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

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

Connecting to HDFS Data

Connecting to HDFS 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.

In order to authenticate, set the following connection properties:

• Host: Set this value to the host of your HDFS installation.
• Port: Set this value to the port of your HDFS installation. Default port: 50070

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

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

cnxn = mod.connect("Host=sandbox-hdp.hortonworks.com;Port=50070;Path=/user/root;User=root;")

Create a SQL Statement to Query HDFS

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

sql = "SELECT FileId, ChildrenNum FROM Files WHERE FileId = '119116'"

Extract, Transform, and Load the HDFS Data

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

Loading HDFS Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for HDFS, you can work with HDFS 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 HDFS to start building Python apps and scripts with connectivity to HDFS data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.hdfs as mod

cnxn = mod.connect("Host=sandbox-hdp.hortonworks.com;Port=50070;Path=/user/root;User=root;")

sql = "SELECT FileId, ChildrenNum FROM Files WHERE FileId = '119116'"

table1 = etl.fromdb(cnxn,sql)

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

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