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

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

Connecting to Access Data

Connecting to Access 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.

To connect, set the DataSource property to the path to the Access database.

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

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

cnxn = mod.connect("DataSource=C:/MyDB.accdb;")

Create a SQL Statement to Query Access

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

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

Extract, Transform, and Load the Access Data

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

Loading Access Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Access

table1 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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

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

cnxn = mod.connect("DataSource=C:/MyDB.accdb;")

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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