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Get the Report →Process & Analyze Parquet Data in Databricks (AWS)
Use CData, AWS, and Databricks to perform data engineering and data science on live Parquet 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 Parquet data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Parquet data in Databricks.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Parquet data. When you issue complex SQL queries to Parquet, the driver pushes supported SQL operations, like filters and aggregations, directly to Parquet 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 Parquet data using native data types.
Install the CData JDBC Driver in Databricks
To work with live Parquet 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.parquet.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).
Access Parquet Data in your Notebook: Python
With the JAR file installed, we are ready to work with live Parquet 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 Parquet, and create a basic report.
Configure the Connection to Parquet
Connect to Parquet 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.parquet.ParquetDriver" url = "jdbc:parquet:RTK=5246...;URI=C:/folder/table.parquet;"
Built-in Connection String Designer
For assistance in constructing the JDBC URL, use the connection string designer built into the Parquet JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.parquet.jar
Fill in the connection properties and copy the connection string to the clipboard.
Connect to your local Parquet file(s) by setting the URI connection property to the location of the Parquet file.
Load Parquet Data
Once you configure the connection, you can load Parquet 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" , "SampleTable_1") \ .load ()
Display Parquet Data
Check the loaded Parquet data by calling the display function.
Step 3: Checking the result
display (remote_table.select ("Id"))
Analyze Parquet 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 Parquet data for reporting, visualization, and analysis.
% sql SELECT Id, Column1 FROM SAMPLE_VIEW ORDER BY Column1 DESC LIMIT 5
The data from Parquet 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 Parquet and start working with your live Parquet data in Databricks. Reach out to our Support Team if you have any questions.