The SAP BusinessObjects RESTful Web Service (RESTful API) is an application programming interface provided by SAP BusinessObjects. It allows developers to interact with SAP BusinessObjects BI (Business Intelligence) platform functionalities through HTTP requests. This RESTful API enables the integration of SAP BusinessObjects services into custom applications, facilitating tasks such as retrieving, creating, updating, and deleting BI content, as well as managing user permissions and security. With the RESTful API, developers can perform operations like fetching information about reports, universes, folders, scheduling, and other BI-related entities. This approach promotes interoperability and simplifies the integration of SAP BusinessObjects BI capabilities into various applications and workflows. How to Fetch universes using Python? Part 1: Authentication To initiate the authentication process, please replace the placeholder values for , , and with your specific configuration details. username password localhost import requests
import pandas as pd
import xml.etree.ElementTree as ET

# define the login request parameters
user_name = 'username'
password = 'password'
localhost = 'localhost'

auth_type = 'secEnterprise'
login_url = 'http://{}:6405/biprws/logon/long'.format(localhost)
login_data = f'<attrs xmlns="http://www.sap.com/rws/bip"><attr name="userName" type="string">{user_name}</attr><attr name="password" type="string">{password}</attr><attr name="auth" type="string" possibilities="secEnterprise,secLDAP,secWinAD,secSAPR3">{auth_type}</attr></attrs>'
login_headers = {'Content-Type': 'application/xml'}

# send the login request and retrieve the response
login_response = requests.post(login_url, headers=login_headers, data=login_data)
# parse the XML response and retrieve the logonToken
root = ET.fromstring(login_response.text)
logon_token = root.find('.//{http://www.sap.com/rws/bip}attr[@name="logonToken"]').text
api_headers = {'Content-Type': 'application/xml', 'X-SAP-LogonToken': logon_token} Part 2: Data Retrieval and DataFrame Creation Previewing Retrieved Data: First Universe's Name This Python snippet fetches all the information about universes from the server. If you run the code, it will print the name of the first universe. response = requests.get("http://{}:6405/biprws/raylight/v1/universes/".format(localhost),
                        headers=api_headers)
root = ET.fromstring(response.text)
print(root.findall('universe')[0][2].tag, ":", root.findall('universe')[0][2].text) Data Transformation Functions: Transform to DataFrame The Python functions, , and , work together to simplify SAP BusinessObjects data retrieval. The first function transforms XML data into a structured pandas DataFrame, capturing document attributes. The second function efficiently handles scenarios with universes exceeding a single request's limit by appending multiple DataFrames. Collectively, these functions streamline the conversion of XML to DataFrame and provide an easy solution for handling a large number of universes. get_dataframe_from_response get_all_dataframe def get_dataframe_from_response(response):
    # Parse the XML data
    root = ET.fromstring(response.text)
    # Extract the data into a list of dictionaries
    res = []
    for item in root.findall('universe'):
        doc_dict = {}
        for elem in item.iter():
            if elem.text is not None:
                doc_dict[elem.tag] = elem.text
        res.append(doc_dict)
    # Convert the list of dictionaries to a pandas dataframe
    df = pd.DataFrame(res)
    return df

def get_all_dataframe(url):
    documents = []
    for i in range(50):
        offset = i * 50
        url_offset = url + "?offset={}&limit=50".format(offset)
        response = requests.get(url_offset, headers=api_headers)
        df = get_dataframe_from_response(response=response)
        if df.empty:
            break
        else:
            documents.append(df)
    dataframe = pd.concat(documents, axis=0)
    return dataframe Retrieve detailed information about SAP BusinessObjects universes effortlessly using a single line of Python code. Utilize the function, and the resulting DataFrame provides a straightforward overview of universes attributes. get_all_dataframe df_universes df_universes = get_all_dataframe(url="http://{}:6405/biprws/sl/v1/universes".format(localhost)) Showcasing : What follows is a glimpse into the dataframe structure df_universes Part 3: Universe Details Extraction If you need additional details such as the universe's path and connected status, utilize the following function. This function fetches the specified details for each universe in the DataFrame, providing a more comprehensive overview of each entry. df_universes def get_universe_detail(universeID, detail):
    url = 'http://{}:6405/biprws/raylight/v1/universes/{}'.format(localhost, universeID)
    res = requests.get(url, headers={
        "Accept": "application/json",
        "Content-Type": "application/json",
        "X-SAP-LogonToken": logon_token
    }).json()
    return res['universe'][detail]

def get_more_information_from_universes(df):
    details = ['path', 'connected']
    for detail in details:
        df[detail] = [get_universe_detail(id, detail) for id in df['id'].values]
    return df

df_universes_more_info = get_more_information_from_universes(df_universes) Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on .  Happy exploring!👋 LinkedIn Also published . here

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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