Load data from Moat Analytics to Google Cloud Storage in a few clicks
Focus on your business, not on getting your Moat Analytics data into Google Cloud Storage. Build scalable, production-ready data pipelines and workflows in hours, not days.
Moat Analytics to Google Cloud Storage Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
How to set up a connection from Moat Analytics to Google Cloud Storage
Moat Analytics & 190+ Other Data Connectors, Fully Managed For You
Connect easily to Moat Analytics with 100% compatibility, regular API updates, and a wide range of other pre-built data connectors out of the box.





















































































































































































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About Moat Analytics
Moat is an analytics and measurement company that offers viewability, attention, and brand safety solutions across display, video, mobile, and more.


About Google Cloud Storage
Google Cloud Storage is unified object storage for developers and enterprises, from live data serving to data analytics/ML to data archiving. Google Cloud Storage is practically infinitely scalable, whether it's used to support a small application or a large exabyte-scale system.
Why Rivery?
Rivery's SaaS platform provides a unified solution for ingestion, transformation, orchestration, and data operations.



“We saved several $100K we could have spent on development and maintenance. Within a few hours, you can build a production-ready, scalable ETL system.”
Gal Bar, Founder and CEO
Data Process Automation
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“We solved some of our most complex data challenges with Rivery. The ability to create a unified data pipeline that is always up-to-date has been a game changer.”
Tali Stern, Director of Business Intelligence
Link Any Data Source
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“Rivery has more than delivered on the value proposition I sold my leadership on. Rather than hiring two more developers, I’ve been able to build all these pipelines on my own.”
Sean Lucas, Head of Data Engineering
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