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A structured method for identifying, extracting and preparing data from diverse sources for analysis, integration or downstream processing.
Data Extraction is a repeatable method for identifying, acquiring and structuring data from heterogeneous sources to prepare it for analysis, integration or downstream processing. It defines discovery, connector selection, sampling, schema mapping and validation steps, ensuring traceability, reproducibility and quality control across extraction workflows.
Average time per extraction run.
Share of failed extraction runs.
Number and severity of validation errors.
An e-commerce team extracts product and price information from supplier APIs for consolidation.
Operational monitoring uses extracted logs from application servers for dashboards.
During migration, tables from a legacy DB are extracted, cleaned and mapped.
Discovery: inventory sources and capture metadata.
Pilot: implement connector, create and validate sample extracts.
Go-live: define scheduling, monitoring and SLAs.