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Extract, Transform, and Load Salesforce Einstein Data in Python

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

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

Connecting to Salesforce Einstein Data

Connecting to Salesforce Einstein 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.

Salesforce Einstein Analytics uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with Salesforce Einstein Analytics.

See the Getting Started section of the CData data provider documentation for an authentication guide.

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

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

cnxn = mod.connect("OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Salesforce Einstein

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

sql = "SELECT Name, CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won'"

Extract, Transform, and Load the Salesforce Einstein Data

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

Loading Salesforce Einstein Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Salesforce Einstein

table1 = [ ['Name','CloseDate'], ['NewName1','NewCloseDate1'], ['NewName2','NewCloseDate2'], ['NewName3','NewCloseDate3'] ]

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

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

cnxn = mod.connect("OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','CloseDate'], ['NewName1','NewCloseDate1'], ['NewName2','NewCloseDate2'], ['NewName3','NewCloseDate3'] ]

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