Revolutionizing banking with data warehousing solutions
Problem statement
Limited Data Traditional systems often rely on manual data scraping and only provide limited data for analysis, resulting in incomplete and unreliable insights.
Time-Consuming: Manually scraping data is time-consuming and can slow down the loan analysis process
Inaccurate Results: Traditional systems are prone to errors and inconsistencies, which can lead to inaccurate results and poor decision making.
Lack of Integration: Traditional systems often don't integrate with other data sources, making it difficult to derive meaningful insights from multiple sources.
Limited Scalability: Traditional systems are often not equipped to handle large volumes of data, making it difficult to scale the analysis process as needed.
Solution approach
We have worked with the client in implementing AWS data warehousing system for collecting their loan data and using them for data analysis.
The datawarehouse solution is capable of handling large data files with micro-level data in a matter of seconds, saving time and effort.
Downloaded end-user loan details as initial step using a scheduled job using SQL Runner Scripts tool.
Built a pipeline to extract the downloaded file and upload it into multiple tables. Uploaded data files are moved to an archive folder. Designed Dimensions, HUB, and SAT tables using the liquibase tool.
Data uploaded to base tables are aggregated and validated using AWS Lambda functions. Build and deploy changes using AWS Code Commit and Code pipeline.
Business impact
Improved data collection and processing: Data engineering models automate and streamline the collection and processing of loan data, reducing manual effort and increasing efficiency.
Enhanced risk management: With the ability to access loan data and perform sophisticated analysis, organizations can better understand and manage risk in their loan portfolio quickly and easily.
Enhanced data insights: By using advanced data processing techniques and visualizations, data engineering models can provide deeper insights into loan data, allowing organizations to make more informed decisions.
Improved data accessibility: With a centralized and organized repository of loan data, stakeholders across the organization can access and utilize data more easily, driving better collaboration and decision-making.
Increased competitiveness: By leveraging data to drive business decisions, organizations using data engineering models have a competitive advantage over those relying on traditional, manual methods.
