Product overview

Data generated by your product applications and services is your biggest lever to delivering differentiated product and user experiences. 

Upsolver helps you extend your business moat by unlocking the full value of your moat data: data that’s uniquely yours provides the best looking glass into how your users benefit from your products and services—and what’s lacking.

upsolver sqlake screens

Addressing the scaling bottleneck of data movement

Level of effort and engineering complexity increase disproportionately with increasing scale of production data to be ingested. Self-serve solutions are designed for small business data obtained through third party SaaS connectors, which caps out around 1TB/month.

Beyond that, data ingestion relies on home-grown solutions leveraging low-level—often open source—software and a lot of scripting. That’s the bottleneck we’re here to open up. Upsolver provides a self-serve, flexible-development, cloud-native solution that can scale infinitely.

CDC that autoscales

  • A modified debezium engine that doesn’t require Kafka Connect
  • Database replication that’s live, not dependent on a batch process
  • Automatic mapping of new and changed tables to the target without dev intervention
  • Large coverage of operational database types: Postgres, MySQL, MSSQL Server, MongoDB and more

Streaming data ingestion from prod message queues

  • Consume from the same message buses your services leverage to transfer operational log data and API events
  • Handle any data payload—nested, evolving, sparse, massive
  • Replay and reprocess data from a previous state without returning to source or rerunning pipelines
  • Support for all modern streaming services including Kafka and Kinesis
unlock complex and streaming data with declarative data pipelines chart



Production treatment for prod data


→ Guaranteed exactly-once processing and strong ordering of data

→ Mask, redact and tag sensitive data in realtime

→ Exclude and coalesce fields to avoid writing redundant data from source

→ Set quality expectations and drop or warn in case of expectation violation





Data Observability


→ Live and historical statistics on data volume catches anomalous flow

→ Cumulative statistics by field including:

→ data density
→ uniqueness
→ type evolution
→ distinct and top values

→ Incident forwarding to incident management software

→ Live alerting on unexpected values as well as new or stale fields

Let our experts show you around, and then start your free trial with confidence.

Start a Technical Deep Dive

Powering data movement for data developers across industries

Using Upsolver, we were analytics-ready and in production within 30 days with our existing staff.

Learn how Cox Automative modernizes log analytics at scale

With Upsolver, we had a data lake driving real value to our customers in weeks. Without it, it would have taken us months.

Learn how Proofpoint builds agile and scalable streaming pipelines

AWS led us to Upsolver. We saved months and didn't expend coding-heavy resources on data pipelines and infrastructure.

Learn how Sisense drives new insights from Amazon S3

'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.

Learn how Clearly built a high performance, low maintenance cloud data platform

I told the Upsolver guys that I really don't need them anymore because everything just works. The adoption was really fast.

Learn how AppsFlyer cut compute costs by 75% to save more than $1M/year

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

Learn how ironSource collects, stores, and prepares 20,000,000,000+ events daily

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.

Learn how Peer39 contextualizes billions of pages for targeting and analytics

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.

Learn how Bigabid built a state-of-the-art mobile marketing and real-time bidding system using Upsolver

Upsolver is completely self-serve. My team quickly became proficient with the platform, and our first stream was up in less than a day.

Learn how Clinch doubled the number of features available to clients every month

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>.

Learn how a single data engineer manages ETL pipelines for 4B events

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.

Learn how VICOMI cut devops cyles from weeks to minutes by switching from Spark to Upsolver

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.

How the Meet Group extracted real-time insights from streaming data using Upsolver and Amazon Athena

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.”

Learn how Gamoshi Saved 75% on real-time pipelines with Upsolver and AWS

Upsolver's ETL pipeline helped improve our efficiency and reduce the time from ingestion to insight from 24 hours to minutes.

Learn how SimilarWeb analyzes hundreds of terabytes of data with Amazon Athena and Upsolver


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