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Business Impact

  • 70%

    Reduction in time to market

  • $15K

    Estimated spend reduction per month

  • 95%

    Meeting SLA efficiency

Customer Key Facts

  • Location : North America
  • Industry : Insurance

Problem Context

The customer is a large insurance Fortune 100 company that had its data stored on over twenty systems and in multiple formats which resulted in data duplicity.

Challenges

 

  • Different teams used multiple tools which resulted in increased cost of ownership support resources
  • Insurance claim data is highly confidential and needs to be secured in transit
  • Integrating data from systems of different sizes and configurations
  • Large-scale data ingestion took required higher turnaround time
Challenges

Technologies Used

Amazon Lambda
Hadoop HDFS
Apache Ranger
Apache Atlas
Apache Knox
Hortonworks Data Platform

Developing a Centralized Data Lake for Scalability, Security and Greater Cost Savings

Solution

Quantiphi migrated their data from multiple on-prem data sources to cloud. A cost-effective and scalable centralized data lake platform was then built on cloud by setting up an ingestion pipeline with security protocols; further enabling them to visualize and draw insights in real time.

In the platform, billing alerts were enabled for any threshold violations, AWS Lambda functions were used to start/stop services as per business hours, and AWS Glue was used for fully automated and highly scalable heavy ETL jobs.

Result

  • Improved data security
  • Flexible user management
  • Foundation for future AI/ML workloads

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