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

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

Connecting to Placid Data

Connecting to Placid 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.

Placid uses API Key authentication to control access to the API. API tokens are project-specific and can be obtained from your project settings on placid.app.

Using API Key Authentication

To obtain your API key, log in to placid.app, navigate to your project, open the project settings, and generate an API token from the API section. Note that each API token is scoped to a specific project.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Placid project API token.

Example connection string:

Profile=C:\profiles\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';

After installing the CData Placid Connector, follow the procedure below to install the other required modules and start accessing Placid 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 Placid 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 Placid Connector to create a connection for working with Placid data.

cnxn = mod.connect("Profile=C:\profiles\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';")

Create a SQL Statement to Query Placid

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

sql = "SELECT ,  FROM Collections WHERE  = ''"

Extract, Transform, and Load the Placid Data

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

Loading Placid Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Placid 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 Placid 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\Placid.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_project_api_token';")

sql = "SELECT ,  FROM Collections WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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