Load data from StreamRail to Redshift in a few clicks
Focus on your business, not on getting your StreamRail data into Redshift. Build scalable, production-ready data pipelines and workflows in hours, not days.
StreamRail to Redshift Data Pipelines Made Easy
Your unified solution for building data pipelines and orchestrating workflows at scale.
StreamRail & 190+ Other Data Connectors, Fully Managed For You
Connect easily to StreamRail 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.
Start analyzing your StreamRail data in minutes with Rivery
StreamRail is a video advertising technology platform, which provides over 200K publishers and ad networks with the technology to deliver and monetize video on any device. StreamRail thrives on solving complex technological challenges involving massive traffic, big data crunching, sophisticated algorithms, and real-time performance.
Amazon Redshift is a fast, fully managed data warehouse to analyze data using standard SQL and Business Intelligence (BI) tools. It enables companies to run complex analytic queries against petabytes of structured data (ETL to Redshift or ETL from Redshift), using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
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