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Data Integration

Data Integration. Combining data from different sources, providing a unified view of the data Data warehouse is a repository that results from some types of data integration processes. Techniques for Data Integration. Consolidation (ETL) Extract/Transform/Load

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Data Integration

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  1. Data Integration Combining data from different sources, providing a unified view of the data Data warehouse is a repository that results from some types of data integration processes

  2. Techniques for Data Integration • Consolidation (ETL) • Extract/Transform/Load • Consolidating all data into a centralized database (like a data warehouse) • Data federation (EII) • Enterprise Information Integration • Provides a virtual view of data without actually creating one centralized database • Data propagation (EAI) • Enterprise Application Integrations • Duplicate data across databases, with near real-time delay

  3. The ETL Process • Capture/Extract • Scrub or data cleansing • Transform • Load and Index ETL = Extract, transform, and load

  4. Capture/Extract…obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse Incremental extract = capturing changes that have occurred since the last static extract Static extract = capturing a snapshot of the source data at a point in time

  5. Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data quality Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data

  6. Transform = convert data from format of operational system to format of data warehouse Record-level: Selection–data partitioning Joining–data combining Aggregation–data summarization Field-level: single-field–from one field to one field multi-field–from many fields to one, or one field to many

  7. Load/Index= place transformed data into the warehouse and create indexes Refresh mode: bulk rewriting of target data at periodic intervals Update mode: only changes in source data are written to data warehouse

  8. Data Transformation Functions • Record-level • Transformation that involves obtaining the set of recordsyou want from the data source • Selection, joining, aggregation • Field-level • Transformation that converts data from fields of a source record to field(s) of a target record. • Single-field vs. Multi-field transformations

  9. Single-field transformation In general–some transformation function translates data from old form to new form Algorithmic transformation uses a formula or logical expression Tablelookup–another approach, uses a separate table keyed by source record code

  10. Multifield transformation M:1–from many source fields to one target field 1:M–from one source field to many target fields

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