Looding...

Centralized Data Lake

Insurance
case study

Business Impacts

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

Technologies Used

Amazon Lambda

Amazon Lambda

Hadoop HDFS

Hadoop HDFS

Apache Ranger

Apache Ranger

Apache Atlas

Apache Atlas

Apache Knox

Apache Knox

Hortonworks Data Platform

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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