Use Dash to Build Web Apps on FTP Data via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas module, and the Dash framework, you can build FTP-connected web applications for FTP data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing FTP data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source: connect with a Personal Access Token and build your app.

Connect to FTP in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "FTP" from the Add Connection panel

3. Enter the necessary authentication properties to connect to FTP.

   To connect to FTP or SFTP servers, specify at least RemoteHost and FileProtocol. Specify the port with RemotePort.

   Set User and Password to perform Basic authentication. Set SSHAuthMode to use SSH authentication. See the Getting Started section of the data provider help documentation for more information on authenticating via SSH.

   Set SSLMode and SSLServerCert to secure connections with SSL.

   The data provider lists the tables based on the available folders in your FTP server. Set the following connection properties to control the relational view of the file system:

   • RemotePath: Set this to the current working directory.
   • TableDepth: Set this to control the depth of folders to list as views.
   • FileRetrievalDepth: Set this to retrieve and list files recursively from the root table.

   Stored Procedures are available to download files, upload files, and send protocol commands. See the Data Model chapter of the FTP data provider documentation for more information.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra), Dash, and Plotly using the pip utility:

pip install "cdata-connect-ai[full]"
pip install dash
pip install plotly

Build a Web App on FTP Data in Python

Once the required modules are installed, you are ready to build the web app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules, then connect to Connect AI with your account email and PAT and read FTP data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, FTP1).

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT Filesize, Filename "
    "FROM [FTP1].[FTP].[MyDirectory] "
    "WHERE FilePath = '/documents/doc.txt'",
    conn,
)

conn.close()

Configure the App and Layout

With the query results stored in a DataFrame, build a bar graph from the FTP data and configure the app layout.

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['Filesize'], y=df['Filename'], name='Filesize')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='FTP MyDirectory Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

Set the App to Run

With the connection, app, and layout configured, you are ready to run the app.

if __name__ == '__main__':
    app.run(debug=True)

Now, use Python to run the web app and a browser to view the FTP data.

python ftp-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live FTP data using the CData Connect AI Python SDK. For more information on connecting to FTP (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live FTP data in Python.

Full Source Code

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT Filesize, Filename "
    "FROM [FTP1].[FTP].[MyDirectory] "
    "WHERE FilePath = '/documents/doc.txt'",
    conn,
)

conn.close()

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['Filesize'], y=df['Filename'], name='Filesize')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='FTP MyDirectory Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

if __name__ == '__main__':
    app.run(debug=True)