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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 explains how to host the CData JDBC Driver in AWS, as well as connect to and process 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.
To work with live MariaDB data in Databricks, install the driver on your Databricks cluster.
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.
Connect to MariaDB 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.
driver = "cdata.jdbc.mariadb.MariaDBDriver" url = "jdbc:mariadb:RTK=5246...;User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;"
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.
๐ Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)Once you configure the connection, 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 ()
Check the loaded MariaDB data by calling the display function.
display (remote_table.select ("ShipName"))
๐ Displaying MariaDB DataIf you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.
remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )
With the Temp View created, you can use SparkSQL to retrieve the MariaDB data for reporting, visualization, and analysis.
% sql SELECT ShipName, ShipCity FROM SAMPLE_VIEW ORDER BY ShipCity DESC LIMIT 5๐ Displaying MariaDB Data
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 Databricks. Reach out to our Support Team if you have any questions.
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