Load data from ShareASale to Google Cloud Storage in a few clicks
Focus on your business, not on getting your ShareASale data into Google Cloud Storage. Build scalable, production-ready data pipelines and workflows in hours, not days.
ShareASale to Google Cloud Storage Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
ShareASale & 190+ Other Data Connectors, Fully Managed For You
Connect easily to ShareASale 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 ShareASale data in minutes with Rivery
ShareASale is an affiliate marketing network that services two customer sets in affiliate marketing: the affiliate, and the merchant.
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.
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