Problem Statement
The client, serving U.S. banks, lacked their own system to consolidate business entity data from all states. Instead, they relied on third-party data sources, which led to additional costs and limited control over data accuracy and consistency.
Approach & Solution
DataRopes.ai developed and implemented a comprehensive end-to-end ETL pipeline using Apache Airflow to manage and orchestrate the entire data flow.
- We built an automated ETL pipeline using Apache Airflow to retrieve data from all 50 U.S. states, ensuring consistent and up-to-date data consolidation.
- We processed the data using Cloud Dataflow, handling over 1 million records per day to ensure smooth data integration across the pipeline.
- The data was loaded into Cloud SQL, where stored procedures efficiently upserted data from temporary tables to production tables, enabling seamless data updates.
- Our setup allowed for state-specific data management, streamlining the transformation and loading of data with daily automation, ensuring real-time insights.
Results & Outcomes
By consolidating business entity data from all states into a single system, the client eliminated dependency on third-party sources, resulting in a 25% reduction in data acquisition costs. The internal data processing pipeline provided more accurate and consistent data, which improved the quality of services offered to U.S. banks. This enhanced operational efficiency and reduced data processing time by 40%, enabling quicker reporting and better decision-making. As a result, the client gained a competitive advantage, leading to increased customer satisfaction and a significant boost in ROI.
Tools & Technologies used
- Google Cloud Platform
- Apache Airflow
- Cloud Dataflow
- Cloud SQL
- PostgreSQL
- Cloud Composer
- Apache Beam
- Python
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