Centralized Lake House
Banking & Financial ServicesBusiness 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
Amazon Redshift
Amazon S3
Amazon EC2
AWS Lambda
AWS IAM
Amazon VPC
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
