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Extract, Transform, and Load Alfresco Data in Python

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

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

Connecting to Alfresco Data

Connecting to Alfresco 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.

To connect to Alfresco, the following connection properties must be supplied: User, Password, and InstanceUrl. User and Password correspond to the login credentials that you use to access Alfresco in a web browser. InstanceUrl corresponds to the Alfresco instance you will be querying. For instance, if you expect your queries to hit https://search-demo.dev.alfresco.me/alfresco/api/-default-/public/search/versions/1/sql, you should supply search-demo.dev.alfresco.me for InstanceUrl.

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

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

cnxn = mod.connect("User=MyUsername; Password=MyPassword; Format=Solr; InstanceUrl=api-explorer.alfresco.com;")

Create a SQL Statement to Query Alfresco

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

sql = "SELECT DBID, Column1 FROM Alfresco WHERE Column2 = 'MyFilter'"

Extract, Transform, and Load the Alfresco Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Alfresco data. In this example, we extract Alfresco data, sort the data by the Column1 column, and load the data into a CSV file.

Loading Alfresco Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

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

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

cnxn = mod.connect("User=MyUsername; Password=MyPassword; Format=Solr; InstanceUrl=api-explorer.alfresco.com;")

sql = "SELECT DBID, Column1 FROM Alfresco WHERE Column2 = 'MyFilter'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

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