From scattered data to ML-ready pipelines

The problem

Critical operational and customer data lived across multiple systems. Teams spent significant time preparing data manually before any analysis or modeling could happen — slowing decisions and limiting ML use cases.

Starting state

• •Manual exports and ad hoc SQL
• •Inconsistent data quality between acquisition and integration layers
• •ML projects blocked by upstream prep work

What changed

• •End-to-end ETL pipelines on Azure with logging and error handling
• •SQL stored procedures for reliable ingestion across data layers
• •Feature engineering pipeline to support modeling in Azure Databricks
• •LLM-assisted ingestion agent to accelerate messy intake workflows

Results

• ✓5× faster data ingestion via intelligent intake automation
• ✓More reliable pipelines with structured error handling
• ✓ML models fed by consistent, analysis-ready datasets