Process & Analyze Sage Cloud Accounting Data in Databricks (AWS)

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Sage Cloud Accounting JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Sage Cloud Accounting.



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

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

Install the CData JDBC Driver in Databricks

To work with live Sage Cloud Accounting 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.sagebcaccounting.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Sage Cloud Accounting\lib).

Access Sage Cloud Accounting Data in your Notebook: Python

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

Configure the Connection to Sage Cloud Accounting

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

Step 1: Connection Information

driver = "cdata.jdbc.sagebcaccounting.SageBCAccountingDriver"
url = "jdbc:sagebcaccounting:InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.sagebcaccounting.jar

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

You can connect to Sage Business Cloud Accounting using the embedded OAuth connectivity. When you connect, the OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

Load Sage Cloud Accounting Data

Once you configure the connection, you can load Sage Cloud Accounting 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" , "SalesInvoices") \
	.load ()

Display Sage Cloud Accounting Data

Check the loaded Sage Cloud Accounting data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("contact_name"))

Analyze Sage Cloud Accounting 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 Sage Cloud Accounting data for reporting, visualization, and analysis.

% sql

SELECT contact_name, total_amount FROM SAMPLE_VIEW ORDER BY total_amount DESC LIMIT 5

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