How to work with Mistral AI Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Mistral AI, Spark can work with live Mistral AI data. This article describes how to connect to and query Mistral AI data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Mistral AI data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Mistral AI data using native data types.

Install the CData JDBC Driver for Mistral AI

Download the CData JDBC Driver for Mistral AI installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Mistral AI Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Mistral AI JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Mistral AI/lib/cdata.jdbc.api.jar
   

2. With the shell running, you can connect to Mistral AI with a JDBC URL and use the SQL Context load() function to read a table.

   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;
   

   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.

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

   Configure the connection to Mistral AI, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;").option("dbtable","AudioTranscriptions").option("driver","cdata.jdbc.api.APIDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Mistral AI data as a temporary table:

   scala> api_df.registerTable("audiotranscriptions")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> api_df.sqlContext.sql("SELECT , FROM AudioTranscriptions WHERE Model = voxtral-mini-latest").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Mistral AI in Apache Spark, you are able to perform fast and complex analytics on Mistral AI data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.