Greenplum Ingestion Source

In Gathr, Greenplum can be added as a channel to help in fetching customers’ and prospects’ data and transform it as needed before storing it in a desired data warehouse to run further analytics.

Data Source Configuration #

Configure the data source parameters as explained below.

Fetch From Source/Upload Data File #

To design the application, you can either fetch the sample data from the Greenplum source by providing the data source connection details or upload a sample data file in one of the supported formats to see the schema details during the application design phase.

Upload Data File #

To design the application, please upload a data file containing sample records in a format supported by Gathr.

The sample data provided for application design should match the data source schema from which data will be fetched during runtime.

If Upload Data File method is selected to design the application, provide the below details.

File Format #

Select the format of the sample file depending on the file type.

Gathr-supported file formats for Greenplum data sources are CSV, JSON, TEXT, Parquet and ORC.

For CSV file format, select its corresponding delimiter.

Header Included #

Enable this option to read the first row as a header if your Greenplum sample data file is in CSV format.

Upload #

Please upload the sample file as per the file format selected above.

Fetch From Source #

If Fetch From Source method is selected to design the application, then the data source connection details will be used to get sample data.

Continue to configure the data source.

Connection Name #

Connections are the service identifiers. A connection name can be selected from the list if you have created and saved connection details for Greenplum earlier. Or create one as explained in the topic - Greenplum Connection →

Use the Test Connection option to ensure that the connection with the Greenplum channel is established successfully.

A success message states that the connection is available. In case of any error in test connection, edit the connection to resolve the issue before proceeding further.

Schema Name #

Source Schema name for which the list of table will be viewed.

Table Name #

Source table name to be selected for which you want to view the metadata.

Query #

Hive compatible SQL query to be executed in the component.

Design Time Query #

Query used to fetch limited records during Application design. Used only during schema detection and inspection.

Enable Query Partitioning #

This enables parallel reading of data from the table. It is disabled by default.

Tables will be partitioned if this check-box is enabled.

If Enable Query Partitioning is check marked, additional fields will be displayed.

No. of Partitions #

Specifies the number of parallel threads to be invoked to partition the table while reading the data.

Partition on Column #

This column will be used to partition the data. This has to be a numeric column, on which spark will perform partitioning to read data in parallel.

Lower Bound #

Value of the lower bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

Upper Bound #

Value of the upper bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

If Enable Query Partitioning is disabled, then proceed by updating the following field.

Fetch Size #

The fetch size determines the number of rows to be fetched per round trip. The default value is 1000.

Add Configuration: Additional properties can be added using this option as key-value pairs.

Schema #

Check the populated schema details. For more details, see Schema Preview →