Load data from Oracle to BigQuery in a few clicks
Focus on your business, not on getting your Oracle data into BigQuery. Build scalable, production-ready data pipelines and workflows in hours, not days.
Oracle to BigQuery Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
Oracle & 190+ Other Data Connectors, Fully Managed For You
Connect easily to Oracle with 100% compatibility, regular API updates, and a wide range of other pre-built data connectors out of the box.
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Start analyzing your Oracle data in minutes with Rivery
Oracle is a database designed for enterprise grid computing, providing a flexible and cost effective way to manage information and applications. Enterprise grid computing creates large pools of modular storage and servers. There is no need for peak workloads, because capacity can be added or reallocated from the resource pools as needed.
BigQuery is Google's fully managed, serverless, petabyte scale, low cost enterprise data warehouse for analytics. There is no infrastructure to manage and it doesn't require a database administrator, helping companies focus on analyzing data using familiar SQL.
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