RudderStack Advances Their Warehouse-First Approach with New Features, Cloud Extract and Warehouse Actions

RudderStack Cloud Extract and Warehouse Actions make it easy for data engineers to answer difficult questions with comprehensive customer data

SAN FRANCISCO--()--RudderStack, the smart customer data pipeline, announced the launch of two new features today, Cloud Extract and Warehouse Actions. These features make it easier for customers to use all of their customer data to answer more difficult questions and send those insights to their whole customer data stack.

Data engineers are often asked to answer difficult business questions. Answering these questions requires integrating data from their digital properties’ event streams and other data sources across their organization, from departments like product, sales, marketing, support, and finance. This is difficult because different departments frequently use their own cloud tools. Combining this with the added complexity of capturing customer event data means data engineers may end up spending more time building and maintaining data pipelines than solving business problems.

RudderStack Event Stream has been the customer data pipeline tool’s core feature. Event Stream makes event streaming simple with SDKs and plugins to instrument sites and apps, collect event data, and route it to a wide array of customer tools and data warehouses.

Event data is only a subset of customer data though. It’s still challenging to capture the non-event customer data locked into cloud applications, like Salesforce and ZenDesk, and correlate it with event data.

RudderStack Cloud Extract solves this. It makes it easy for data engineers to build ELT pipelines from cloud applications to their data warehouse, letting them aggregate all of their customer data in their warehouse, from customer event data generated by digital touchpoints to non-event customer data from different cloud applications.

The next problem data engineers encounter is activating the analysis they do on their warehouse. They are frequently asked to pull a list or build a model that produces data for business units to action on. When these processes need to be automated to satisfy closer-to-real-time requirements, data engineers are again faced with the task of building and maintaining data pipelines, this time out of their warehouse, instead of solving business problems.

RudderStack Warehouse Actions solves this. It lets data engineers use their warehouse as a data source for their whole customer data stack, making it easy to send the results from their analysis, materialized on their data warehouse, to the 80+ destinations RudderStack supports.

“At Proposify, we get a lot of traffic from organic search sources. Being able to have insight into Google Search Console data to monitor relevant search trends, keyword rankings, and landing page performance is crucial to inform everything from content to SEO and inbound marketing. RudderStack’s Cloud Extract lets us seamlessly integrate this data into our Redshift warehouse and data modeling workflows for a complete view of our acquisition efforts. It’s a powerful turnkey solution!”

- Max Werner, Data Operations Manager, Proposify

“We have a bunch of uses for Warehouse Actions, and we’re only at the tip of the iceberg really. A lot of what we want to do in the future will involve getting data out of the warehouse with Warehouse Actions.”

- Dan Baker, Marketing Operations Manager, Pachyderm

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About RudderStack

RudderStack is the smart customer data pipeline. Easily build pipelines connecting your whole customer data stack, then make them smarter by pulling analysis from your data warehouse to trigger enrichment and activation in customer tools, enabling identity stitching and advanced use cases like lead scoring and in-app personalization. Start building a smarter customer data pipeline today. Start building a smarter customer data pipeline today. Learn more at

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Gavin Johnson

Release Summary

RudderStack Cloud Extract and Warehouse Actions make it easy for data engineers to answer difficult questions with comprehensive customer data.


Gavin Johnson