Over the past year, I have learned a lot and I am extremely excited to share some of the cool things with you. As the world keeps evolving at a fast pace, it has led to automation. In my daily work, it is a key priority to automate some aspects of work to make life easy for everyone. Who doesn’t love to do the hard lifting and enjoy its fruits? 😅

With this said. I had a long discussion with my friend and mentor some months ago, a big shout-out to Lawer Akrofi. To be exact two years ago. I know!!! it has taken me two years to put this together. But then it is good news because I tried re-implementing this solution we came up with and realized I had some restrictions on the server hence an API to the server to insert data was not a solution for me. So what did I do? I had a script that fetched daily CSV files onto my machine with the help of cronjobs and Tableau Prep flow which cleans the data and publishes it to the server using the prep command line. oh yes, it was a fun time for me to learn and once I figured it out, I did reward myself with a very good piece of tiramisù. If you do know me, well I have a sweet tooth. 😊

I have said a lot of things but no codes yet. Let’s dive into publishing a static CSV file as hyper on the Tableau server using the Tableau Server’s API. In another blog post, we will do a little rewiring when we talk about using SharePoint instead of a CSV file that sits on your laptop. In case your firm has its own database and API, an API call to fetch data, is indeed possible. Everything under the sun is possible to achieve!!!!

To every data enthusiast out there using python, is it safe to say pandas is our best friend and first love of data manipulation? Below, I will insert the block of codes and try my very best to explain. I came across a blog post that was extremely helpful for the creation of the hyper extract. I modified it to prevent typing in 100 column names if your data is large while creating the schema and also allows users to specify which project to publish the data to.

#importing all needed libarires
import pandas as pd

from tableau_api_lib import TableauServerConnection
from tableau_api_lib.utils.querying import get_projects_dataframe

from tableauhyperapi import HyperProcess, Connection, TableDefinition, SqlType, Telemetry, Inserter, CreateMode, TableName
from tableauhyperapi import escape_string_literal

import tableauserverclient as TSC

#Get the data; read in csv file
filename = pd.read_csv(['file name goes here'], usecols = ['read in appropriate columns needed'])

#convert all datetime values to appropriate type
#convert data types into their appropriate types and formats
#an example below to convert date
filename['date'] = filename['date'].apply(pd.to_datetime)

# get a list of column names from the csv file
colnames =filename.columns

# get a list of column typesfrom the csv file
coltypes =filename.dtypes

#the above would help with the definition of the schema

#specifying the various data type to be used to define the schema 
field = { 
    'float64' :     SqlType.double(), 
    'float32' :     SqlType.double(),
    'int64' :       SqlType.double(),
    'int32' :       SqlType.double(),
    'object':       SqlType.text(),
    'bool':         SqlType.bool(),
    'datetime64[ns, UTC]':   SqlType.date(),

# for each column, add the appropriate info for the Table Definition
column_names = []
column_type = []
for i in range(0, len(colnames)):
    cname = colnames[i] #header of column
    coltype = coltypes[i] #pandas data type of column
    ctype = field.get(str(coltype)) #get corresponding sql column type 

    #store in lists to used for column and schema

#name and path to save extract temporarily
PATH_TO_HYPER = 'hyper_extract.hyper'

# Step 1: Start a new private local Hyper instance
with HyperProcess(Telemetry.SEND_USAGE_DATA_TO_TABLEAU, 'myapp' ) as hyper:

# Step 2:  Create the the .hyper file, replace it if it already exists
    with Connection(endpoint=hyper.endpoint, 
                    database=PATH_TO_HYPER) as connection:

# Step 3: Create the schema(empty)

# Step 4: Create the table definition
        #defining the columns according to the dataframe
        cols = []
        for i, j in zip(column_names, column_type):
            columns = TableDefinition.Column(i, j)

        #creating schema 
        schema = TableDefinition(table_name=TableName('Extract','Extract'),
                columns= cols)

# Step 5: Create the table in the connection catalog
        with Inserter(connection, schema) as inserter:
            for index, row infilename.iterrows():

    print("The connection to the Hyper file is closed.")     

# Establish a connection to Tableau Server and publish the extract to Tableau Server incase your firm using the Server, else see the authentication to Tableau Online below

tableau_auth = TSC.PersonalAccessTokenAuth('name of API goes here', 'your token secret goes here')
server = TSC.Server('site address')


# Establish a connection to Tableau Online and publish the extract to Tableau Online

tableau_auth = TSC.PersonalAccessTokenAuth('name of API goes here', 'your tokensecret goes here', 'name of site')
server = TSC.Server('site address')


#allow user to input project name for datasource to be published; that is identify the mother project folder to publish the datasource on the server
datasource = input(str('enter name of datasource project directory to publish datasource '))

with server.auth.sign_in(tableau_auth):

     # fetch the corresponding project id 
    ID = ''

    all_project_items, pagination_item = server.projects.get()
    for proj in all_project_items:
        if proj.name == datasource:
            ID = proj.id

    # Use the project id to create new datsource_item
    new_datasource = TSC.DatasourceItem(ID)

    # publish data source (specified in file_path)
    new_datasource = server.datasources.publish(
                    #new_datasource, PATH_TO_HYPER, 'CreateNew')
    '''#since datasource exist from prior publish, overwrite it 
    new_datasource = server.datasources.publish(
                    new_datasource, PATH_TO_HYPER, 'Overwrite')'''

#signing out of the tableau server
print('Publishing completed')


That is all folks!! This is pretty straightforward. In order to run this periodically, saving these codes as .py file and using cronjobs on MAC or task scheduler on WINDOWS, I believe a good job on automation could be done. Having this periodically on the server and feeding it into a dashboard for reporting is pretty much hard lifting work. Sit back and enjoy the beauty of technology and evolution. I hope the above comments in the codes make a good explanation. 😊



  1. Tableau Hyper API
  2. Tableau REST API