Simplifying Pipelines for Every Data Engineer
No custom coding. No orchestration. No infrastructure maintenance.
Using Upsolver, we were analytics-ready and in production within 30 days with our existing staff.
With Upsolver, we had a data lake driving real value to our customers in weeks. Without it, it would have taken us months.
AWS led us to Upsolver. We saved months and didn't expend coding-heavy resources on data pipelines and infrastructure.
'Don’t reinvent the wheel' is one of the pillars of our data strategy. With Upsolver, I can see the most up-to-date data on Amazon S3, and I don’t need to manage complex architecture that provides the same functionality.
I told the Upsolver guys that I really don't need them anymore because everything just works. The adoption was really fast.
Upsolver has saved thousands of engineering hours and significantly reduced total cost of ownership, which enables us to invest these resources in continuing our hypergrowth rather than data pipelines
I chose Upsolver because time-to-analytics over Amazon S3 is 20X faster compared to Spark. Our existing staff deployed a production-ready solution within one month, which eliminated the risk of not being able to replace IBM Netezza on schedule.
Upsolver plays a crucial part in our core data infrastructure, and the team has proven to be a reliable partner that’s been committed to our success from day one.
Upsolver is completely self-serve. My team quickly became proficient with the platform, and our first stream was up in less than a day.
I used to spend dozens of hours on infrastructure - today I spend virtually none. Upsolver has made my life way better because now I can actually work on developing new features rather than coding and maintaining ETL pipelines/mark>.
With Spark, it used to be that every dashboard was considered ‘untouchable’ – as long as it was working, we didn’t want to break anything. Since we’ve started using Upsolver, we can make any change we want, it happens in literally minutes and it just works.
Upsolver makes big data much easier than it would be if we had to research all of the technology it covers. Furthermore, Upsolver has been very responsive to our requests for help and enhancements. Their support is phenomenal.
Upsolver provides us peace of mind, because now that we store everything in the data lake, I can reprocess the data in case we make a mistake or need to add new fields.”
Upsolver's ETL pipeline helped improve our efficiency and reduce the time from ingestion to insight from 24 hours to minutes.