Writing and reading data from MongoDB using a Spark Batch Job - 7.3

1;Gregg Araki	
2;P.J. Hogan 
3;Alan Rudolph 
4;Alex Proyas
5;Alex Sichel

• 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-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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