Export data to cloud databases
You can export data to the following cloud databases.
- Amazon RDS - MySQL
- Amazon RDS - MS SQL Server
- Amazon RDS - Oracle
- Amazon RDS - PostgreSQL
- Amazon RDS - Maria DB
- Amazon RDS - Amazon Aurora MySQL
- Amazon RDS - Amazon Aurora PostgreSQL
- Amazon Redshift
- Microsoft Azure-MySQL
- Microsoft Azure - PostgreSQL
- Microsoft Azure - Maria DB
- Microsoft Azure - SQL Database
- Microsoft Azure - SQL Data Warehouse
- Google Cloud SQL - MySQL
- Google Cloud SQL - PostgreSQL
- Snowflake
- Oracle Cloud
- IBM Cloud - DB2
- Heroku PostgreSQL
- Rackspace Cloud - MySQL
- Rackspace Cloud - Maria DB
- Panoply
- MySQL
- MS SQL Server
- Oracle
- PostgreSQL
- Maria DB
- MemSQL
- DB2
To export data to cloud databases
1. Click the Export now option from the Export menu in the Studio page.
2. You can type in the destination name in the search box or choose the Cloud databases category and the required export option.
Note: If you have already added a cloud database connection, 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 or ePHI data, you can choose which columns need to be exported from the Columns section.
You can also apply the necessary security methods to protect your personal 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. You can choose whether to export these column using the corresponding check boxes.
4. Click Next and select your database service name, database type, enter the values in the required fields such as endpoint and database name to configure the cloud database connection.
5. You can also provide a username and password if the database connection is to be authenticated.
6. Enter a unique connection name.
7. You can also select the Use SSL check box if your database server has been setup to serve encrypted data through SSL.
8. Click the Connect button.
9. Once you have successfully connected to your cloud database, you can choose how and where to export the data.
10. 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. If you select the existing table option, there are two ways in which you can choose how to add the new rows to the table.
- 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.
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 filters the columns in the source dataset that do not match the columns in the target.
Info : When using the unmatched columns filter, you can toggle the Show only mandatory columns to verify if there are any mandatory columns required and include them.
- Data type mismatch : This option filters the columns from the source dataset having data types that do not match the columns in the target.
- Data format mismatch : This option filters columns from the source dataset having data formats that differ from those in the target dataset.
- Constraint mismatch : This option filters 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 columns mismatch : This option filters the columns that are mandatorily required in your target.
- Data size overflow warnings : This option filters the columns with data exceeding the maximum size allowed in the target.
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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