Analyze Azure Data Catalog Data in R

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Azure Data Catalog JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Azure Data Catalog.



Use standard R functions and the development environment of your choice to analyze Azure Data Catalog data with the CData JDBC Driver for Azure Data Catalog.

Access Azure Data Catalog data with pure R script and standard SQL on any machine where R and Java can be installed. You can use the CData JDBC Driver for Azure Data Catalog and the RJDBC package to work with remote Azure Data Catalog data in R. By using the CData Driver, you are leveraging a driver written for industry-proven standards to access your data in the popular, open-source R language. This article shows how to use the driver to execute SQL queries to Azure Data Catalog and visualize Azure Data Catalog data by calling standard R functions.

Install R

You can match the driver's performance gains from multi-threading and managed code by running the multithreaded Microsoft R Open or by running open R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This snapshot ensures reproducibility.

Load the RJDBC Package

To use the driver, download the RJDBC package. After installing the RJDBC package, the following line loads the package:

library(RJDBC)

Connect to Azure Data Catalog as a JDBC Data Source

You will need the following information to connect to Azure Data Catalog as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.azuredatacatalog.AzureDataCatalogDriver
  • Classpath: Set this to the location of the driver JAR. By default this is the lib subfolder of the installation folder.

The DBI functions, such as dbConnect and dbSendQuery, provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Azure Data Catalog:

driver <- JDBC(driverClass = "cdata.jdbc.azuredatacatalog.AzureDataCatalogDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.azuredatacatalog.jar", identifier.quote = "'")

You can now use DBI functions to connect to Azure Data Catalog and execute SQL queries. Initialize the JDBC connection with the dbConnect function.

You can optionally set the following to read the different catalog data returned from Azure Data Catalog.

    CatalogName: Set this to the CatalogName associated with your Azure Data Catalog. To get your Catalog name, navigate to your Azure Portal home page > Data Catalog > Catalog Name

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

Built-in Connection String Designer

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

java -jar cdata.jdbc.azuredatacatalog.jar

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

Below is a sample dbConnect call, including a typical JDBC connection string:

conn <- dbConnect(driver,"jdbc:azuredatacatalog:InitiateOAuth=GETANDREFRESH")

Schema Discovery

The driver models Azure Data Catalog APIs as relational tables, views, and stored procedures. Use the following line to retrieve the list of tables:

dbListTables(conn)

Execute SQL Queries

You can use the dbGetQuery function to execute any SQL query supported by the Azure Data Catalog API:

tables <- dbGetQuery(conn,"SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'")

You can view the results in a data viewer window with the following command:

View(tables)

Plot Azure Data Catalog Data

You can now analyze Azure Data Catalog data with any of the data visualization packages available in the CRAN repository. You can create simple bar plots with the built-in bar plot function:

par(las=2,ps=10,mar=c(5,15,4,2)) barplot(tables$Type, main="Azure Data Catalog Tables", names.arg = tables$DslAddressDatabase, horiz=TRUE)