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Extract, Transform, and Load Dynamics GP Data in Python

The CData Python Connector for Dynamics GP enables you to create ETL applications and pipelines for Dynamics GP 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 Dynamics GP and the petl framework, you can build Dynamics GP-connected applications and pipelines for extracting, transforming, and loading Dynamics GP data. This article shows how to connect to Dynamics GP with the CData Python Connector and use petl and pandas to extract, transform, and load Dynamics GP data.

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

Connecting to Dynamics GP Data

Connecting to Dynamics GP 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 authenticate set the User and Password connection properties.

To connect set the URL to the Web services endpoint; for example, http://{servername}:{port}/Dynamics/GPService. Additionally, set CompanyId; you can obtain this value in the company setup window: Click Tools -> Setup -> Company.

By default, data summaries are not returned to save performance. Set LookupIds to true to return details such as line items; however, note that entities must be retrieved one at a time.

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

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

cnxn = mod.connect("CompanyId=mycompanyId;user=myuser;password=mypassword;URL= http://{servername}:{port}/Dynamics/GPService;")

Create a SQL Statement to Query Dynamics GP

Use SQL to create a statement for querying Dynamics GP. In this article, we read data from the SalesInvoice entity.

sql = "SELECT CustomerName, TotalAmount FROM SalesInvoice WHERE CustomerName = 'Bob'"

Extract, Transform, and Load the Dynamics GP Data

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

Loading Dynamics GP Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("CompanyId=mycompanyId;user=myuser;password=mypassword;URL= http://{servername}:{port}/Dynamics/GPService;")

sql = "SELECT CustomerName, TotalAmount FROM SalesInvoice WHERE CustomerName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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