Build Alchemy-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 API Driver for JDBC enables users to access live Alchemy data from within their Google Data Fusion pipelines. While the CData JDBC Driver enables piping Alchemy data to any data source natively supported in Google Data Fusion, this article explains how to pipe data from Alchemy to Google BigQuery,

Upload the CData API Driver for JDBC to Google Data Fusion

Upload the CData API Driver for JDBC to your Google Data Fusion instance to work with live Alchemy 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: cdataapi-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.api) and make note of the name
   • Class name: Set the JDBC class name: (cdata.jdbc.api.APIDriver)
   
5. Click "Finish"

Connect to Alchemy Data in Google Data Fusion

With the JDBC Driver uploaded, you are ready to work with live Alchemy 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-api)
   • Set Plugin Type to "jdbc"
   • Set Connection String to the JDBC URL for Alchemy. For example:
     
     jdbc:api:RTK=5246...;Profile=C:\profiles\Alchemy.apip;AuthScheme=APIKey;Network=eth-mainnet;ProfileSettings='APIKey=your_api_key';

     Start by setting the Profile connection property to the location of the Alchemy Profile on disk (e.g. C:\profiles\Alchemy.apip). Next, set the ProfileSettings connection property to the connection string for Alchemy (see below).

     Alchemy API Profile Settings

     Alchemy uses API key authentication. The API key is supplied in the request URL path. To obtain an API key:

     1. Sign in to your Alchemy account at https://dashboard.alchemy.com.
     2. Open an existing app or create a new one.
     3. Copy the app's API key from the API Key dialog.

     After obtaining your API key, set the following connection properties:

     • AuthScheme: Set this to APIKey.
     • Network: Optional. The blockchain network slug for NFT API requests (for example, eth-mainnet, base-mainnet, polygon-mainnet). Defaults to eth-mainnet.

     Set the following in the ProfileSettings connection property:

     • APIKey: Set this to your Alchemy app API key.

     Built-in Connection String Designer

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

     
           java -jar cdata.jdbc.api.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 Alchemy, i.e.:
     SELECT * FROM ContractsForOwner
   
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 api-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 Alchemy data into
   

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

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