mstrio – Python and R wrappers for the MicroStrategy

mstrio – Python and R wrappers for the MicroStrategy REST APIs

Connecting to MicroStrategy 

Create a connection to the Intelligence Server using Connection() and  connect() in Python and R, respectively. Required arguments for the  Connection() function are the URL for the MicroStrategy REST API server, MicroStrategy Intelligence Server username and password, as well as the MicroStrategy project name. By default, the  connect() function anticipates your MicroStrategy Intelligence Server username and password. LDAP authentication is also supported. Use the optional argument  login_mode=16  in the  connect() function for LDAP authentication. 

Extract data from cubes and reports 

To extract data from MicroStrategy cubes and reports, use the  get_cube() and  get_report() functions. Use your connection object and the ID for the cube or report that you are fetching. You can get the ID by navigating to the cube or report within MicroStrategy Web, right-clicking on the desired cube, and selecting Properties. Alternatively, you can use MicroStrategy Developer in a similar manner.  The  get_cube() and  get_report() functions will return a data frame with the requested data. 

Upload data to MicroStrategy 

Create a new in-memory dataset with the create_dataset() function. You'll need to provide a name for your cube, as well as a name for the table that will contain the data. At this time, only one table per cube is supported. The create_cube() function will return the datasetID and tableID, which can be used to update a dataset with new data. 

Add or update a dataset with new data 

Once a dataset has been created, you can both update the data within the cube and add new data to it with the  update_dataset() function. Note that you'll need to pass in both the datasetID and tableID for the target dataset and table within the dataset, respectively. These are returned by the  create_dataset() function. 

The  update_policy  parameter controls the update behavior. Currently supported update operations are: 

• add (inserts entirely new data) 
• update (updates existing data) 
• upsert (simultaneously updates existing data and inserts new data) 
• replace (truncates and replaces the data)

This Knowledge Base article introduces "mstrio," Python and R packages which provide an interface for the MicroStrategy REST APIs. With a few lines of code, data scientists can extract data from cubes and reports and publish entirely new datasets for consumption by other analysts. 

This provides data scientists access to trusted, operational business data using Python and R, which is used in an overwhelming number of data science projects today. With mstrio, data scientists can quickly push their findings into a MicroStrategy dataset, enabling decision-makers to act on the insights. 

Connecting to MicroStrategy 

Create a connection to the Intelligence Server using Connection() and  connect() in Python and R, respectively. Required arguments for the  Connection() function are the URL for the MicroStrategy REST API server, MicroStrategy Intelligence Server username and password, as well as the MicroStrategy project name. By default, the  connect() function anticipates your MicroStrategy Intelligence Server username and password. LDAP authentication is also supported. Use the optional argument  login_mode=16  in the  connect() function for LDAP authentication. 

Extract data from cubes and reports 

To extract data from MicroStrategy cubes and reports, use the  get_cube() and  get_report() functions. Use your connection object and the ID for the cube or report that you are fetching. You can get the ID by navigating to the cube or report within MicroStrategy Web, right-clicking on the desired cube, and selecting Properties. Alternatively, you can use MicroStrategy Developer in a similar manner.  The  get_cube() and  get_report() functions will return a data frame with the requested data. 

Upload data to MicroStrategy 

Create a new in-memory dataset with the create_dataset() function. You'll need to provide a name for your cube, as well as a name for the table that will contain the data. At this time, only one table per cube is supported. The create_cube() function will return the datasetID and tableID, which can be used to update a dataset with new data. 

Add or update a dataset with new data 

Once a dataset has been created, you can both update the data within the cube and add new data to it with the  update_dataset() function. Note that you'll need to pass in both the datasetID and tableIDfor the target dataset and table within the dataset, respectively. These are returned by the  create_dataset() function. 

The  update_policy  parameter controls the update behavior. Currently supported update operations are: 

• add (inserts entirely new data) 
• update (updates existing data) 
• upsert (simultaneously updates existing data and inserts new data) 
• replace (truncates and replaces the data).