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Developer Advocate
Developer Advocate
Some time ago I wrote about a way to quickly load data from CSV files into SAP HANA table using hana_ml Python package. It became quite a popular post. But it does require running some code to get a file loaded.

Recently I was asked if that hana_ml functionality can be wrapped into some kind of UI and made available as an app on SAP Business Technology Platform for a user just upload any CSV file into a staging table in the SAP HANA Cloud database.

It sounded like a good exercise to finally try Python's Streamlit module that dmitrybuslov and andreas.forster wrote about in Low-code data analysis application with SAP HANA push-down. Streamlit promises to help rapidly develop applications in Python.

So, I decided to give it a try and see how to make it work in the CloudFoundry environment of SAP BTP. So, it was a good exercise deploying Python applications to SAP BTP too.


  • SAP BTP Trial account with SAP HANA Database created and running in SAP HANA Cloud

  • cf command-line tool (CLI)

If you are not familiar with deploying Python applications to SAP BTP, CloudFoundry environment, then please check Create a Python Application via Cloud Foundry Command Line Interface tutorial first.

I won't repeat steps from there, like how to logon to your SAP BTP account using cf CLI. But I will cover extras we are going to work with / experiment with here.

Let's have a look at the application

Before discussing the details of the implementation of the application, let's first check how it looks and what it does.

Once started:

  1. The app checks that expected schema exists in SAP HANA Cloud database and connects to it.

  2. It allows a user to upload any CSV or TXT file (please note 10MB limit).

  3. Once you preview uploaded file you can persist it into your SAP HANA db instance.

In the end, we can see a table "STAGING4UPLOADS"."STAGING" with corresponding structure created and data uploaded.

If we upload another file, then the structure of this table will be recreated to fit the new data.


Create a service for a staging area in SAP HANA db...

Checking available plans for hana service:
cf marketplace -e hana

The following command creates a hana service instance named hdb-staging-schema with the plan schema and database schema name to be created STAGING4UPLOADS:
cf create-service hana schema hdb-staging-schema -c '{ "schema" : "STAGING4UPLOADS" }'

Once created you should see the service in your SAP BTP Cockpit too:

...and a service key for us to access data in that schema

cf create-service-key hdb-staging-schema hdb-staging-schema-sk
cf service-key hdb-staging-schema hdb-staging-schema-sk

We can use it to connect to the database and the required schema STAGING4UPLOADS.
CONNECT STAGING4UPLOADS PASSWORD "Your_Long_Password_From_The_ServiceKey";
SELECT Current_User, Current_Schema FROM dummy;

Implement the Python application

The source code of the application is downloadable from

The main file with the minimal required logic (therefore mvp in the name: there is not much error-handling to make the code more readable).
from io import StringIO
import streamlit as st
import hana_ml.dataframe as dataframe
from cfenv import AppEnv
import pandas as pd



env = AppEnv()

HANA_SERVICE = 'hdb-staging-schema'
hana = env.get_service(name=HANA_SERVICE)

# Instantiate connection object
conn = dataframe.ConnectionContext(address=hana.credentials['host'],

st.write(f'1. Connected to the schema {conn.get_current_schema()}')

df_data = None

some_file = st.file_uploader("2. Upload a file to be loaded into SAP HANA Cloud db", type={"csv", "txt"})
if some_file is not None:
# To read file as bytes:
bytes_data = some_file.getvalue()

# To convert to a string based IO:
stringio = StringIO(some_file.getvalue().decode("utf-8"))

# To read file as string:
string_data =
st.write('File preview (up to 5 lines):', string_data[:5])

# Load CSV into pandas DataFrame

df_data = None
if some_file is not None:
df_data = pd.read_csv(some_file, sep=None).convert_dtypes()

st.write(f"Pandas dataframe size: {0.0 if df_data is None else round(df_data.memory_usage(deep=True).sum()/1024/1024, 2)} MB")
st.write('Dataframe (up to 5 rows):', df_data if df_data is None else df_data.head(5))

# Load data to SAP HANA
service_schema = hana.credentials['schema']
target_table = st.text_input('HANA table name: ', 'STAGING')

if st.button('3. Persist data into the HANA db') and df_data is not None and target_table!='':
df_remote = dataframe.create_dataframe_from_pandas(connection_context = conn,
pandas_df = df_data,
table_name = target_table,
schema = service_schema,
force = True,
disable_progressbar = True)

st.write(f'Successful creation of the table {target_table}: {conn.has_table(table=target_table, schema = service_schema)}')

Please note:

  1. Setting up the page icon

  2. The binding to the service to get connection details for SAP HANA database.

  3. The use of sep=None in Pandas' read_csv() to automate the recognition of the separator in the file.


The file with required modules to be pulled from PyPI.

  1. cfenv is a tiny utility that simplifies interactions with Cloud Foundry environment variables

  2. hana-ml is a Python machine learning client for SAP HANA

  3. streamlit is a Streamlit’s open-source app framework to create web apps


The file defines the version of Python to be used. The CloudFoundry buildpack with the corresponding version will be downloaded.

Please note:

  1. the use of .* which ensures that the latest available buildpck for Python 3.9 will be used.


The files defines the command to be executed when the application start.
web: streamlit run --server.port ${PORT}

Please note:

  1. the use of the variable ${PORT} which ensures that streamlit app runs (via --server.port argument) on the default web port of the CloudFoundry app instead of the default Streamlit port 5000.


The CloudFoundry manifest of the application.
- name: ((app-name))
- route: ((app-name))-((account-name)).cfapps.((btp-region))
memory: 2048M
- python_buildpack
- hdb-staging-schema

Please note:

  1. The binding to hdb-staging-schemaa service instance.

  2. Passing the Streamlit's environment variable STREAMLIT_SERVER_MAX_UPLOAD_SIZE to limit a size of a file to 100MB. Bigger file size would require a bigger memory size of the CloudFoundry app than a limit set to 2GB in the manifest.

  3. The use of variables to avoid hard-coding of the application name app-name, account name account-name (used to create unique URLs for your deployments), and a BTP region btp-region where the app is deployed.
    These variables are provided via a separate file vars.yml described below.


The file with the variables for the manifest file.
app-name: csv2hdb
account-name: e8ee8684trial
btp-region: us10

You should modify these three variable to match your application's requirements!

Deploy the application

To deploy the application use:
cf push --vars-file vars.yml

You should see a log similar to the one below:
Pushing app csv2hdb to org e8ee8684trial / space dev as witalij... ...
Applying manifest file /.../sap-tech-bytes/scripts/manifest.yml...
Manifest applied
Packaging files to upload...
Uploading files...
1.89 KiB / 1.89 KiB [=========================================================================================================================================] 100.00% 1s

Waiting for API to complete processing files...

Staging app and tracing logs...
Downloading python_buildpack...
Downloaded python_buildpack
Cell d5f8ba8f-6b80-4d41-b335-1a257634845d creating container for instance e4c343c7-2922-43d4-a6a0-b7055e4ab8a7
Cell d5f8ba8f-6b80-4d41-b335-1a257634845d successfully created container for instance e4c343c7-2922-43d4-a6a0-b7055e4ab8a7
Downloading app package...
Downloaded app package (1.9K)
-----> Python Buildpack version 1.7.49
-----> Supplying Python
-----> Installing python 3.9.9
Copy [/tmp/buildpacks/49b223c631091864eee2a117ec43f025/dependencies/15b399819fce43a5b5eedb0316dbb3c1/python_3.9.9_linux_x64_cflinuxfs3_6a29c275.tgz]
Using python's pip module
-----> Running Pip Install
Collecting cfenv
Downloading cfenv-0.5.3-py2.py3-none-any.whl (4.5 kB)
Collecting hana-ml
Downloading hana_ml-2.13.22060800-py3-none-any.whl (5.1 MB)
Collecting streamlit
Downloading streamlit-1.10.0-py2.py3-none-any.whl (9.1 MB)
Exit status 0
Uploading droplet, build artifacts cache...
Uploading droplet...
Uploading build artifacts cache...
Uploaded build artifacts cache (127.7M)
Uploaded droplet (201.5M)
Uploading complete

Waiting for app csv2hdb to start...

Instances starting...

name: csv2hdb
requested state: started
isolation segment: trial
last uploaded: Thu 09 Jun 12:55:08 CEST 2022
stack: cflinuxfs3
name version detect output buildpack name
python_buildpack 1.7.49 python python

type: web
instances: 1/1
memory usage: 2048M
start command: streamlit run --server.port ${PORT}
state since cpu memory disk details
#0 running 2022-06-09T10:55:34Z 0.0% 106.1M of 2G 751M of 1G

The application is running and we can see it from the command line...
cf app csv2hdb

...and in the SAP BTP Cockpit

Clean up your trial environment

If you used this just as an exercise and would like to clean your development space in SAP BTP from created artifacts, then execute the following commands:
cf delete -rf csv2hdb
cf delete-service-key -f hdb-staging-schema hdb-staging-schema-sk
cf delete-service -fw hdb-staging-schema

Please note there will be no requests of confirmation because of the use of the -f flag!

I hope you find this post helpful as an example of a Python application deployment to SAP BTP, CloudFoundry environment, as well as an example of uploading any CSV/TXT file with tabular data into SAP HANA Cloud.

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