Process & Analyze FedEx Data in Azure Databricks

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FedEx JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with FedEx data including Packages, Shipments, Recipients, and more!



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

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

Install the CData JDBC Driver in Azure

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

Connect to FedEx from Databricks

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

Configure the Connection to FedEx

Connect to FedEx by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK 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.fedex.FedExDriver"
url = "jdbc:fedex:RTK=5246...;Server='https://gatewaybeta.fedex.com:443/xml';DeveloperKey='alsdkfjpqoewiru';Password='zxczxqqtyiuowkdlkn';AccountNumber='110371337';MeterNumber='240134349';
PrintLabelLocation='C:/users/username/documents/mylabels';CacheProvider='org.sqlite.JDBC';CacheConnection='jdbc:sqlite:C:/users/username/documents/fedexcache.db';"

Built-in Connection String Designer

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

java -jar cdata.jdbc.fedex.jar

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

There are five pieces of information needed in order to authenticate its actions with the FedEx service. This information is below.

  • Server: This controls the URL where the requests should be sent. Common testing options for this are: "https://gatewaybeta.fedex.com:443/xml", "https://wsbeta.fedex.com:443/xml", "https://gatewaybeta.fedex.com:443/web-service", and "https://wsbeta.fedex.com:443/web-service"
  • DeveloperKey: This is the identifier part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • Password: This is the secret part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • AccountNumber: This valid 9-digit FedEx account number is used for logging into the FedEx server.
  • MeterNumber: This value is used for submitting requests to FedEx. This value will be provided to you by FedEx after registration.
  • PrintLabelLocation: This property is required if one intends to use the GenerateLabels or GenerateReturnLabels stored procedures. This should be set to the folder location where generated labels should be stored.

The Cache Database

Many of the useful tasks available from FedEx require a lot of data. To ensure this data is easy to input and recall later, utilizes a cache database to make these requests. You must set the cache connection properties:

  • CacheProvider: The specific database you are using to cache with. For example, org.sqlite.JDBC.
  • CacheConnection: The connection string to be passed to the cache provider. For example, jdbc:sqlite:C:/users/username/documents/fedexcache.db

Load FedEx Data

Once the connection is configured, you can load FedEx 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" , "Senders") \
	.load ()

Display FedEx Data

Check the loaded FedEx data by calling the display function.

display (remote_table.select ("FirstName"))

Analyze FedEx 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 FedEx data for analysis.

% sql

SELECT FirstName, Phone FROM Senders WHERE SenderID = ab26f704-5edf-4a9f-9e4c-25

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