Platform Transforms Overview:

Platform Transforms Overview:

Platform Transforms List & Overview:

The list of Platform transforms present are:

1.       Case

2.       Data_Mask

3.       Map_Operation

4.       Merge

5.       Query

6.       Row_Generation

7.       SQL

8.       Validation

Lets look into the detailed description of each of the transforms present under Platform category.

1.Case:

·         This transform specifies multiple paths in a single transform (different rows are processed in different ways).

·         The Case transform simplifies branch logic in data flows by consolidating case or decision making logic in one transform. Paths are defined in an expression table.

Please go through the article ‘Case transform in SAP Data Services’ to know more details about this transform.

2. Data_Mask:

The Data Mask transform enables us to protect personally identifiable information in our data.

Personal information includes data such as credit card numbers, salary information, birth dates, personal identification numbers, or bank account numbers.

We may want to use data masking to support security and privacy policies, and to protect our customer or employee information from possible theft or exploitation.

Please go through the article ‘Data Mask transform in SAP Data Services’ to know more details about this transform.

3. Map Operation:

·         This transform modifies data based on mapping expressions and current operation codes. The operation codes can be converted between data manipulation operations.

·         Writing map expressions per column and per row type (INSERT/UPDATE/DELETE) allows us to perform:

·         Change the value of data for a column.

·         Execute different expressions on a column, based on its input row type.

·         Use the before_image function to access the before image value of an UPDATE row.

Please go through the article ‘Map Operation transform in SAP Data Services’ to know more details about this transform.

4. Merge:

·         This transform combines incoming data sets, producing a single output data set with the same schema as the input data sets.

Please go through the article ‘Merge transform in SAP Data Services’  to know more details about this transform.

5. Query:

The Query transform retrieves a data set that satisfies conditions that we specify.

·         A Query transform is similar to a SQL SELECT statement.

Please go through the article ‘Query transform in SAP Data Services’ to know more details about this transform.

6. Row generation:

This transform produces a data set with a single column.

·         The column values start with the number that we set in the ‘Row number starts’ at option. The value then increments by one to a specified number of rows.

Please go through the article ‘Row Generation transform in SAP Data Services’ to know more details about this transform.

7. SQL:

·         This transform performs the indicated SQL query operation. Use this transform to perform standard SQL operations when other built-in transforms cannot perform them.

·         The options for the SQL transform include specifying a datastore, join rank, cache, array fetch size, and entering SQL text.

·         Note:The SQL transform supports a single SELECT statement only.

Please go through the article ‘SQL transform in SAP Data Services’ to know more details about this transform.

8.Validation:

·         The Validation transform qualifies a data set based on rules for input schema columns.

·         We can apply multiple rules per column or bind a single reusable rule (in the form of a validation function) to multiple columns.

·         The Validation transform can identify the row, column, or columns for each validation failure. We can also use the Validation transform to filter or replace (substitute) data that fails our criteria.

·         When we enable a validation rule for a column, a check mark appears next to it in the input schema.

Please go through the article ‘Validation transform in SAP Data Services’ to know more details about this transform.

10. XML Map:

·         The XML_Map transform is a data transform engine designed for hierarchical data. It provides functionality similar to a typical XQuery or XSLT engine.

·         The XML_Map transform takes one or more source data sets and produces a single target data set. Flat data structures such as database tables or flat files are also supported as both source and target data sets.

·         We can use the XML_Map transform to perform a variety of tasks. For example:

·         We can create a hierarchical target data structure such as XML or IDoc from a hierarchical source data structure.

·         We can create a hierarchical target data structure based on data from flat tables.

·         We can create a flat target data set such as a database table from data in a hierarchical source data structure.

·         XML_Map transform works in two modes- Normal and Batch mode.

·         In normal mode, data is handled on a row by row basis before sending it to the next transform.

·         In batch mode, data is handled as block of rows, before sending it to the next transform.

·         There are different transform icons to indicate each mode.

Please go through the article ‘XML Map transform in SAP Data Services’ to know more details about this transform.

Text data Processing Transforms Overview

Text data Processing Transforms:

The list of Text data processing transforms present are:

·         Entity_Extraction

·       Lets look into the detailed description of each of the transforms present under Text data Processing category.

·       Entity Extraction:

·       The Entity Extraction transform performs linguistic processing on content by using semantic and syntactic knowledge of words.

·       We can configure the transform to identify paragraphs, sentences, and clauses and it can extract entities and facts from text.

·       Typically, we use the Entity Extraction transform when we have text with specific information we want to extract and then use in downstream
 analytics and applications.

·       Please go through the article ‘Entity Extraction transform in SAP Data Services’ to know more details about this transform.

Data Flow:

Data flow is used to extract, transform and load data from the source to the target system. All the transformations, loading and formatting occurs in dataflow.

Once you define a data flow in a project, this can be added to a workflow or an ETL job. Data flow can send or receive objects/information using parameters. Data flow is named in format DF_Name.