tBigQueryOutput properties for Apache Spark Batch - 7.1

Google BigQuery

author
Talend Documentation Team
EnrichVersion
7.1
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Talend Big Data
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task
Data Governance > Third-party systems > Cloud storages > Google BigQuery components
Data Quality and Preparation > Third-party systems > Cloud storages > Google BigQuery components
Design and Development > Third-party systems > Cloud storages > Google BigQuery components
EnrichPlatform
Talend Studio

These properties are used to configure tBigQueryOutput running in the Spark Batch Job framework.

The Spark Batch tBigQueryOutput component belongs to the Databases family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

Basic settings

Dataset

Enter the name of the dataset to which the table to be created or updated belongs.

When you use BigQuery with Dataproc, in Google Cloud Platform, select the same region for your Google BigQuery dataset as for the Dataproc cluster to be run.

Table

Enter the name of the table to be created or updated.

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 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.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Table operations

Select the operation to be performed on the defined table:
  • Create table if does not exist: The table is created if it does not exist.

  • Truncate: The table content is deleted.

Data operation

Select the operation to be performed on the incoming data:
  • Append: Append data to the table, whether the table is empty or not.

Usage

Usage rule

This is an input component. It sends data extracted from BigQuery to the component that follows it.

Place a tBigQueryConfiguration component in the same Job because it needs to use the BigQuery configuration information provided by tBigQueryConfiguration.

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.