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Extract, Transform, and Load Streak Data in Python

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

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

Connecting to Streak Data

Connecting to Streak 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.

Use the following steps to generate a new API key for authenticating to Streak.

  1. Navigate to Gmail
  2. Click on the Streak dropdown to the right of the search bar
  3. Select the Integrations button. This will open a window where you can view existing integrations and create new API keys.
  4. Under the Streak API section of integrations, click the button to Create New Key.

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

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

cnxn = mod.connect("ApiKey=8c84j9b4j54762ce809ej6a782d776j3;")

Create a SQL Statement to Query Streak

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

sql = "SELECT UserKey, Email FROM Users WHERE Email = 'user@domain.com'"

Extract, Transform, and Load the Streak Data

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

Loading Streak Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("ApiKey=8c84j9b4j54762ce809ej6a782d776j3;")

sql = "SELECT UserKey, Email FROM Users WHERE Email = 'user@domain.com'"

table1 = etl.fromdb(cnxn,sql)

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

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