Load data from Moat Analytics to Databricks in a few clicks
Focus on your business, not on getting your Moat Analytics data into Databricks. Build scalable, production-ready data pipelines and workflows in hours, not days.
Moat Analytics to Databricks 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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Ask us anything. We have the best customer support in the industry, staffed with data experts who are ready to help solve your data challenges.
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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.
Databricks SQL allows users to operate a multi-cloud lakehouse architecture that provides data warehousing performance at data lake economics. Databricks SQL is based on Databricks' Delta Lake, an open source solution for building, managing and processing data using Lakehouse architecture. Benefits for users include: SQL-native interface, ability to create visualizations and share dashboards, data lake administration, reliability and governance for data lakes, and multi-cloud support.
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
“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
“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
"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