Processing strings about harvested crops

A pipeline with an S3 source, two Strings processors, and an S3 destination.

Before you begin

You have previously created a connection to the system storing your source data.

Here, an Amazon S3 connection.
You have previously added the dataset holding your source data.

Download the file: string-crops.csv. It contains a dataset with data about harvested crops in Mali with crop types, value of production, harvested areas, etc.
You also have created the connection and the related dataset that will hold the processed data.

Here, a dataset stored in the same S3 bucket.

Procedure

Click Add pipeline on the Pipelines page. Your new pipeline opens.
Give the pipeline a meaningful name.
Example
Process strings about harvested crops
Click ADD SOURCE to open the panel allowing you to select your source data, here data about harvested crops in Mali in the year 2005.
Example
Select your dataset and click Select in order to add it to the pipeline.
Rename it if needed.
Click and add a Strings processor to the pipeline. The configuration panel opens.
Give a meaningful name to the processor.
Example
change crop types to upper case
In the Configuration area:
1. Select Change to upper case in the Function name list.
2. Select .crop_parent in the Fields to process list, as you want to change the crop type values to upper case.
Click Save to save your configuration.

Look at the preview of the processor to compare your data before and after the operation.
Click and add another Strings processor to the pipeline. The configuration panel opens.
Give a meaningful name to the processor.
Example
match crop IDs with IDs
In the Configuration area:
1. Select Match similar text in the Function name list.
2. Select .crop in the Fields to process list.
3. Select Other column in the Use with list and .id in the Column list as you want to compare the crop name ID with the record ID.
4. Enter 0 in the Fuzziness field as you want exact matches between the two field values.
Click Save to save your configuration.

Look at the preview of the processor to compare your data before and after the operation. You can see a new column crop_matches in which exact matches have a true values and IDs that do not match have a false value.
Click ADD DESTINATION and select the dataset that will hold your processed data.
Rename it if needed.
On the top toolbar of Talend Cloud Pipeline Designer, click the Run button to open the panel allowing you to select your run profile.
Select your run profile in the list (for more information, see Run profiles), then click Run to run your pipeline.