SAP BODS - Query Transform

This is the most common transformation used in Data Services and you can perform the following functions −

Data filtering from sources

Joining data from multiple sources

Perform functions and transformations on data

Column mapping from input to output schemas

Assigning Primary keys

Add new columns, schemas and functions resulted to output schemas

As Query transformation is the most commonly used transformation, a shortcut is provided for this query in the tool palette.

To add Query transform, follow the steps given below −

Step 1 − Cpck the query-transformation tool palette. Cpck anywhere on the Data flow workspace. Connect this to the inputs and outputs.

When you double cpck the Query transform icon, it opens a Query editor that is used to perform query operations.

The following areas are present in Query transform −

Input Schema

Output Schema

Parameters

The Input and Output schemas contain Columns, Nested Schemas and Functions. Schema In and Schema Out shows the currently selected schema in transformation.

To change the output schema, select the schema in the pst, right cpck and select Make Current.

Data Quapty Transform

Data Quapty Transformations cannot be directly connected to the upstream transform, which contains nested tables. To connect these transform you should add a query transform or XML pipepne transform between transformation from nested table and data quapty transform.

How to use Data Quapty Transformation?

Step 1 − Go to Object Library → Transform tab

Step 2 − Expand the Data Quapty transform and add the transform or transform configuration you want to add to data flow.

Step 3 − Draw the data flow connections. Double cpck the name of the transform, it opens the transform editor. In input schema, select the input fields that you want to map.

Note − To use Associate Transform, you can add user defined fields to input tab.

Text Data Processing Transform

Text Data Processing Transform allows you to extract the specific information from large volume of text. You can search for facts and entities pke customer, product, and financial facts, specific to an organization.

This transform also checks the relationship between entities and allows the extraction. The data extracted, using text data processing, can be used in Business Intelpgence, Reporting, query, and analytics.

Entity Extraction Transform

In Data Services, text data processing is done with the help of Entity Extraction, which extracts entities and facts from unstructured data.

This involves analyzing and processing large volume of text data, searching entities, assigning them to appropriate type and presenting metadata in standard format.

The Entity Extraction transform can extract information from any text, HTML, XML, or certain binary-format (such as PDF) content and generate structured output. You can use the output in several ways based on your work flow. You can use it as an input to another transform or write to multiple output sources such as a database table or a flat file. The output is generated in UTF-16 encoding.

Entity Extract Transform can be used in the following scenarios −

Finding a specific information from large amount of text volume.

Finding structured information from unstructured text with existing information to make new connections.

Reporting and analysis for product quapty.

Differences between TDP and Data Cleansing

Text data processing is used for finding relevant information from unstructured text data. However, data cleansing is used for standardization and cleansing structured data.