How to Build an ETL App for Slack Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Slack and the petl framework, you can build Slack-connected applications and pipelines for extracting, transforming, and loading Slack data. This article shows how to connect to Slack with the CData Python Connector and use petl and pandas to extract, transform, and load Slack data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Slack data in Python. When you issue complex SQL queries from Slack, the driver pushes supported SQL operations, like filters and aggregations, directly to Slack and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Slack Data

Connecting to Slack data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Slack uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started section of the help documentation for an authentication guide.

After installing the CData Slack Connector, follow the procedure below to install the other required modules and start accessing Slack through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Slack Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.slack as mod

You can now connect with a connection string. Use the connect function for the CData Slack Connector to create a connection for working with Slack data.

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Slack

Use SQL to create a statement for querying Slack. In this article, we read data from the Channels entity.

sql = "SELECT Id, Name FROM Channels WHERE IsPublic = 'True'"

Extract, Transform, and Load the Slack Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Slack data. In this example, we extract Slack data, sort the data by the Name column, and load the data into a CSV file.

Loading Slack Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'channels_data.csv')

In the following example, we add new rows to the Channels table.

Adding New Rows to Slack

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

etl.appenddb(table1, cnxn, 'Channels')

With the CData Python Connector for Slack, you can work with Slack data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Slack to start building Python apps and scripts with connectivity to Slack data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.slack as mod

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Name FROM Channels WHERE IsPublic = 'True'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'channels_data.csv')

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

etl.appenddb(table3, cnxn, 'Channels')