Process & Analyze Alfresco Data in Databricks (AWS)

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Alfresco JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Alfresco data including Files, Folders, Users, Groups, Sites, Tags, and more!



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

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

Install the CData JDBC Driver in Databricks

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

Access Alfresco Data in your Notebook: Python

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

Configure the Connection to Alfresco

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

Step 1: Connection Information

driver = "cdata.jdbc.alfresco.AlfrescoDriver"
url = "jdbc:alfresco:User=MyUsername; Password=MyPassword; Format=Solr; InstanceUrl=api-explorer.alfresco.com;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.alfresco.jar

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

To connect to Alfresco, the following connection properties must be supplied: User, Password, and InstanceUrl. User and Password correspond to the login credentials that you use to access Alfresco in a web browser. InstanceUrl corresponds to the Alfresco instance you will be querying. For instance, if you expect your queries to hit https://search-demo.dev.alfresco.me/alfresco/api/-default-/public/search/versions/1/sql, you should supply search-demo.dev.alfresco.me for InstanceUrl.

Load Alfresco Data

Once you configure the connection, you can load Alfresco 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" , "Alfresco") \
	.load ()

Display Alfresco Data

Check the loaded Alfresco data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("DBID"))

Analyze Alfresco 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 Alfresco data for reporting, visualization, and analysis.

% sql

SELECT DBID, Column1 FROM SAMPLE_VIEW ORDER BY Column1 DESC LIMIT 5

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