Case Study: ETL Solutions
The client needed ETL solutions for handling data of any complexity or size in a variety of formats and/or from different upstream sources.
The client’s data management team extracted and processed data from different sources and different types of databases (e.g. Oracle, Netezza, Excel files, SAS datasets, etc.), and needed to load into its Oracle and AWS datamarts for it’s revenue and loss forecasting processes.
The client’s forecasting process used very complex large-scale datasets in different formats which needed to be consumed and loaded in an automated and timely manner.
RiskSpan was engaged to design, develop and implement ETL (Extract, Transform and Load) solutions for handling input and output data for the client’s revenue and loss forecasting processes. This included dealing with large volumes of data and multiple source systems, transforming and loading data to and from data marts and data ware houses.
- Analyzed data sources and developed ETL strategies for different data types and sources
- Performed source target mapping in support of report and warehouse technical designs
- Implemented business-driven requirements using Informatica
- Collaborated with cross-functional business and development teams to document ETL requirements and turn them into ETL jobs
- Optimized, developed, and maintained integration solutions as necessary to connect legacy data stores and the data warehouses