ETL CSV in Oracle Data Integrator
Leverage existing skills by using the JDBC standard to connect to CSV: Through drop-in integration into ETL tools like Oracle Data Integrator (ODI), the CData JDBC Driver for CSV connects real-time CSV data to your data warehouse, business intelligence, and Big Data technologies.
JDBC connectivity enables you to work with CSV just as you would any other database in ODI. As with an RDBMS, you can use the driver to connect directly to the CSV APIs in real time instead of working with flat files.
This article covers a JDBC-based ETL -- CSV to Oracle. After reverse engineering a data model of CSV entities, you will create a mapping and select a data loading strategy -- since the driver supports SQL-92, this last step can easily be accomplished by selecting the built-in SQL to SQL Loading Knowledge Module.
Install the Driver
To install the driver, copy the driver JAR (cdata.jdbc.csv.jar) and .lic file (cdata.jdbc.csv.lic), located in the installation folder, into the ODI appropriate directory:
- UNIX/Linux without Agent: ~/.odi/oracledi/userlib
- UNIX/Linux with Agent: ~/.odi/oracledi/userlib and $ODI_HOME/odi/agent/lib
- Windows without Agent: %APPDATA%\Roaming\odi\oracledi\userlib
- Windows with Agent: %APPDATA%\odi\oracledi\userlib and %APPDATA%\odi\agent\lib
Restart ODI to complete the installation.
Reverse Engineer a Model
Reverse engineering the model retrieves metadata about the driver's relational view of CSV data. After reverse engineering, you can query real-time CSV data and create mappings based on CSV tables.
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In ODI, connect to your repository and click New -> Model and Topology Objects.
- On the Model screen of the resulting dialog, enter the following information:
- Name: Enter CSV.
- Technology: Select Generic SQL (for ODI Version 12.2+, select Microsoft SQL Server).
- Logical Schema: Enter CSV.
- Context: Select Global.
- On the Data Server screen of the resulting dialog, enter the following information:
- Name: Enter CSV.
- Driver List: Select Oracle JDBC Driver.
- Driver: Enter cdata.jdbc.csv.CSVDriver
- URL: Enter the JDBC URL containing the connection string.
Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) CSV Files
CData Drivers let you work with CSV files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.
Setting connection properties for local files
Set the URI property to local folder path.
Setting connection properties for files stored in Amazon S3
To connect to CSV file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended CSV files exist. In addition, at least set these properties:
- AWSAccessKey: AWS Access Key (username)
- AWSSecretKey: AWS Secret Key
Setting connection properties for files stored in Box
To connect to CSV file(s) within Box, set the URI property to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect to Box.
Dropbox
To connect to CSV file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.
SharePoint Online (SOAP)
To connect to CSV file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended CSV file. Set User, Password, and StorageBaseURL.
SharePoint Online REST
To connect to CSV file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended CSV file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.
Google Drive
To connect to CSV file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.
Built-in Connection String Designer
For assistance in constructing the JDBC URL, use the connection string designer built into the CSV JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.csv.jarFill in the connection properties and copy the connection string to the clipboard.
Below is a typical connection string:
jdbc:csv:URI=/PATH/TO/MyCSVFilesFolder;
- On the Physical Schema screen, enter the following information:
- Name: Select from the Drop Down menu.
- Database (Catalog): Enter CData.
- Owner (Schema): If you select a Schema for CSV, enter the Schema selected, otherwise enter CSV.
- Database (Work Catalog): Enter CData.
- Owner (Work Schema): If you select a Schema for CSV, enter the Schema selected, otherwise enter CSV.
- In the opened model click Reverse Engineer to retrieve the metadata for CSV tables.
Edit and Save CSV Data
After reverse engineering you can now work with CSV data in ODI.
To view CSV data, expand the Models accordion in the Designer navigator, right-click a table, and click View data.
Create an ETL Project
Follow the steps below to create an ETL from CSV. You will load Customer entities into the sample data warehouse included in the ODI Getting Started VM.
Open SQL Developer and connect to your Oracle database. Right-click the node for your database in the Connections pane and click new SQL Worksheet.
Alternatively you can use SQLPlus. From a command prompt enter the following:
sqlplus / as sysdba- Enter the following query to create a new target table in the sample data warehouse, which is in the ODI_DEMO schema. The following query defines a few columns that match the Customer table in CSV:
CREATE TABLE ODI_DEMO.TRG_CUSTOMER (TOTALDUE NUMBER(20,0),City VARCHAR2(255)); - In ODI expand the Models accordion in the Designer navigator and double-click the Sales Administration node in the ODI_DEMO folder. The model is opened in the Model Editor.
- Click Reverse Engineer. The TRG_CUSTOMER table is added to the model.
- Right-click the Mappings node in your project and click New Mapping. Enter a name for the mapping and clear the Create Empty Dataset option. The Mapping Editor is displayed.
- Drag the TRG_CUSTOMER table from the Sales Administration model onto the mapping.
- Drag the Customer table from the CSV model onto the mapping.
- Click the source connector point and drag to the target connector point. The Attribute Matching dialog is displayed. For this example, use the default options. The target expressions are then displayed in the properties for the target columns.
- Open the Physical tab of the Mapping Editor and click CUSTOMER_AP in TARGET_GROUP.
- In the CUSTOMER_AP properties, select LKM SQL to SQL (Built-In) on the Loading Knowledge Module tab.
You can then run the mapping to load CSV data into Oracle.