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

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

Connecting to Microsoft Project Data

Connecting to Microsoft Project 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.

The User and Password properties, under the Authentication section, must be set to valid Microsoft Project user credentials. In addition, you will need to specify a URL to a valid Microsoft Project server organization root or Microsoft Project services file.

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;URL=http://myserver/myOrgRoot;")

Create a SQL Statement to Query Microsoft Project

Use SQL to create a statement for querying Microsoft Project. In this article, we read data from the Projects entity.

sql = "SELECT ProjectName, ProjectActualCost FROM Projects WHERE ProjectName = 'Tax Checker'"

Extract, Transform, and Load the Microsoft Project Data

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

Loading Microsoft Project Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Microsoft Project

table1 = [ ['ProjectName','ProjectActualCost'], ['NewProjectName1','NewProjectActualCost1'], ['NewProjectName2','NewProjectActualCost2'], ['NewProjectName3','NewProjectActualCost3'] ]

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

With the CData Python Connector for MS Project, you can work with Microsoft Project 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 MS Project to start building Python apps and scripts with connectivity to Microsoft Project 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.microsoftproject as mod

cnxn = mod.connect("User=myuseraccount;Password=mypassword;URL=http://myserver/myOrgRoot;")

sql = "SELECT ProjectName, ProjectActualCost FROM Projects WHERE ProjectName = 'Tax Checker'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ProjectName','ProjectActualCost'], ['NewProjectName1','NewProjectActualCost1'], ['NewProjectName2','NewProjectActualCost2'], ['NewProjectName3','NewProjectActualCost3'] ]

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