REPLACE_ME: Scalable ETL Pipeline Architecture
REPLACE_ME: Built a robust data pipeline processing 10M+ records daily with real-time monitoring and automated error handling.
Designed and implemented a scalable ETL pipeline processing 10M+ records daily with 99.9% uptime, reducing data processing time by 75% and enabling real-time analytics.
The Problem
REPLACE_ME: The client's existing data infrastructure couldn't handle the growing volume of transactional data. Manual processes were causing delays, data quality issues, and preventing real-time business insights.
Approach
- 1
Analyzed existing data flows and identified bottlenecks in the current system
- 2
Designed a microservices-based architecture for scalable data processing
- 3
Implemented Apache Airflow for workflow orchestration and monitoring
- 4
Built data quality checks and automated error handling mechanisms
- 5
Created real-time monitoring dashboards for pipeline health
Solution
REPLACE_ME: Architected a cloud-native ETL pipeline using Apache Airflow for orchestration, AWS services for scalable compute and storage, and implemented real-time data quality monitoring with automated alerting.
Technologies Used
Results & Impact
99.9% pipeline uptime with automated error recovery
75% reduction in data processing time
10M+ records processed daily with linear scalability
Real-time data availability enabling instant business insights