Extraction

Enhanced Definition

"Extraction" in the mainframe context refers to the process of selectively retrieving specific data records or fields from a larger dataset, file (e.g., VSAM, sequential), or database (e.g., DB2, IMS). This "removal process" involves isolating desired information based on defined criteria, often transforming it, and preparing it for subsequent processing, reporting, or migration, without altering the original source data.

Key Characteristics

- Non-destructive: The source data remains unchanged and intact after the extraction process.
- Filtering and Selection: Utilizes specific criteria (e.g., WHERE clauses, SELECT statements, control cards) to identify and isolate relevant data.
- Data Transformation: Often includes reformatting, aggregation, or derivation of new data elements during the extraction phase to meet target system requirements.
- Batch-Oriented: Typically executed as part of a batch job stream using JCL, custom COBOL programs, or specialized mainframe utilities.
- Output Flexibility: Extracted data can be directed to various targets, such as sequential files, VSAM datasets, other databases, or reports.
- Performance Critical: For large volumes of data, efficient utility usage, optimized program logic, and proper indexing are crucial for timely completion and minimal system impact.

Use Cases

- Data Warehousing and Business Intelligence: Populating data warehouses or data marts with operational data from transactional systems for analytical purposes.
- Reporting and Auditing: Generating specific management reports, regulatory compliance reports, or audit trails from large production datasets.
- Data Migration and Conversion: Preparing subsets of data for transfer to new systems, platforms, or applications, often involving reformatting.
- Interface File Generation: Creating flat files or other structured datasets for exchange with external systems or applications.
- Test Data Creation: Extracting a representative subset of production data to create realistic test environments without exposing sensitive full datasets.