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

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

Connecting to Instagram Data

Connecting to Instagram 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.

Instagram uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Instagram. See the help documentation for a guide.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Instagram

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

sql = "SELECT Link, LikesCount FROM Media WHERE TagName = 'goldfish'"

Extract, Transform, and Load the Instagram Data

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

Loading Instagram Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Instagram

table1 = [ ['Link','LikesCount'], ['NewLink1','NewLikesCount1'], ['NewLink2','NewLikesCount2'], ['NewLink3','NewLikesCount3'] ]

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

With the CData Python Connector for Instagram, you can work with Instagram 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 Instagram to start building Python apps and scripts with connectivity to Instagram 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.instagram as mod

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Link, LikesCount FROM Media WHERE TagName = 'goldfish'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Link','LikesCount'], ['NewLink1','NewLikesCount1'], ['NewLink2','NewLikesCount2'], ['NewLink3','NewLikesCount3'] ]

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