoring, and alerting. Data validation layers were introduced to ensure quality at every stage.
A global logistics company operating across 40+ international hubs, specializing in cargo management, fleet tracking, and third-party delivery coordination. The client wanted to overhaul its data infrastructure to support real-time analytics and reduce downtime in business-critical systems.
The legacy ETL system, built on outdated scripting frameworks, caused frequent data mismatches and daily reporting failures. Lack of centralized job orchestration led to untracked failures and delays in shipment analytics, affecting decision-making across regional centers.
The revamped pipeline eliminated all critical ETL failures and reduced data latency by 60%, supporting real-time insights across operations. The system scaled effortlessly across regions with minimal intervention.
Zymr rebuilt the ETL pipeline using Python, Apache Airflow, and Pandas. We modularized ingestion, transformation, and load stages into task-based DAGs with robust error handling and retries. Python scripts were optimized for multi-threaded processing and performance tuning. The Airflow UI enabled centralized scheduling, monitoring, and alerting. Data validation layers were introduced to ensure quality at every stage.
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