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Zendesk Chat
Azure Data Lake

Integrate Zendesk Chat with Azure Data Lake

Don't go through the pain of direct integration. RudderStack’s Zendesk Chat integration makes it easy to send data from Zendesk Chat to Azure Data Lake and all of your other cloud tools.

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Easy Zendesk Chat to Azure Data Lake integration with RudderStack

RudderStack’s open source Zendesk Chat integration allows you to integrate RudderStack with your Zendesk Chat to track event data and automatically send it to Azure Data Lake. With the RudderStack Zendesk Chat integration, you do not have to worry about having to learn, test, implement or deal with changes in a new API and multiple endpoints every time someone asks for a new integration.

Popular ways to use Azure Data Lake and RudderStack

Query customer support data

Import analytics-ready customer support data into your warehouse. Select the data points you need and sync with the click of a button.

Perform deep support ticket analysis

Run analytics and ML on your raw support ticket data to gain a better understanding of sentiment and outcomes.

Improve support training

Build richer, data-driven training programs for your sales reps based on actual ticketing and response data.

Frequently Asked Questions

Depending on the format, there are multiple ways to export data from Zendesk Chat, whether manually, through a native integration or through a data platform like RudderStack.
There are two primary ways: First, you could write your own code script to pull the data and load it via API. Second, you could use an ETL integration provider like RudderStack to load the data automatically.
Building your own integration between Zendesk Chat and Azure Data Lake costs engineering time, while ETL providers like RudderStack generally charge by the number of rows loaded.

RudderStack Azure Data Lake Documentation

Refer to our step-by-step guide and start using Azure Data Lake today

About Azure Data Lake

Microsoft Azure Data Lake is a secure and scalable data lake that lets you store data of varying sizes and complexity. It also allows you to write your own code to perform operational or transactional data transformation and analysis tasks.