A leading US MedTech Company wanted to modernize their data architecture to meet increasing analytics demands, so they reached out to Exavalu for advisory and implementation services
Challenge
1
The on-premises data platform struggled to keep up with high order demands like prescriptive analytics, and patient-centric insights, resulting in slow, inconsistent and limited data delivery.
2
Data was siloed in various departments, limiting the implementation of unified analytics.
3
Futureproofing evolving data needs, third party integrations, and consumption patterns.
The Difference Exavalu Delivered
- Built cloud data architecture for Enterprise Data Organization with source data integration patterns, data lake formation and medallion architecture, spark based ETL, data warehouse, BI and analytics platforms.
- Developed an Enterprise Data Warehouse in Snowflake .
- Migrated from Informatica code to Spark based Glue ETL jobs, orchestrated via AWS Lambda and Step Functions, integrating data from JDE, EBS, PPA systems.
Tech Stack
- AWS S3
- Lambda
- Glue
- Athena
- Airflow
- Snowflake
Benefits
Key Highlights
1
200+
Entities in Snowflake
2
50%
Reduction in ETL run-time
3
25+
Sources integrated with Cloud Data Lake