The First Indexing Engine for Data Lakes

Query Amazon S3 by any set of keys at high-throughput and milliseconds-latency using a REST API, without the overhead of managing any additional data stores.

Key Benefits

10-15x

More data in memory compared to NoSQL databases

50k

Reads per second, per server, at 1ms latency on average

100x

Faster time to production for real-time analytics and machine learning

What are Upsolver Lookup Tables?

Lookup Tables add indexing at high cardinality and performance to your data lake. They enable users to index data by a set of keys and then retrieve the results in milliseconds. Unlike NoSQL alternatives, Upsolver’s ETL platform stores indexed data on S3 rather than local servers, which turns IT-intensive cluster management into a non-issue. Lookup Tables leverage break-through compression technology and smart rollups that enable 10X-15X more data in-memory compared to alternatives.

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Upsolver Lookup Tables
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Key Benefits and Feature Highlights

Fully Secure

Reduce 70-90% of infrastructure costs by storing more data in RAM

Elastic Scaling

Decoupled compute and storage for easy healing, scaling, and disaster recovery

Shockingly Easy

Easy to create without ETL coding or IT management using aself-service UI and UpSQ

Query by any set of keys or time range.

Lookup Tables are stored on S3 as a time-series. Using smart rollups, Upsolver makes it possible to query any time range by any set of keys.

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Query S3 by any time window
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Rich library of out-of-the-box window aggregations

Capture real-time behaviour for users and devices, using window aggregations, nested aggregations and time-series aggregations.

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window aggregations
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Simple REST API

Embed granular user or device data in your applications using a simple REST API and avoid the overhead of additional data stores.

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Upsolver API call example
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Learn more:

ironSource built a multi-purpose data lake with Upsolver

Read this case study to learn how Upsolver helped ironSource save thousands of engineering hours and cut costs.

Read the Case Study »

Partitioning Data on S3 to Improve Performance in Athena/Presto

Discover best practices you need to know in order to optimize your analytics infrastructure for performance.

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AWS Athena Challenges & Best Practices

Learn how to avoid common pitfalls, reduce costs and ensure high performance for Amazon Athena.

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Webinar: Improving Athena + Looker Performance

Instantly improve performance and get fresher, more up-to-date data in dashboards built on AWS Athena – all while reducing querying costs

Watch Webinar »
data lake ETL Demo

Let’s get personal:
See Upsolver on your data in a live demo.

Schedule a free, no-strings-attached demo to discover how Upsolver can radically simplify data lake ETL in your organization.

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