SAP BODS Query Transform - SAP BODS

What is SAP BODS Query Transform?

This is the most common transformation used in Data Services and you can execute 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 conversion is the most usually used conversion, a shortcut is providing for this query in the tool palette.

To add Query transform, follow the phases given below −

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

connect_inputs_outputs

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

The following areas are existing in Query transform −

  • Input Schema
  • Output Schema
  • Parameters

The Input and Output schemas hold Columns, Nested Schemas and Functions. Schema In and Schema Out display the presently every schema in transformation.

input_output_schemas

To modification the output schema, choice the schema in the list, right click and choice Create Current.

change_output_schema

Data Quality Transform

Data Quality Transformations cannot be directly associated to the upstream transform, which holds nested tables. To connect these transform you should add a query transform or XML tube transform between transformation from nested table and data quality transform.

How to use Data Quality Transformation?

Step 1 − Go to Object Library → Transform tab

library_transform_tab

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

transform_configuration

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

Note − to use Associate Transform, you can add user distinct 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 like customer, product, and financial facts, specific to an organization.

This transform also checks the connection between objects and permits the extraction. The data removed, using text data processing, can be used in Business Intelligence, Reporting, query, and analytics.

Entity Extraction Transform

In Data Services, text data processing is completed with the help of Entity Abstraction, which abstracts objects and facts from unstructured data.
This involves analyzing and processing large capacity of text data, searching individuals, allocating them to correct type and offering metadata in standard format.

The Entity Extraction transform can extract information from any text, HTML, XML, or certain binary-format (such as PDF) satisfied 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 many output sources such as a database table or a flat file. The output is generated in UTF-16 programming.

Entity Extract Transform can be used in the following scenarios −

  • Finding 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 quality.

Differences between TDP and Data Cleansing

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

Parameters Text Data Processing Data Cleansing
Input Type Unstructured Data Structured Data
Input Size More than 5KB Less than 5KB
Input Scope Broad domain with many variations Limited variations
Potential Usage Potential meaningful information from unstructured data Quality of data for storing in to Repository
Output Create annotations in form of entities, type, etc. Input is not changed Create standardized fields, Input is changed

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