Business Impact

  • 70%

    Reduction in time to market

  • Operational Efficiency due to self-triggering ETLs and data transformations for warehouse ingestions

  • Single view of data with a centralized lake house

Customer Key Facts

  • Location : India
  • Industry : Financial Services

Problem Context 

The client is a leading FinTech company that had their data distributed across multiple sources such as AWS RDS, on-premise SFTP & SQL Server, and in multiple formats. This resulted in data duplication along with high latency in data processing and the creation of financial reports. The client needed a centralized Data Lake with Data Warehouse and Data Mart strategies across three banking verticals for BI Dashboarding and efficient reporting.


  • Insurance claim data was highly confidential, thereby needed to be secured in transit
  • The business had around 1600 tables and a lot of attributes to work with for reporting
  • Absence of proper Data dictionary and Data Lineage tracking feature

Technologies Used

AWS Glue
Amazon Redshift
Amazon S3
Amazon EC2
AWS Lambda
Amazon VPC
Amazon SNS


Quantiphi helped to migrate historical data and built integrated data sources pipelines for daily incremental data ingestion and processing. We also built automated ETL pipelines for cleaning, transforming data, and loading it to serve as a data warehouse.


We have successfully created a centralized Data Lake across three banking verticals for BI Dashboarding and efficient reporting purposes

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