Data transformation and movement is the process by which source data is selected, converted, and mapped to the format required by targeted systems.
The process manipulates data to bring it into compliance with business, domain, and integrity rules and with other data in the target environment. Transformation can take some of the following forms:
Aggregation
Consolidating or summarizing data values into a single value. Collecting
daily sales data to be aggregated to the weekly level is a common
example of aggregation.
Basic conversion
Ensuring that data types are correctly converted and mapped from source to target columns.
Cleansing
Resolving inconsistencies and fixing the anomalies in source data.
Derivation
Transforming data from multiple sources by using a complex business rule or algorithm.
Enrichment
Combining data from internal or external sources to provide additional meaning to the data.
Normalizing
Reducing the amount of redundant and potentially duplicated data.
Combining
The process of combining data from multiple sources via parallel Lookup, Join, or Merge operations.
Pivoting
Converting records in an input stream to many records in the appropriate table in the data warehouse or data mart.
Sorting
Grouping related records and sequencing data based on data or string values.
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