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Extract, Transform, and Load Spark Data in Python

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

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

Connecting to Spark Data

Connecting to Spark 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.

Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

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

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

cnxn = mod.connect("Server=127.0.0.1;")

Create a SQL Statement to Query Spark

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

sql = "SELECT City, Balance FROM Customers WHERE Country = 'US'"

Extract, Transform, and Load the Spark Data

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

Loading Spark Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Spark

table1 = [ ['City','Balance'], ['NewCity1','NewBalance1'], ['NewCity2','NewBalance2'], ['NewCity3','NewBalance3'] ]

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

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

cnxn = mod.connect("Server=127.0.0.1;")

sql = "SELECT City, Balance FROM Customers WHERE Country = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['City','Balance'], ['NewCity1','NewBalance1'], ['NewCity2','NewBalance2'], ['NewCity3','NewBalance3'] ]

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