Extract, Transform, and Load Avro Data in Python

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Avro Python Connector

Python Connector Libraries for Avro Data Connectivity. Integrate Avro with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Avro Data

Connecting to Avro 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 Avro file(s) by setting the URI connection property to the location of the Avro file.

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

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

cnxn = mod.connect("URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Avro

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

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'"

Extract, Transform, and Load the Avro Data

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

Loading Avro 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 Avro, you can work with Avro 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 Avro Python Connector to start building Python apps and scripts with connectivity to Avro 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.avro as mod

cnxn = mod.connect("URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'"

table1 = etl.fromdb(cnxn,sql)

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

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