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Get the Report →How to connect and process Tally Data from Azure Databricks
Use CData, Azure, and Databricks to perform data engineering and data science on live Tally 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 Tally data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Tally data in Databricks.
With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Tally data. When you issue complex SQL queries to Tally, the driver pushes supported SQL operations, like filters and aggregations, directly to Tally 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 Tally data using native data types.
Install the CData JDBC Driver in Azure
To work with live Tally data in Databricks, install the driver on your Azure cluster.
- Navigate to your Databricks administration screen and select the target cluster.
- On the Libraries tab, click "Install New."
- Select "DBFS" as the Library Source and "Jar" as the Library Type.
- Upload the JDBC JAR file (cdata.jdbc.tally.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).
Connect to Tally from Databricks
With the JAR file installed, we are ready to work with live Tally data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).
Configure the Connection to Tally
Connect to Tally 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.tally.TallyDriver" url = "jdbc:tally:RTK=5246...;Url='http://localhost:9000'"
Built-in Connection String Designer
For assistance in constructing the JDBC URL, use the connection string designer built into the Tally JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.tally.jar
Fill in the connection properties and copy the connection string to the clipboard.
Set the following connection properties to connect to Tally Instance:
- Url: Set this to the URL for your Tally instance. For example: http://localhost:9000.
Load Tally Data
Once the connection is configured, you can load Tally 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" , "Company") \ .load ()
Display Tally Data
Check the loaded Tally data by calling the display function.
display (remote_table.select ("Name"))
Analyze Tally 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 Tally data for analysis.
result = spark.sql("SELECT Name, Address FROM SAMPLE_VIEW WHERE SAMPLE_VIEWNumber = '1000'")
The data from Tally 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 Tally and start working with your live Tally data in Azure Databricks. Reach out to our Support Team if you have any questions.