Connect to Vertica for JDBC From pyspark azure synapse analytics

Question

I have a requirement to connect Vertica via jdbc from an Azure Synapse analytics notebook using pyspark. I would like to know how I can achieve this?

I made several tutorials from other forums that I found online but I have not had success. But I also didn't find a specific tutorial for synapse analysis.

Best Regards!

Answer 1

The Vertica Spark connector source API supports both parallel write and read operations :https://www.vertica.com/docs/10.1.x/HTML/Content/Authoring/SparkConnector/LoadingVerticaDataToSparkUsingVerticaDataSource.htm

Or in an easier way :

https://learn.microsoft.com/en-us/azure/data-factory/connector-vertica?tabs=data-factory

Using Pyspark :

Start by importing the necessary PySpark libraries.

    from pyspark.sql import SparkSession

Create a Spark session and include the Vertica JDBC driver in the session. You may need to download the Vertica JDBC driver and upload it to Azure Synapse or reference it if it's already available in your environment.

 spark = SparkSession.builder \
        .appName("Vertica Connection Example") \
        .config("spark.jars", "/path/to/vertica-jdbc.jar") \
        .getOrCreate()

Define the connection properties for connecting to the Vertica database :

    vertica_url = "jdbc:vertica://YourVerticaHost:5433/YourDatabase"
    connection_properties = {
        "user": "YourUsername",
        "password": "YourPassword",
        "driver": "com.vertica.jdbc.Driver"
    }

Use the Spark session to read data from Vertica. You can specify the table or query you want to load.

    df = spark.read.jdbc(url=vertica_url, table="YourTableName", properties=connection_properties)

#If you need to read data :
    df.show()
#If you need to write data back to Vertica, you can use the `write` method.
    df.write.jdbc(url=vertica_url, table="YourTargetTable", properties=connection_properties, mode="append")
#Once all operations are complete, close the Spark session.
    spark.stop()