How to Build an ETL App for Snapchat Ads 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 Snapchat Ads and the petl framework, you can build Snapchat Ads-connected applications and pipelines for extracting, transforming, and loading Snapchat Ads data. This article shows how to connect to Snapchat Ads with the CData Python Connector and use petl and pandas to extract, transform, and load Snapchat Ads data.

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

Connecting to Snapchat Ads Data

Connecting to Snapchat Ads 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.

You will need to create an OAuth application to connect to Snapchat Ads. See the online Help documentation for an authentication guide.

Additionally, you can optionally set AccountId to provide a default Account ID (meaning it won't need to be manually provided in the WHERE clause). If the AccountId is not specified, the first account in the Accounts view is used.

After installing the CData Snapchat Ads Connector, follow the procedure below to install the other required modules and start accessing Snapchat Ads 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 Snapchat Ads 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.snapchatads as mod

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Snapchat Ads

Use SQL to create a statement for querying Snapchat Ads. In this article, we read data from the Campaigns entity.

sql = "SELECT AccountId, Name FROM Campaigns WHERE Id = '123'"

Extract, Transform, and Load the Snapchat Ads Data

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

Loading Snapchat Ads Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Snapchat Ads

table1 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

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

With the CData Python Connector for Snapchat Ads, you can work with Snapchat Ads 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 Snapchat Ads to start building Python apps and scripts with connectivity to Snapchat Ads data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT AccountId, Name FROM Campaigns WHERE Id = '123'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['AccountId','Name'], ['NewAccountId1','NewName1'], ['NewAccountId2','NewName2'], ['NewAccountId3','NewName3'] ]

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