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Process & Analyze CSV Data in Azure Databricks

Host the CData JDBC Driver for CSV in Azure and use Databricks to perform data engineering and data science on live CSV 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 CSV data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live CSV data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live CSV 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.csv.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for CSV\lib).

Connect to Salesforce from Databricks

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

Configure the Connection to CSV

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

driver = "cdata.jdbc.csv.CSVDriver"
url = "jdbc:csv:DataSource=MyCSVFilesFolder;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.csv.jar

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

The DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

Load CSV Data

Once the connection is configured, you can load CSV 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" , "Customer") \
	.load ()

Display CSV Data

Check the loaded CSV data by calling the display function.

display (remote_table.select ("City"))

Analyze CSV 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 CSV data for analysis.

% sql

SELECT City, SUM(TotalDue) FROM Customer GROUP BY City

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