Cloud-based data warehouse
Challenge
A client in the telecommunications industry decided to build a new data warehouse based on a cloud environment. The focus was on address points for FTTH/FTTB fiber optic installations, covering approximately one million residential units (including both existing and new locations). The main goals for the new data warehouse were:
- To gather dimensions and attributes for address points in the warehouse to meet current reporting needs
- To reduce data processing and storage costs
- To apply standards and best practices in data warehouse construction
- To facilitate the integration of address point data with data from other areas, such as the orders area
Solution
Based on Sorigo's experience in the telecommunications field and in building data warehouse solutions, our team designed and implemented a data warehouse for the client. The following activities contributed to its execution:
- Developing and utilizing a standard for building data warehouse areas using the Data Vault 2.0 methodology and the Google BigQuery platform
- Conducting a series of workshops and meetings with business stakeholders to define the relationships of dimensions and attributes (describing address points) in use cases and business processes
- Preparing a comprehensive dependency analysis using Sparx Enterprise Architect, resulting in dependency matrices and relationship views focused on specific reporting dimensions
- Implementing processes for loading address point data into the warehouse
- Building a mechanism for managing subscriptions to specific dimension attributes describing the address points of fiber optic service installations
- Providing an information layer and data mart for address point data to agency network teams (in collaboration with operators) and back-office consultants
Sorigo also recommended changes to systems based on the analysis and reporting requirements.
Result
Thanks to the implementation of a data warehouse based on the Google BigQuery platform and the Data Vault 2.0 model, the client:
- Increased the efficiency of using address point data for business purposes
- Reduced data processing and storage costs
- Significantly improved data processing performance
- Standardized the data model and solution architecture
- Gained the ability to track attribute changes over time (change history), which is crucial for back-office consultants
- Lowered the costs of introducing new attributes in defined dimensions
- Made it easier to integrate address point data with data from other areas
- Enhanced the solution's scalability, flexibility, and security