Process & Analyze Mistral AI 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 Mistral AI data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Mistral AI data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Mistral AI data. When you issue complex SQL queries to Mistral AI, the driver pushes supported SQL operations, like filters and aggregations, directly to Mistral AI 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 Mistral AI data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Mistral AI 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[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Mistral AI Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Mistral AI 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 Mistral AI, and create a basic report.

Configure the Connection to Mistral AI

Connect to Mistral AI by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the 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\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Mistral AI 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.

The MistralAI API uses API key authentication.

Using API Key Authentication

Your MistralAI API Key is required to create a connection to MistralAI. API Keys can be obtained from your MistralAI account at console.mistral.ai by navigating to the API Keys section. Once you have obtained the API key, set it in the ProfileSettings connection property.

Example Connection string

Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Mistral AI Data

Once you configure the connection, you can load Mistral AI 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" , "AudioTranscriptions") \
	.load ()

Display Mistral AI Data

Check the loaded Mistral AI data by calling the display function.

Step 3: Checking the result

display (remote_table.select (""))

👁 Displaying Mistral AI Data

Analyze Mistral AI 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 Mistral AI data for reporting, visualization, and analysis.

% sql

SELECT , FROM SAMPLE_VIEW ORDER BY DESC LIMIT 5

👁 Displaying Mistral AI Data

The data from Mistral AI 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 Mistral AI data in Databricks. Reach out to our Support Team if you have any questions.