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

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

Connecting to Highrise Data

Connecting to Highrise 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.

Highrise uses the OAuth authentication standard. To authenticate to Highrise, obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Highrise. You will also need to set the AccountId to connect to data.

See the "Getting Started" section in the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;AccountId=MyAccountId;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Highrise

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

sql = "SELECT Name, Price FROM Deals WHERE GroupId = 'MyGroupId'"

Extract, Transform, and Load the Highrise Data

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

Loading Highrise Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Highrise

table1 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;AccountId=MyAccountId;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Name, Price FROM Deals WHERE GroupId = 'MyGroupId'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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