Analyze Azure Analysis Services Data in R

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Azure Analysis Services JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Azure Analysis Services.



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

Access Azure Analysis Services 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 Analysis Services and the RJDBC package to work with remote Azure Analysis Services 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 Analysis Services and visualize Azure Analysis Services 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 Analysis Services as a JDBC Data Source

You will need the following information to connect to Azure Analysis Services as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.aas.AASDriver
  • 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 Analysis Services:

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

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

To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

Built-in Connection String Designer

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

java -jar cdata.jdbc.aas.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:aas:URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH")

Schema Discovery

The driver models Azure Analysis Services 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 Analysis Services API:

customer <- dbGetQuery(conn,"SELECT Country, Education FROM Customer WHERE Country = 'Australia'")

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

View(customer)

Plot Azure Analysis Services Data

You can now analyze Azure Analysis Services 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(customer$Education, main="Azure Analysis Services Customer", names.arg = customer$Country, horiz=TRUE)