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

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

Connecting to Flowlu Data

Connecting to Flowlu 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.

Start by setting the Profile connection property to the location of the Flowlu Profile on disk (e.g. C:\profiles\Flowlu.apip). Next, set the ProfileSettings connection property to the connection string for Flowlu (see below).

Flowlu API Profile Settings

In your Flowlu account, navigate to System Settings > API Settings > Create to generate an API key. Your Company name is the subdomain from your Flowlu account URL.

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

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

cnxn = mod.connect("Profile=C:\profiles\Flowlu.apip;ProfileSettings='APIKey=your_api_key;Company=your_company_subdomain';")

Create a SQL Statement to Query Flowlu

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

sql = "SELECT Id, Name FROM AgileCategories WHERE ProjectId = '1'"

Extract, Transform, and Load the Flowlu Data

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

Loading Flowlu Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Flowlu 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 API Driver for Python to start building Python apps and scripts with connectivity to Flowlu 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Flowlu.apip;ProfileSettings='APIKey=your_api_key;Company=your_company_subdomain';")

sql = "SELECT Id, Name FROM AgileCategories WHERE ProjectId = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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