tJapaneseTransliterate properties for Apache Spark Batch
These properties are used to configure tJapaneseTransliterate running in the Spark Batch Job framework.
The Spark Batch tJapaneseTransliterate component belongs to the Data Quality family.
The component in this framework is available in Talend Data Management Platform, Talend Big Data Platform, Talend Real-Time Big Data Platform, Talend Data Services Platform, and in Talend Data Fabric.
Basic settings
Schema and Edit Schema |
A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields. Click Sync columns to retrieve the schema from the previous component connected in the Job. Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
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Built-In: You create and store the schema locally for this component only. |
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Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. |
Transliteration |
The columns from the output schema are added to the Column column in the Transliteration table. For each of the schema columns containing Japanese text to be transliterated into another system, select the corresponding check box in the Transliterate column. You can select the check box in the header row to select all schema columns. After selecting the Transliterate check box, select the transliteration system from the Way to transliterate list:
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Advanced settings
tStatCatcher Statistics |
Select this check box to gather the Job processing metadata at the Job level as well as at each component level. |
Usage
Usage rule |
This component is usually used as an intermediate component, and it requires an input component and an output component. |
Spark Connection |
In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:
This connection is effective on a per-Job basis. |