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

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

Connecting to Parquet Data

Connecting to Parquet 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.

Connect to your local Parquet file(s) by setting the URI connection property to the location of the Parquet file.

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

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

cnxn = mod.connect("URI=C:/folder/table.parquet;")

Create a SQL Statement to Query Parquet

Use SQL to create a statement for querying Parquet. In this article, we read data from the SampleTable_1 entity.

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'SAMPLE_VALUE'"

Extract, Transform, and Load the Parquet Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Parquet data. In this example, we extract Parquet data, sort the data by the Column1 column, and load the data into a CSV file.

Loading Parquet Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')

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

cnxn = mod.connect("URI=C:/folder/table.parquet;")

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'SAMPLE_VALUE'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')