Financial Information Hub for Claims Analytics
Large US health insurance
Client is one of the largest health insurers in the USA.
Cost-effective & scalable framework
- Datametica created a cost-effective and scalable framework for their financial information hub to acquire, clean, and prepare data for Enterprise consumption;
- Established a layered data lake/Hub architecture on Hadoop to provide the flexibility to store and process unlimited data;
- Data was loaded from source data subject areas, processed in Hadoop, and then exposed for analytics;
- Kafka, Storm, Camus, and Hbase was used for real-time data collection and data aggregation;
- Gold data tables are exposed to Hadoop SQL interface to enable business users to query;
- Data governance support with an audit process, metadata management, and data lineage.
The use of distributed processing power of Hadoop framework led to greatly improved scalability compared to Teradata
Improved security and fault tolerance
Real-time data views for proactive analysis
Ingest of both structured and unstructured data
Costs reduction compared to legacy Teradata data warehouse approach