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.
And: every defined filtering conditions must be satisfied. It represents the relationship FilterList.Operator.MUST_PASS_ALL
Or: at least one of the defined filtering conditions must be satisfied. It represents the relationship: FilterList.Operator.MUST_PASS_ONE
Filter type: the drop-down list presents pre-existing filter types that are already defined by HBase. Select the type of the filter you need to use.
Filter column: enter the column qualifier on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.
Filter family: enter the column family on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.
Filter operation: select from the drop-down list the operation to be used for the active filter.
Filter Value: enter the value on which you want to use the operator selected from the Filter operation drop-down list.
Filter comparator type: select the type of the comparator to be combined with the filter you are using.
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 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).