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Upsolver

Upsolver

Overview

What is Upsolver?

Upsolver is an In-Memory Data Preparation Platform that aims to remove the complexity from Big Data and Real-Time projects, and shorten their implementation time from weeks/months to several hours.Powered by a cutting edge VolcanoTM technology, it queries an entire…

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Recent Reviews

TrustRadius Insights

Upsolver has proven to be a versatile solution for users who need to work with semi-structured log files and other forms of raw data. By …
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Pricing

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What is Upsolver?

Upsolver is an In-Memory Data Preparation Platform that aims to remove the complexity from Big Data and Real-Time projects, and shorten their implementation time from weeks/months to several hours. Powered by a cutting edge VolcanoTM technology, it queries an entire data lake in less…

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Details

What is Upsolver?

Upsolver is an In-Memory Data Preparation Platform that aims to remove the complexity from Big Data and Real-Time projects, and shorten their implementation time from weeks/months to several hours.


Powered by a cutting edge VolcanoTM technology, it queries an entire data lake in less than a millisecond and stores 10x more data in RAM - allowing you to meet any scale and performance needs without complex data engineering work.


Upsolver is packaged as a Public or Private Cloud solution, easily configured using a WYSIWYG UI and streaming SQL engine.

Upsolver Features

  • Supported: Standard ansi-SQL functions
  • Supported: Advanced functions for numbers, strings, dates and arrays
  • Supported: Extraction of geo data from IPs (using MaxMind)
  • Supported: User agent parser
  • Supported: Filters
  • Supported: User defined functions in Python
  • Supported: Regular or aggregated output tables
  • Supported: Sessionization
  • Supported: Accurate count distinct in real-time and at scale
  • Supported: Nested aggregations for user/device profiling
  • Supported: Monitoring

Upsolver Screenshots

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Upsolver Integrations

Upsolver Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(1)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Upsolver has proven to be a versatile solution for users who need to work with semi-structured log files and other forms of raw data. By ingesting CDC data from various sources, Upsolver allows users to build data pipelines, transfer raw data, enrich it with Lookup Tables, and send it to multiple resources using Transform. This not only simplifies the process but also shortens development times, saving manpower and infrastructure.

The software is also capable of generating real-time tables and views for CDC data, enabling sharing of scalable clusters across multiple streaming pipelines. Different teams across the company can use a single holistic architecture to build new features into their products such as analytics big-data processing or aggregating hourly data without installing external components or working around complex requirements. The API enables users to define pipelines without introducing the system to more users. With Upsolver, users have been able to recognize source schemas quickly and easily, which has helped them meet product SLAs and open up new opportunities they had previously not seen as possible.

Efficient and cost-effective solution: Reviewers have praised Upsolver as a highly efficient and cost-effective solution for managing cloud data pipelines. Several users have mentioned that the platform has helped them build data lakes, warehouses, or other cloud databases quickly with increased agility. They appreciate Upsolver's ability to reduce implementation time of production-grade solutions compared to fully-self-developed solutions using open or closed source tools.

End-to-end coverage: Users find Upsolver to be a comprehensive solution that covers all application needs from data loading and scanning to queuing and built-in monitoring capabilities. Reviewers note that this comprehensive coverage leaves little room for mistake while saving time, effort, and resources in building an end-to-end pipeline.

Scalable and flexible: Many reviewers mention that they appreciate how scalable and flexible Upsolver is. The platform offers out-of-the-box support for many connectors to either read data from or output data to, which helps streamline the process significantly. Additionally, its smart automatic scaling capabilities allow users to utilize pre-built scaling strategies easily.

Confusing UI: Several users have found the Upsolver UI to be confusing and unpleasant to look at. It requires a lot of tinkering, leading to mistakes that can be frustrating for users.

Maturity issues with bugs and unpredictable behavior: Many reviewers have reported experiencing maturity issues while using Upsolver. The software is prone to bugs and unpredictable behavior, making it difficult for users to rely on its performance.

Limited support for classic ETL needs: Some customers have noted that Upsolver is not suitable for projects with strict budget constraints as adding Logic pipelines and/or compute resources can quickly increase costs. Additionally, the tool lacks support for more batch-oriented classic ETL needs, making it unsuitable for some use cases.

Reviews

(1-1 of 1)
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Martin Lance | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Define pipelines using only SQL on the auto-generated schema on reading. Add upserts and deletes to data lake tables. Blend streaming and large-scale batch data. Automate schema evolution and reprocessing from the previous state. Allows for automatic orchestration of pipelines. Capacity to fully manage execution at scale. Strong consistency is guaranteed over object storage.
  • Data lake table management.
  • High performance at scale on complex data.
  • Capacity to parquet based data for fast queries.
  • Enables low latency dimension tables using streaming upsets.
  • Continuous lock free compaction.
  • Automatic schema on read and data profiling.
Data lineage visibility from source to the lake to target. Effective transactional data from databases using JDBC or CDC. Integration with lake query engines. Automated use of low-cost spot instances. Automated use of low-cost cloud object storage. Automated vacuum at stale and intermediate data. Continuous, high integrity table management.
  • Lower cloud compute and data engineering cost.
  • Free for small workloads.
  • Connects faster to a solution guru whenever a problem arise.
  • Integrations and connectors.
  • Effective stream processing engines.
  • Ability to write to Amazon.
Great in streamlining workload. Continuously serve data to lakes, warehouses, databases, and streaming systems. Near-zero maintenance overhead for analytics ready data. Blend streaming and large-scale batch data. Low code, SQL-based data transformation. UI-driven ingestion connections with auto-generated schema on reading. Automated pipeline orchestration with built-in data lake best practices.
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