Introducing Upsolver SQLake

Build Pipelines. Not DAGs.

Streaming plus batch in a single pipeline platform
No Airflow - orchestration inferred from data
$99 / TB of data ingested | unlimited free pipelines

Or alternatively, try SQLake for free

Webinar (CDC) Automated Orchestration Free Workshop Builders Hub

$99 per TB Ingested | Transformations Free | 30 Day Unlimited Trial

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% (over $1m/year saved).

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 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 4bn 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 dev ops time 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.

The Broken Pipeline Development Process

When you query a database, it automatically generates an execution plan. While a data pipeline is just query logic that runs continuously, data engineers are forced to spend countless hours manually building execution plans – a.k.a. coding orchestration DAGs.

Having to manually orchestrate data pipelines leads to:

Long analytics cycles – every pipeline is an engineering project
Unreliable and untrustworthy data
High cost from poorly optimized pipelines
Data engineering burnout from frequent break-fix cycles
Frustrated data consumers who can’t self-serve raw data

Write a Query -> Get a Pipeline

With SQLake, you use SQL to ingest streams and files into data lake tables and then create jobs which transform, join, and aggregate that data, and stream it into destination systems for analytics consumption.

How SQLake Simplifies Pipelines for Data-in-Motion

Unify Batch & Streaming

Batch and stream processing has traditionally required separate tools and skills. We unify batch and streaming sources into a single stream processing flow that minimizes complexity.

Escape DAG Hell

Mapping all pipeline success and failure modes is manual work in Airflow. It’s hard, time consuming and error-prone. SQLake’s unique architecture deterministically guarantees correct results in all cases.

Avoid Sticker Shock

Predictable, affordable pricing. You only pay for data you ingest ($99/TB, or < 10 cents / GB), which is retained and managed in the data lake. You build unlimited transformation pipelines for free.

Try For Free