Export data to Amazon Redshift
Zoho DataPrep supports importing data from Amazon Redshift, a cloud database. It is a fully managed, petabyte-scale data warehouse service in the cloud.
Export data to Amazon Redshift
1. Click the Export now option from the Export menu on the DataPrep Studio page.
2. You can select Oracle Cloud from the Cloud databases category or search it in the search box.
Note: If you have already added a Amazon Redshift connection earlier, you can simply select the existing connection under the Saved connections section and proceed with exporting.
3. If your data contains columns with personal data, you can choose to include or exclude some or all of them in the PII columns section.
You can also apply the necessary security methods below to protect your data column:
A. Data masking
Data masking hides original data with 'x' to protect personal information.
B. Data Tokenization
Data tokenization replaces each distinct value in your data with a random value. Hence the output is statistically identical to the original data.
C. None
You can select None if you do not want to use any security method.
4. Click Next and select Amazon Redshift in the Database service name dropdown.
5. Enter the Endpoint, Port, Database name, Username and Password to authenticate the database connection.
6. You can also select the Use SSL checkbox if your database server has been setup to serve encrypted data through SSL
7. Enter a unique name for your connection under Connection name and click Connect.
Note: The connection configuration will be saved for exporting data in the future. Credentials are securely encrypted and stored.
Note: If you face trouble connecting to your database, please make sure Zoho DataPrep's IP Addresses are whitelisted in your application to export data to cloud databases. Click here to know about Zoho DataPrep IP addresses.
8. Once you have successfully connected to your Amazon Redshift account, you can choose how and where to export the data.
9. Choose Existing table if you want to export data to an existing table and select one from the list of tables available in the database.
10. If you select the Existing table option, there are two ways in which you can choose how to export the new rows from your dataset in DataPrep to the table in your database.
- If the new rows are to be added to the table, choose Append.
- If the newly added rows are to replace the existing rows, select Overwrite from the dropdown.
If you select the Existing table option,
a. Click Export to view the Export summary. The summary consists of details such as the destination, target table, number of records to be exported, and the target match check result.
b. If the target match check fails, you need to fix the errors by completing the target matching steps. If the target match check is passed, you can proceed with exporting your data to the required cloud database.
Info : Target matching is a useful feature in DataPrep that prevents export failures caused due to errors from the data model mismatch. Learn more about target matching.
11. If you want to create a new table and export data, select the New table option, enter the Schema name, Table name and proceed to exporting.
Note: Schema name is not a mandatory field.
12. Click Export.
Target matching during export to cloud databases
Target matching happens before the data is exported to the destination. Target matching is a useful feature in DataPrep that prevents export failures caused due to errors from the data model mismatch. Using target matching, you can set the required cloud database table as the target and align the source dataset columns to match with your target table. This ensures seamless export of high quality data to the cloud databases.
Note: Target matching failure is not an export failure. Target matching happens before the data is actually exported to the destination. This way the schema or data model errors that could cause export to fail are caught beforehand preventing export failures. Learn more about target matching.
When target match check fails
1. If the target match check fails during cloud database export, you can click the View error details link from the Export summary pane to get an overview of the errors.
2. The Target match errors panel shows the different model match errors and the number of columns associated with each error.
Pro Tip: The default view shows only the error columns, but you can always uncheck the Show only error checkbox to view all the columns.
The errors in target matching are explained below:
-
Unmatched columns : This option shows all the unmatched columns in the source and target datasets.
Note:
- The non-mandatory columns in the target can either be matched with a source column if available or ignored.
- The columns in the source that are missing in the target need to be matched or removed to proceed exporting.
When using the unmatched columns option, you can toggle the Show only mandatory columns option to see if there are any mandatory columns(set as mandatory in the target) and include them. You can also fix only the mandatory columns and proceed to exporting.
- Data type mismatch : This option displays the columns from the source dataset having data types that do not match the columns in the target.
- Data format mismatch : This option displays columns from the source dataset having date, datetime and time formats that differ from those in the target dataset.
- Constraint mismatch : This option displays the columns that do not match the data type constraints of the columns in the target. To know how to add constraints for a column, click here.
-
Mandatory column mismatch: This option displays the columns that are set as mandatory in the target but not set as mandatory in your source dataset.
Note: The mandatory columns cannot be exported to the destination unless they are matched and set as mandatory. You can click the
icon above the column to set it as mandatory. You can also use the Set as mandatory (not null) check box under the Change data type transform to set a column as mandatory.
- Data size overflow warnings : This option filters the columns with data exceeding the maximum size allowed in the target.
Note: The Data size overflow warning is only applicable to database targets.
3. You can return to your dataset to fix the column-level issues shown in the Target match errors pane using the Fix errors button.
To make it easier for you to fix the errors, the target module in your cloud database is attached as a target to your dataset. You can view the mapping of your dataset with the table in the DataPrep Studio page along with the errors wherever there is a mismatch. You can hover over the error icons to understand the issue and click on them to resolve each error.
- The Target match errors section shows the errors and the number of columns associated with each error.
- The section at the top lists the error categories along with the number of errors in each category.
- You can click them to filter errors related to each category in the panel.
- In the default view, all columns are displayed. However, you can click any error category and get a closer look at the columns or view the error columns alone by selecting the Show only errors checkbox.
- Your filter selection in the Target match errors panel will also be applied on the grid in the DataPrep Studio page.
4. After fixing the errors you can proceed with exporting your data to cloud database.
6. You can also choose to schedule the export using the Schedule this export? option.
To schedule export,
- Select a Repeat method (Every 'N' hours, Every day, Weekly once, Monthly once) and set frequency using Perform every dropdown.
- You can also select the Time zone to export data. By default, your local timezone will be selected and click Save .
Click here to know more about Schedule export.
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