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Access Amazon S3 Data in Anypoint Using SQL

Create a simple Mule Application that uses HTTP and SQL with the CData Mule Connector for Amazon S3 to create a JSON endpoint for Amazon S3 data.

The CData Mule Connector for Amazon S3 connects Amazon S3 data to Mule applications enabling read, write, update, and delete functionality with familiar SQL queries. The Connector allows users to easily create Mule Applications to backup, transform, report, and analyze Amazon S3 data.

This article demonstrates how to use the CData Mule Connector for Amazon S3 inside of a Mule project to create a Web interface for Amazon S3 data. The application created allows you to request Amazon S3 data using an HTTP request and have the results returned as JSON. The exact same procedure outlined below can be used with any CData Mule Connector to create a Web interface for the 160+ available data sources.

  1. Create a new Mule Project in Anypoint Studio.
  2. Add an HTTP Connector to the Message Flow.
  3. Configure the address for the HTTP Connector.
  4. Add a CData Amazon S3 Connector to the same flow, after the HTTP Connector.
  5. Create a new Connection (or edit an existing one) and configure the properties to connect to Amazon S3 (see below). Once the connection is configured, click Test Connection to ensure the connectivity to Amazon S3.

    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.

  6. Configure the CData Amazon S3 Connector.
    1. Set the Operation to 'Select with Streaming'.
    2. Set the Query type to Dynamic.
    3. Set the SQL query to SELECT * FROM #[message.inboundProperties.'http.query.params'.get('table')] to parse the URL parameter table and use it as the target of the SELECT query. You can customize the query further by referencing other potential URL parameters.
  7. Add a Transform Message Component to the flow.
    1. Map the Payload from the input to the Map in the output.
    2. Set the Output script to the following to convert the payload to JSON:
      %dw 1.0
      %output application/json
      ---
      payload
              
  8. To view your Amazon S3 data, navigate to the address you configured for the HTTP Connector (localhost:8081 by default) and pass a table name as the table URL parameter: http://localhost:8081?table=ObjectsACL
    The ObjectsACL data is available as JSON in your Web browser and any other tools capable of consuming JSON endpoints.

At this point, you have a simple Web interface for working with Amazon S3 data (as JSON data) in custom apps and a wide variety of BI, reporting, and ETL tools. Download a free, 30 day trial of the Mule Connector for Amazon S3 and see the CData difference in your Mule Applications today.