Extract, Transform, and Load EnterpriseDB Data in Python

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EnterpriseDB Python Connector

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



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

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

Connecting to EnterpriseDB Data

Connecting to EnterpriseDB 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 required in order to connect to data.

  • Server: The host name or IP of the server hosting the EnterpriseDB database.
  • Port: The port of the server hosting the EnterpriseDB database.

You can also optionally set the following:

  • Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

  • User: The user which will be used to authenticate with the EnterpriseDB server.
  • Password: The password which will be used to authenticate with the EnterpriseDB server.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

  • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
  • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
  • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
  • SSLClientCertType: The certificate type of the client store.
  • SSLServerCert: The certificate to be accepted from the server.

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

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

cnxn = mod.connect("User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5444")

Create a SQL Statement to Query EnterpriseDB

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

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

Extract, Transform, and Load the EnterpriseDB Data

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

Loading EnterpriseDB 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 EnterpriseDB

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

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

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

cnxn = mod.connect("User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5444")

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')