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Process & Analyze MariaDB Data in Azure Databricks

Host the CData JDBC Driver for MariaDB in Azure and use Databricks to perform data engineering and data science on live MariaDB data.

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 MariaDB data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live MariaDB data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live MariaDB data in Databricks, install the driver on your Azure 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.mariadb.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for MariaDB\lib).

Connect to MariaDB from Databricks

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

Configure the Connection to MariaDB

Connect to MariaDB by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL.

driver = "cdata.jdbc.mariadb.MariaDBDriver"
url = "jdbc:mariadb:User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.mariadb.jar

Fill in the connection properties and copy the connection string to the clipboard.

The Server and Port properties must be set to a MariaDB server. If IntegratedSecurity is set to false, then User and Password must be set to valid user credentials. Optionally, Database can be set to connect to a specific database. If not set, the tables from all databases are reported.

Load MariaDB Data

Once the connection is configured, you can load MariaDB data as a dataframe using the CData JDBC Driver and the connection information.

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

Display MariaDB Data

Check the loaded MariaDB data by calling the display function.

display (remote_table.select ("ShipName"))

Analyze MariaDB Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the MariaDB data for analysis.

% sql

SELECT ShipName, ShipCity FROM Orders

The data from MariaDB 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 JDBC Driver for MariaDB and start working with your live MariaDB data in Apache NiFi. Reach out to our Support Team if you have any questions.