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

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

Connecting to HBase Data

Connecting to HBase 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 Port and Server to connect to Apache HBase.

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=8080;")

Create a SQL Statement to Query HBase

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

sql = "SELECT CustomerName, Price FROM Customers WHERE ShipCity = 'New York'"

Extract, Transform, and Load the HBase Data

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

Loading HBase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to HBase

table1 = [ ['CustomerName','Price'], ['NewCustomerName1','NewPrice1'], ['NewCustomerName2','NewPrice2'], ['NewCustomerName3','NewPrice3'] ]

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=8080;")

sql = "SELECT CustomerName, Price FROM Customers WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['CustomerName','Price'], ['NewCustomerName1','NewPrice1'], ['NewCustomerName2','NewPrice2'], ['NewCustomerName3','NewPrice3'] ]

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