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Step plugin integrates with PDI by implementing four distinct Java interfaces. We recommend that you open and refer to sample step plugin sources while following these instructions. This section explains architecture and programming concepts for creating your own PDI transformation step plugin. Output steps write process data back to storage, files, or databases. Processing steps work with data rows, perform field calculations, and stream operations, such as joining or filtering.
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Input steps fetch data rows from external data sources, such as files or databases. Transformation steps are design for input, processing, or output. The Transformation step implements data processing task in ETL data flow.
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In the next section, Dimension Lookup / Update step will be added to the transformation. Finally, it identifies target table and SQL DDL statements necessary to create table and load data. It then adds fields to the Dimension table to handle Type 2 Slowly Changing Dimension. Next it established a surrogate key, which is also called technical key in Pentaho. This transformation step accomplishes multiple goals: First, it identifies business keys in source data and all of the fields in source data that will be applied to Dimension. In this section of the tutorial, each of the dimensions for data warehouse schema will be created by using the Dimension Lookup / Update transformation step. * Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions
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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions. Parent Topic Pentaho Data Integration Pentaho Data Integration provides Extract, Transform, and Load capabilities that facilitate the process of capturing, cleansing, and storing data using a uniform and consistent format that is accessible and relevant to end users and IoT technologies. Other PDI components, such as Spoon, Pan, and Kitchen, have names that were originally meant to support culinary metaphor of ETL offerings. When Pentaho acquired KETTLE, name was changed to Pentaho Data Integration. The term, KETTLE is a recursive term that stands for KETTLE Extraction Transformation Transport Load Environment. Pentaho Data Integration began as an open source project call. If you are new to Pentaho, you may sometimes see or hear Pentaho Data Integration referred to as, KETTLE.