05
Multi-Source Data Integration & ETL Automation Platform
Business Challenges
Fragmented Data Sources
Multiple APIs and systems
Lack of Standard Formats
Inconsistent structures
Delayed Processing
Slow data updates
Data Quality Issues
Inconsistent and duplicate data
Manual ETL Effort
High dependency on manual workflows
Solution Implemented
API & Webhook Integration
Automated multi-source ingestion
ETL Pipeline Automation
Built using Magic ETL and Python
Centralized Data Layer
Maintained structured datasets
Data Validation and Deduplication
Ensured quality and accuracy
XML Transformation Workflows
Delivered structured outputs
Technology Stack
Domo (Magic ETL)
AppDB
Jupyter
Python
APIs
Webhooks
Business Impact
60–70% Reduction in manual effort
Improved data accuracy and consistency
Faster processing cycles
Enhanced system reliability
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