Load data from Moat Analytics to PostgreSQL in a few clicks
Focus on your business, not on getting your Moat Analytics data into PostgreSQL. Build scalable, production-ready data pipelines and workflows in hours, not days.
Moat Analytics to PostgreSQL Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
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.
PostgreSQL is an open source object-relational database system. With more than 15 years of active development and a proven architecture, it has earned a strong reputation for reliability, data integrity, and correctness. It runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, macOS, Solaris, Tru64), and Windows.
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
“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
“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
Zero Friction, Quicker Uptime
"A reporting process that once required back-and-forth between different teams is now executed ad-hoc by team leads in minutes, cutting time to execution in half."
Jean Huang, Analytics Manager