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Raw Function

get(connection, parameters_dict)

A function to return back raw data by querying databricks SQL Warehouse using a connection specified by the user.

The available connectors by RTDIP are Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect.

The available authentcation methods are Certificate Authentication, Client Secret Authentication or Default Authentication. See documentation.

This function requires the user to input a dictionary of parameters. (See Attributes table below)

Parameters:

Name Type Description Default
connection object

Connection chosen by the user (Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect)

 required
parameters_dict dict

A dictionary of parameters (see Attributes table below)

 required

Attributes:

Name Type Description
business_unit str

Business unit

region str

Region
asset str

Asset

data_security_level str

Level of data security
data_type str

Type of the data (float, integer, double, string)
tag_names list

List of tagname or tagnames ["tag_1", "tag_2"]
start_date str

Start date (Either a date in the format YY-MM-DD or a datetime in the format YYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
end_date str

End date (Either a date in the format YY-MM-DD or a datetime in the format YYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
include_bad_data bool

Include "Bad" data points with True or remove "Bad" data points with False

Returns:

Name Type Description
DataFrame pd.DataFrame

A dataframe of raw timeseries data.
Source code in src/sdk/python/rtdip_sdk/queries/time_series/raw.py 
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def get(connection: object, parameters_dict: dict) -> pd.DataFrame:
    '''
    A function to return back raw data by querying databricks SQL Warehouse using a connection specified by the user. 

    The available connectors by RTDIP are Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect.

    The available authentcation methods are Certificate Authentication, Client Secret Authentication or Default Authentication. See documentation.

    This function requires the user to input a dictionary of parameters. (See Attributes table below)

    Args:
        connection: Connection chosen by the user (Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect)
        parameters_dict: A dictionary of parameters (see Attributes table below)

    Attributes:
        business_unit (str): Business unit 
        region (str): Region
        asset (str): Asset 
        data_security_level (str): Level of data security
        data_type (str): Type of the data (float, integer, double, string)
        tag_names (list): List of tagname or tagnames ["tag_1", "tag_2"]
        start_date (str): Start date (Either a date in the format YY-MM-DD or a datetime in the format YYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
        end_date (str): End date (Either a date in the format YY-MM-DD or a datetime in the format YYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
        include_bad_data (bool): Include "Bad" data points with True or remove "Bad" data points with False

    Returns:
        DataFrame: A dataframe of raw timeseries data.
    '''
    try:
        query = _query_builder(parameters_dict)

        try:
            cursor = connection.cursor()
            cursor.execute(query)
            df = cursor.fetch_all()
            cursor.close()
            return df
        except Exception as e:
            logging.exception('error returning dataframe')
            raise e

    except Exception as e:
        logging.exception('error with raw function')
        raise e

Example

from rtdip_sdk.authentication.azure import DefaultAuth
from rtdip_sdk.connectors import DatabricksSQLConnection
from rtdip_sdk.queries import raw

auth = DefaultAuth().authenticate()
token = auth.get_token("2ff814a6-3304-4ab8-85cb-cd0e6f879c1d/.default").token
connection = DatabricksSQLConnection("{server_hostname}", "{http_path}", token)

parameters = {
    "business_unit": "Business Unit",
    "region": "Region", 
    "asset": "Asset Name", 
    "data_security_level": "Security Level", 
    "data_type": "float", #options:["float", "double", "integer", "string"]
    "tag_names": ["tag_1", "tag_2"], #list of tags
    "start_date": "2023-01-01", #start_date can be a date in the format "YYYY-MM-DD" or a datetime in the format "YYYY-MM-DDTHH:MM:SS" or specify the timezone offset in the format "YYYY-MM-DDTHH:MM:SS+zz:zz"
    "end_date": "2023-01-31", #end_date can be a date in the format "YYYY-MM-DD" or a datetime in the format "YYYY-MM-DDTHH:MM:SS" or specify the timezone offset in the format "YYYY-MM-DDTHH:MM:SS+zz:zz"
    "include_bad_data": True, #options: [True, False]
}
x = raw.get(connection, parameters)
print(x)

This example is using DefaultAuth() and DatabricksSQLConnection() to authenticate and connect. You can find other ways to authenticate here. The alternative built in connection methods are either by PYODBCSQLConnection(), TURBODBCSQLConnection() or SparkConnection().

Note

server_hostname and http_path can be found on the SQL Warehouses Page.