The Cleansing module in the Quantela Platform ensures raw, inconsistent, or unstructured data is standardized for analytics, visualization, and automation workflows. By validating, deduplicating, and normalizing data formats, it enhances accuracy, reliability, and decision-making. Integrated with external sources, APIs, and real-time streams, the module ensures data remains structured and up-to-date without manual intervention.
Key Features:
The platform handles tabular (CSV), semi-structured (JSON, XML, HTML), and unstructured text data, enabling seamless ingestion from enterprise databases, IoT devices, and cloud applications. It automatically detects and corrects schema mismatches, missing fields, and delimiter inconsistencies. For semi-structured data, schema inference and entity extraction ensure optimized formatting for analytics.
By integrating with the Connectors module, the cleansing engine consolidates data from multiple internal and external sources into a unified repository. This eliminates silos, supports real-time synchronization, and enhances cross-functional analysis. The system maps, merges, and normalizes datasets, ensuring consistent and up-to-date information across business units.
The platform enforces standardization, removes duplicates, and validates data integrity before it reaches analytics or AI-driven processes. It corrects format mismatches, date/time inconsistencies, and encoding errors while applying predefined business rules and anomaly detection to ensure only relevant, high-quality data is used.
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