AI-Powered Data Pipelines on Azure

Summary

Across multiple projects, I built scalable, event-driven ETL pipelines on Microsoft Azure by combining AI tools like Cognitive Services and Form Recognizer with Azure Functions. These solutions automated the extraction and processing of structured and unstructured data, improving accuracy and significantly reducing manual workload.

Context & Challenge 🧩

Across several data-focused projects, the aim was to automate the ingestion of both structured (databases, CSVs) and unstructured data (PDFs, images, forms). Manual entry was slow and error-prone, creating bottlenecks. The challenge: build a smart, scalable workflow that could handle diverse formats efficiently, react to changes, and stay cost-effective.

My Role & Contributions 🧑‍💻

I designed and built serverless, event-driven data pipelines on Microsoft Azure, focusing on automation and AI integration.

Real-Time Processing -> Used Azure Functions to trigger document processing as soon as files hit Azure Storage, enabling reactive, hands-free workflows.

AI-Driven Extraction -> Integrated Cognitive Services and Form Recognizer to extract text, key-values, and entities from unstructured formats like scanned invoices and forms.

Flexible Input Handling -> Developed logic to support various document types—ensuring consistent, automated processing across formats.

Scalable Monitoring -> Deployed and monitored solutions using Azure Monitor and Application Insights for robust, production-grade reliability.

Outcomes & Learnings 🚀

These projects delivered clear, measurable outcomes:

Fully automated manual data entry in key use cases.

Boosted data quality and accuracy from unstructured sources.

Reduced time from data arrival to analytics readiness.

Built scalable, reusable architectures with serverless and AI tools.

This experience highlighted how serverless and ML can drive smarter, faster, and more efficient data workflows.