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

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

Connecting to AlloyDB Data

Connecting to AlloyDB 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.

The following connection properties are usually required in order to connect to AlloyDB.

• Server: The host name or IP of the server hosting the AlloyDB database.
• User: The user which will be used to authenticate with the AlloyDB server.
• Password: The password which will be used to authenticate with the AlloyDB server.

You can also optionally set the following:

• Database: The database to connect to when connecting to the AlloyDB Server. If this is not set, the user's default database will be used.
• Port: The port of the server hosting the AlloyDB database. This property is set to 5432 by default.

Authenticating with Standard Authentication

Standard authentication (using the user/password combination supplied earlier) is the default form of authentication.

No further action is required to leverage Standard Authentication to connect.

Authenticating with pg_hba.conf Auth Schemes

There are additional methods of authentication available which must be enabled in the pg_hba.conf file on the AlloyDB server.

Find instructions about authentication setup on the AlloyDB Server here.

Authenticating with MD5 Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to md5.

Authenticating with SASL Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to scram-sha-256.

Authenticating with Kerberos

The authentication with Kerberos is initiated by AlloyDB Server when the ∏ is trying to connect to it. You should set up Kerberos on the AlloyDB Server to activate this authentication method. Once you have Kerberos authentication set up on the AlloyDB Server, see the Kerberos section of the help documentation for details on how to authenticate with Kerberos.

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

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

cnxn = mod.connect("User=alloydb;Password=admin;Database=alloydb;Server=127.0.0.1;Port=5432")

Create a SQL Statement to Query AlloyDB

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

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

Extract, Transform, and Load the AlloyDB Data

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

Loading AlloyDB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to AlloyDB

table1 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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

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

cnxn = mod.connect("User=alloydb;Password=admin;Database=alloydb;Server=127.0.0.1;Port=5432")

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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