Upsolver enables its users to build Data Ingestion pipelines without setting up additional infrastructure. In the latest version, one can build the entire pipeline and schedule it with an SQL Like framework. Review collected by and hosted on G2.com.

Upsolver can look to integrate more Data stores for ingestion as well as data dumps. Review collected by and hosted on G2.com.

With the platform's simplicity, it is effortless to set up a source connector, transform the data using a simple SQL editor and send it wherever I want. The best feature, in my opinion, is the fact that I can duplicate a "Flow" and send it to another destination. Review collected by and hosted on G2.com.

The UI is a little bit unpleasant to the human eye, but it is a small thing compared to the system's functionality and simplicity Review collected by and hosted on G2.com.

* Ease of use

* UI simplicity

* Support team availability & professionalism Review collected by and hosted on G2.com.

* Sometimes, it takes time for the UI to get updated

* Couldn't find a simple login page;) Review collected by and hosted on G2.com.

Simplicity of performing transforms with SQL-like development language. Review collected by and hosted on G2.com.

Better documentation and examples of function use that is more helpful to understand the inputs and outputs. Review collected by and hosted on G2.com.

Upsolver enabled us to generate real time tables over streaming inputs, a problem that took us endless resources to solve. The interface is fairly intuitive and API first. The engineers help us to solve all our use-cases and are very driven to create a robust and stable infrastructure. Support handled by the engineers quickly and generously 24/7. Review collected by and hosted on G2.com.

As Upsolver still suffers from maturity issues, we still have many Bugs, irregular and unpredictable behavior. Some of the releases may contain regressions. The UI can be quite cumbersome and repetitive when trying to duplicate the use-cases. We eventually had to fallback to do most of our pipeline generation, using our own tools. Review collected by and hosted on G2.com.

We came to POC Upsolver after facing issues with streaming CDC data effectively into our S3 data lake for a long time. We found the solution so refreshing! performance was unbelievable, and at a fraction of the cost we used to spend using other (open source) technologies. We found the Upsolver staff to be super intelligent and responsive, and we now see them as partners with whom we can grow. It is also important to note that output data remains in Parquet format which relieves us from confronting hard decisions around our dependency in the SW and gives us the ability to consume the outputs using any technology we wish, free of dependencies. Review collected by and hosted on G2.com.

I find the UI and the configurations to be a little confusing (and prone to mistakes, in our use case), which brought us to enable internal users at Yotpo to deploy new pipelines using a CLI interface we constructed over Upsolver's REST API. Also, processing sizeable historical data sets spread over a wide time frame can be slow and expensive. Complex data pipelines, including multiple sources and triggers, are hard to implement through Upsolver (as through most streaming infra today) using lookup tables and non-intuitive syntax, I feel as if this area has much potential and I'm certainly looking forward to what Upsolver design would be for these use cases in the (hopefully near) future. Review collected by and hosted on G2.com.

We have evaluated Upsolver for replacing an existing batch-based MPP Analytics pipeline. The main reasons we eventually decided to go with Upsolver for, are: 1. Fast Time-To-Market - implementation time of production-grade solution goes down to a fraction compared to a fully-self-developed solution using either open or closed source tools. 2. Highly efficient \ Cost-Effective A. The number of developers and IT\devops personnel required to operate the solution is much lower than traditional solutions. B. real ETL-as-a-service - Upsolver is doing it all for you; it leaves little room for mistake by providing end-to-end coverage of the full application needs - from data loading and scanning, through queuing and up to built-in monitoring capabilities. C. Compute resources are utilized wisely and precisely to reduce cost as much as possible. D. you have 0 cost for maintaining the operation 3. Scalable and Flexible: OOB support for many connectors to either read data from, or output data to. Smart automatic scaling capabilities by utilizing pre-built scaling strategies. Upsolver SQL is enriched by all standard and advanced SQL capabilities (json, arrays etc...) and also supports extended capabilities for manipulting special data (eg. geo\ip, URL and more). 4. API and serving: All Upsolver capabilities are provided in API - this gives many options to automate processes. Upsolver Lookup is blazing fast when used as key-value store for serving external API needs, with high scale and throughput. This is a big advantage for specific use-cases, and we use it to query our streaming data from our Web API. Review collected by and hosted on G2.com.

After using the platform for over 1 year, main drawbacks are: 1. Development cycle is limited; automated deployment cycle is difficult to achieve since Upsolver account is tied to a cloud account where development environments are maintained in a separate model. 2. The tool is aimed for streaming-aggregates data pipeline and lacks the support for more batch-oriented (classic) ETL needs (e.g. controlling the time of a particular output) 3. Incurred license and cloud costs should not be ignored while developing the Upsolver pipeline (as with any cloud-based solution for data processing). Adding Logic\pipelines and\or compute resources can go "under the radar" quickly, and cost can rise, especially if a developer has no access to the cloud resources console. * Especially for a project with strict budget borders Review collected by and hosted on G2.com.

* Intuitive GUI - can be operated by users who are not fluent with SQL or programming

* Modular Pipelines - easy to create different pipeline that are using data for different use cases

* Multiple Connectors - easy to connect to data in different data stores and to update into different ones as well Review collected by and hosted on G2.com.

Focus on Streaming and minimal support for old ETL - some of our customers have old or legacy data infrastructure and need simpler capabilities compare to the modern streaming support of UpSolver. You can still use the product, however, you need to wait for the streamingn to catch up, even when no streaming is needed. Review collected by and hosted on G2.com.

Real PaaS in the deep meaning of the words. Data flow in my infrastructure and the app connect perfectly with it. Review collected by and hosted on G2.com.

They limited OOB connectors for non-aws apps Review collected by and hosted on G2.com.

Upsolver solved a genuine problem within the data integration space, streaming data in minimum latency and cost. I love the easy setup and integration. customer support is one of the best I've seen. Review collected by and hosted on G2.com.

As new data formats are becoming the new standard such as Delta, Iceberg & Hudi, Would love to see some sort of implementation to of those formats within the platform. The UI requires a lot of tinkering in order to get something running, and it's easy to get confused and make mistakes, Review collected by and hosted on G2.com.