tJapaneseTransliterate properties for Apache Spark Streaming - 7.1

Text standardization

author
Talend Documentation Team
EnrichVersion
Cloud
7.1
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Data Management Platform
Talend Data Services Platform
Talend MDM Platform
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Data Quality components > Standardization components > Text standardization components
Data Quality and Preparation > Third-party systems > Data Quality components > Standardization components > Text standardization components
Design and Development > Third-party systems > Data Quality components > Standardization components > Text standardization components
EnrichPlatform
Talend Studio

These properties are used to configure tJapaneseTransliterate running in the Spark Streaming Job framework.

The Spark Streaming 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, Talend MDM 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:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

 

Built-In: You create and store the schema locally for this component only. Related topic: see Talend Studio User Guide.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

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:

  • Hiragana (default value): this system converts the input Japanese text into its hiragana form.
  • Katakana reading: this system converts the input Japanese text (Kanji/Hiragana) into its katakana reading form.
  • Katakana pronunication: this system converts the input Japanese text (Kanji/Hiragana) into its katakana pronunciation form.
  • Revised Hepburn: This is the most widely used romanization system.
  • Kunrei-shiki: This romanization system has been standardized by the Japanese Government and the International Organisation for Standardisation as ISO 3602. It is a modified version of the Nihon-shiki system for modern standard Japanese.
  • Nihon-shiki: This romanization system maintains a one-to-one correspondence between kana and rĊmaji.

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:
  • Yarn mode (Yarn client or Yarn cluster):
    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premise distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.