Retail — Multi-Source Retail Data Ingestion Pipeline
FreeThis DAG facilitates the ingestion of multi-source data for market analysis in retail. It ensures data normalization and enrichment, followed by quality checks to maintain data integrity.
Overview
The purpose of the retail_ecommerce_km5_data_ingestion DAG is to streamline the ingestion of data from various sources such as ERP systems, CRM platforms, and CSV files. This pipeline is designed to normalize and enrich the incoming data before storing it in a centralized data warehouse, enabling comprehensive market analysis. The ingestion process begins with extracting data from specified sources, followed by transformation steps that include data cleansing, normalization, and enrichment with
The purpose of the retail_ecommerce_km5_data_ingestion DAG is to streamline the ingestion of data from various sources such as ERP systems, CRM platforms, and CSV files. This pipeline is designed to normalize and enrich the incoming data before storing it in a centralized data warehouse, enabling comprehensive market analysis. The ingestion process begins with extracting data from specified sources, followed by transformation steps that include data cleansing, normalization, and enrichment with additional contextual information. Quality control measures are implemented throughout the process to ensure data integrity, with automated alerts configured to notify stakeholders in case of ingestion failures. The outputs of this DAG include a well-structured data repository that supports analytical queries and reporting. Monitoring key performance indicators (KPIs) such as ingestion success rates and data quality metrics is crucial for maintaining operational efficiency. The business value of this DAG lies in its ability to provide accurate and timely data insights, driving informed decision-making and enhancing competitive advantage in the retail sector.
Part of the Document Automation solution for the Retail industry.
Use cases
- Improved data accuracy enhances decision-making
- Faster access to critical market insights
- Reduced manual data handling and errors
- Increased operational efficiency through automation
- Enhanced competitive advantage with timely analytics
Technical Specifications
Inputs
- • ERP transaction logs
- • CRM customer interaction data
- • CSV sales reports
Outputs
- • Normalized data warehouse entries
- • Data quality reports
- • Market analysis dashboards
Processing Steps
- 1. Extract data from ERP, CRM, and CSV sources
- 2. Cleanse and validate incoming data
- 3. Normalize data formats for consistency
- 4. Enrich data with additional contextual information
- 5. Apply quality control checks
- 6. Store processed data in the data warehouse
- 7. Generate reports and dashboards for analysis
Additional Information
DAG ID
WK-0365
Last Updated
2025-10-31
Downloads
72