Operational systems of record often provide data that is not readily consumable by analytics projects and frequently require complex extract, transform and load (ETL) processing to conform the information into a
standard data warehouse model. One common transformation is to pivot multiple rows into a single row
with multiple columns to provide a flattened data set for analysis. Most ETL technologies provide a capability to perform that transformation when processing data in batch; however, the Qlik Data Integration platform provides methods that support both batch and near real-time pivoting of source data into the data warehouse.
This paper provides real-world examples that explain the purpose of a pivoted data set, describes how to model the data structures, and how to implement transformation logic for batch and near real-time processing.
