The Big Data Ingestion Platform that
Protects You from Bad Data

Reliable – Exactly-once, strongly-ordered data

Observable – Resolve data issues upon ingestion

Easy – No-code UI for batch, stream and DB sources

Fresh – Minute-fresh data in your warehouse and lake

upsolver sqlake screens

Bulletproof your Big Data Ingestion

Upsolver provides built-in data quality and observability as it ingests data from stream, file and database sources in real-time directly to your data warehouse or queryable staging tables in your data lake.

Data Reliability

Upsolver employs a unique monotonically-increasing time column plus a distributed locking mechanism to protect data from technical delivery issues and changes to data that regularly occur upstream.

  • Data availability – deliver on-time, exactly-once, strongly ordered data
  • Data evolution – detect data drift, automatically adapt to evolving schema
  • Data cleansing – filter data in-stream and mask sensitive data

Data Observability

If your data is polluted at the source, you need to know before it gets to your warehouse and lake. Upsolver monitors your incoming data and schema, identifies and alerts you to anomalies, and helps you diagnose and remediate any issues.

  • Detection / alerting - data and schema validation
  • Investigation - field stats, queryable info schema
  • Remediation - schema evolution and data replay

Easy - No-Code Configuration

Your data might be complex, but configuring ingestion shouldn’t be. Upsolver gives you a no-code IDE that enables you to set up ingestion in minutes.

Fresh - A Streaming Platform for Up-to-the-Minute Data

A major obstacle to improving data freshness is having to wait for downstream quality checks and fixes to run. Upsolver ensures the integrity of your data as it ingests in real time, so you can improve your downstream data freshness and simplify your data pipeline at the same time.

  • Real-time ingestion from Kafka, Kinesis, S3 and databases to live tables
  • Automatic table creation and schema evolution
  • Improved freshness by eliminating hand-coded quality fixes

Streamlined Operations

Upsolver is a fully managed cloud-native service that deploys to your AWS account. It scales automatically and can be configured for workload isolation. You can use the Upsolver Cloud as a development sandbox.

  • Elastically scale, with multi-cluster support for workload isolation.
  • Monitor job performance metrics such as throughout, latency and error rates
  • Automate anything - CI/CD, CLI, Python SDK, version control, rollback

Powering Big Data Ingestion 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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