Load data from YouTube to Amazon S3 in a few clicks
Focus on your business, not on getting your YouTube data into Amazon S3. Build scalable, production-ready data pipelines and workflows in hours, not days.
YouTube to Amazon S3 Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
YouTube & 190+ Other Data Connectors, Fully Managed For You
Connect easily to YouTube with 100% compatibility, regular API updates, and a wide range of other pre-built data connectors out of the box.
We've Got Your Back
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.
Start analyzing your YouTube data in minutes with Rivery
YouTube is the world's leading a video-sharing platform, which allows users to upload, view, and share videos. YouTube analytics and reporting APIs use dimensions and metrics to aggregate data and measure user activity.
About Amazon S3
Amazon Simple Storage Service (Amazon S3) is object storage with a simple web service interface to store and retrieve any amount of data from anywhere on the web. Customers use S3 as primary storage for cloud-native applications; as a bulk repository, as a data lake for analytics; or as a target for backup, recovery, and disaster recovery with serverless computing.
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