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Get the Report →Process & Analyze Klipfolio Data in Databricks (AWS)
Use CData, AWS, and Databricks to perform data engineering and data science on live Klipfolio 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 Klipfolio data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Klipfolio data in Databricks.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Klipfolio data. When you issue complex SQL queries to Klipfolio, the driver pushes supported SQL operations, like filters and aggregations, directly to Klipfolio 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 Klipfolio data using native data types.
Install the CData JDBC Driver in Databricks
To work with live Klipfolio data in Databricks, install the driver on your Databricks cluster.
- Navigate to your Databricks administration screen and select the target cluster.
- On the Libraries tab, click "Install New."
- Select "Upload" as the Library Source and "Jar" as the Library Type.
- Upload the JDBC JAR file (cdata.jdbc.api.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).
Access Klipfolio Data in your Notebook: Python
With the JAR file installed, we are ready to work with live Klipfolio 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 Klipfolio, and create a basic report.
Configure the Connection to Klipfolio
Connect to Klipfolio by referencing the JDBC Driver class 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.
Step 1: Connection Information
driver = "cdata.jdbc.api.APIDriver" url = "jdbc:api:RTK=5246...;Profile=C:\profiles\Klipfolio.apip;ProfileSettings='APIKey=your_api_key';"
Built-in Connection String Designer
For assistance in constructing the JDBC URL, use the connection string designer built into the Klipfolio JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.api.jar
Fill in the connection properties and copy the connection string to the clipboard.
Start by setting the Profile connection property to the location of the Klipfolio Profile on disk (e.g. C:\profiles\Klipfolio.apip). Next, set the ProfileSettings connection property to the connection string for Klipfolio (see below).
Klipfolio API Profile Settings
In order to authenticate to Klipfolio, you'll need to provide your API Key. You can generate an API key from the Klipfolio Dashboard app through either the My Profile page or from Users if you are an administrator (you must have the user.manage permission). Set the API Key in the ProfileSettings property to connect.
Load Klipfolio Data
Once you configure the connection, you can load Klipfolio data as a dataframe using the CData JDBC Driver and the connection information.
Step 2: Reading the data
remote_table = spark.read.format ( "jdbc" ) \ .option ( "driver" , driver) \ .option ( "url" , url) \ .option ( "dbtable" , "DataSources") \ .load ()
Display Klipfolio Data
Check the loaded Klipfolio data by calling the display function.
Step 3: Checking the result
display (remote_table.select ("Id"))
Analyze Klipfolio Data in Databricks
If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.
Step 4: Create a view or table
remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )
With the Temp View created, you can use SparkSQL to retrieve the Klipfolio data for reporting, visualization, and analysis.
% sql SELECT Id, Name FROM SAMPLE_VIEW ORDER BY Name DESC LIMIT 5
The data from Klipfolio 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 API Driver for JDBC and start working with your live Klipfolio data in Databricks. Reach out to our Support Team if you have any questions.