Dataabsorption

Dataabsorption is the process by which data from external sources is captured, processed, and incorporated into a computing environment for storage and analysis. The term is used in information management and data engineering to describe the initial interface between data sources and the absorbing system, including ingestion, transformation, and entry into data stores.

Mechanisms include data ingestion, schema mapping, data cleansing, normalization, deduplication, and handling of latency. Data may be absorbed into data lakes, data warehouses, or operational data stores, using batch or streaming methods. The goal is to provide a consistent, queryable dataset.

In practice, dataabsorption supports analytics, reporting, machine learning, and real-time monitoring. It encompasses structured, semi-structured, and unstructured data from ERP systems, sensors, logs, social media, and external feeds, with governance and metadata management to maintain provenance.

Key challenges include data quality, schema evolution, duplication, timeliness, security and privacy, cost, and ensuring data lineage. Efficient absorption requires robust monitoring, error handling, and data validation.

Metrics used to evaluate absorption include throughput, latency, data completeness, accuracy, deduplication rate, and failure rates, as well as drift in schemas and schema compatibility over time.