case study

Centralized Lake House

Banking & Financial Services

Business Impacts

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

Challenges

  • 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

AWS Glue

Amazon Redshift

Amazon Redshift

Amazon S3

Amazon S3

Amazon EC2

Amazon EC2

AWS Lambda

AWS Lambda

AWS IAM

AWS IAM

Amazon VPC

Amazon VPC

Amazon SNS

Amazon SNS

Solution

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.

Results

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

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