Extract, Transform, and Load Plaid Data in Python

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Plaid Python Connector

Python Connector Libraries for Plaid Data Connectivity. Integrate Plaid with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Plaid enables you to create ETL applications and pipelines for Plaid data in Python with petl.

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

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

Connecting to Plaid Data

Connecting to Plaid 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 can connect to Plaid using the embedded OAuth connectivity. When you connect, the Plaid OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

Optionally set the Account Id property to return data related to a specific Account.

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

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

cnxn = mod.connect("AccountId=123456789;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Plaid

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

sql = "SELECT AccountId, Name FROM Transactions WHERE Name = 'Apple Store'"

Extract, Transform, and Load the Plaid Data

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

Loading Plaid Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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


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

Adding New Rows to Plaid

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

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

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

cnxn = mod.connect("AccountId=123456789;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT AccountId, Name FROM Transactions WHERE Name = 'Apple Store'"

table1 = etl.fromdb(cnxn,sql)

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


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

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