Build Amazon S3-Connected ETL Processes in Google Data Fusion

Google Data Fusion allows users to perform self-service data integration to consolidate disparate data. Uploading the CData JDBC Driver for Amazon S3 enables users to access live Amazon S3 data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Amazon S3 data to any data source natively supported in Google Data Fusion, this article walks through piping data from Amazon S3 to Google BigQuery,

Upload the CData JDBC Driver for Amazon S3 to Google Data Fusion

Upload the CData JDBC Driver for Amazon S3 to your Google Data Fusion instance to work with live Amazon S3 data. Due to the naming restrictions for JDBC drivers in Google Data Fusion, create a copy or rename the JAR file to match the following format driver-version.jar. For example: cdataamazons3-2020.jar

1. Open your Google Data Fusion instance
2. Click the to add an entity and upload a driver
3. On the "Upload driver" tab, drag or browse to the renamed JAR file.
4. On the "Driver configuration" tab:
   • Name: Create a name for the driver (cdata.jdbc.amazons3) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.amazons3.AmazonS3Driver)
5. Click "Finish"

Connect to Amazon S3 Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Amazon S3 data in Google Data Fusion Pipelines.

1. Navigate to the Pipeline Studio to create a new Pipeline
2. From the "Source" options, click "Database" to add a source for the JDBC Driver
3. Click "Properties" on the Database source to edit the properties

   NOTE: To use the JDBC Driver in Google Data Fusion, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

   • Set the Label
   • Set Reference Name to a value for any future references (i.e.: cdata-amazons3)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Amazon S3. For example:
     
     jdbc:amazons3:RTK=5246...;AccessKey=a123;SecretKey=s123;

     To authorize Amazon S3 requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

     Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

     For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

     Built-in Connection String Designer

     For assistance in constructing the JDBC URL, use the connection string designer built into the Amazon S3 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

     java -jar cdata.jdbc.amazons3.jar

     Fill in the connection properties and copy the connection string to the clipboard.

   • Set Import Query to a SQL query that will extract the data you want from Amazon S3, i.e.:
     SELECT * FROM ObjectsACL
4. From the "Sink" tab, click to add a destination sink (we use Google BigQuery in this example)
5. Click "Properties" on the BigQuery sink to edit the properties
   • Set the Label
   • Set Reference Name to a value like amazons3-bigquery
   • Set Project ID to a specific Google BigQuery Project ID (or leave as the default, "auto-detect")
   • Set Dataset to a specific Google BigQuery dataset
   • Set Table to the name of the table you wish to insert Amazon S3 data into

With the Source and Sink configured, you are ready to pipe Amazon S3 data into Google BigQuery. Save and deploy the pipeline. When you run the pipeline, Google Data Fusion will request live data from Amazon S3 and import it into Google BigQuery.

While this is a simple pipeline, you can create more complex Amazon S3 pipelines with transforms, analytics, conditions, and more. Download a free, 30-day trial of the CData JDBC Driver for Amazon S3 and start working with your live Amazon S3 data in Google Data Fusion today.