High Tech — Multi-Source Data Ingestion for Pricing Optimization
NewThis DAG ingests data from multiple sources to enhance pricing strategies. It ensures data integrity and prepares information for advanced analysis.
Overview
The purpose of this DAG is to facilitate the ingestion of data from diverse sources such as ERP systems, CRM platforms, and business APIs to support pricing optimization efforts in the high-tech industry. The ingestion pipeline begins with collecting raw data from these various sources, ensuring that all relevant information is captured for analysis. Once the data is ingested, it undergoes normalization to standardize formats and historical data storage to maintain a comprehensive record of chan
The purpose of this DAG is to facilitate the ingestion of data from diverse sources such as ERP systems, CRM platforms, and business APIs to support pricing optimization efforts in the high-tech industry. The ingestion pipeline begins with collecting raw data from these various sources, ensuring that all relevant information is captured for analysis. Once the data is ingested, it undergoes normalization to standardize formats and historical data storage to maintain a comprehensive record of changes over time. Quality control measures are implemented throughout the process to verify data integrity, including validation checks and error logging, which help in identifying and rectifying issues promptly. The processed data is then stored in a centralized data warehouse, making it readily accessible for further analysis and decision-making. Key performance indicators (KPIs) such as ingestion time and error rates are monitored to assess the efficiency and reliability of the pipeline. In case of failures, a robust recovery mechanism is in place to ensure minimal disruption to operations. By leveraging this DAG, organizations can achieve significant business value through improved pricing strategies, enhanced decision-making capabilities, and increased operational efficiency.
Part of the Pricing Optimization solution for the High Tech industry.
Use cases
- Improved pricing strategies through data-driven insights
- Enhanced decision-making capabilities with reliable data
- Increased operational efficiency and reduced downtime
- Comprehensive view of historical pricing data
- Reduced risk of data errors impacting business outcomes
Technical Specifications
Inputs
- • ERP transaction logs
- • CRM customer interaction data
- • Business API sales data
Outputs
- • Normalized data sets in the data warehouse
- • Error logs for quality control
- • Performance reports on ingestion metrics
Processing Steps
- 1. Collect data from ERP, CRM, and APIs
- 2. Normalize and standardize incoming data
- 3. Apply quality control checks and validations
- 4. Store processed data in the data warehouse
- 5. Monitor ingestion performance and log errors
- 6. Implement recovery mechanisms for failures
Additional Information
DAG ID
WK-0984
Last Updated
2025-05-12
Downloads
69