Telecom — Multi-Source Telecom Data Ingestion Pipeline

Overview

The telecom_km5_data_ingestion DAG is designed to ingest data from various sources, including Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, and Internet of Things (IoT) logs. The primary purpose of this DAG is to streamline the data ingestion process, ensuring that data is accurately normalized and subjected to quality checks to maintain its integrity. The ingestion pipeline begins with the extraction of data from the specified sources, followed by a

The telecom_km5_data_ingestion DAG is designed to ingest data from various sources, including Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, and Internet of Things (IoT) logs. The primary purpose of this DAG is to streamline the data ingestion process, ensuring that data is accurately normalized and subjected to quality checks to maintain its integrity. The ingestion pipeline begins with the extraction of data from the specified sources, followed by a normalization step that standardizes the data formats for consistency. Quality control measures are then applied to identify and rectify any discrepancies or errors in the data. Once the data passes these quality checks, it is stored in a centralized data warehouse, making it readily accessible for analysis. The pipeline also generates quality reports that provide insights into the ingestion performance, while alerts are configured to notify stakeholders of any potential errors during the process. Monitoring key performance indicators (KPIs) such as ingestion speed, error rates, and data completeness ensures that the pipeline operates efficiently. The business value of this DAG lies in its ability to provide accurate and timely data for performance analysis, enabling telecom companies to make informed decisions and enhance operational efficiency.

Part of the Document Automation solution for the Telecom industry.

Use cases

• Improves data accuracy for performance analysis
• Enhances decision-making through timely insights
• Streamlines data management across multiple sources
• Reduces operational risks with quality controls
• Increases efficiency in data processing workflows