Upsolver SQLake

Batch and streaming pipelines.
No DAGs.

Accelerate data lake queries

Real-time ETL for cloud data warehouse

Build real-time data products

Try SQLake
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SQLake Unifies Streaming and Batch Data Processing

SQLake lets you ingest from stream, file and database sources in real-time to queryable staging tables in your data lake, implement transformations that populate data lake target tables (for query engine access) or populate live target tables in your data warehouse.

Streaming and Batch Connectivity

SQLake ingests event streams, files and CDC events in real-time. Then it joins, aggregates and transforms the data, and outputs it live to a variety of analytic systems.

Powerful SQL for Pipelines

SQLake lets you perform stateful operations such as joins and aggregations at scale as well as over 150 SQL functions including table functions, data functions, window operations and transformations functions.

Its special functionality includes:

Architected for Automation | Write a Query, Get a Pipeline

SQLake slashes the time it takes to develop production pipelines by eliminating orchestration and automating other management and optimization tasks that take the bulk of an engineer’s time.

  • No DAGs — SQLake’s unique architecture eliminates the need for orchestration
  • Optimizations such as compaction, vacuuming and metadata management are handled automatically
  • Source schema is detected and mapped to staging tables automatically, including added fields
  • Tables are updated incrementally without full data scans

Streamlined Operations

SQLake is a cloud-native service that deploys to your AWS account. You can also use the Upsolver Cloud as a sandbox environment for education and working with sample data.

Recorded Demo - Data Ingestion With SQLake

Recorded Demo - Data Ingestion With SQLake

Upsolver SQLake Interface Walkthrough

Upsolver SQLake Interface Walkthrough

Creating an S3-to-Snowflake Data Pipeline with SQL

Creating an S3-to-Snowflake Data Pipeline with SQL

SQLake CLI Tool

Powering Real-Time Analytics at Data-Intensive Companies

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.

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

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