Process & Analyze Alchemy Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Alchemy data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Alchemy data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Alchemy data. When you issue complex SQL queries to Alchemy, the driver pushes supported SQL operations, like filters and aggregations, directly to Alchemy and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Alchemy data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Alchemy data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.api.jar) from the installation location (typically C:\Program Files\CData\CData API Driver for JDBC\lib).

Access Alchemy Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Alchemy data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Alchemy, and create a basic report.

Configure the Connection to Alchemy

Connect to Alchemy by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Alchemy.apip;AuthScheme=APIKey;Network=eth-mainnet;ProfileSettings='APIKey=your_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.

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.

Load Alchemy Data

Once you configure the connection, you can load Alchemy data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "ContractsForOwner") \
	.load ()

Display Alchemy Data

Check the loaded Alchemy data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Address"))

Analyze Alchemy Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Alchemy data for reporting, visualization, and analysis.

% sql

SELECT Address, Name FROM SAMPLE_VIEW ORDER BY Name DESC LIMIT 5

The data from Alchemy is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Alchemy data in Databricks. Reach out to our Support Team if you have any questions.