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Great Tool to Setup Data Ingestion pipeline
What do you like best about the product?
Upsolver enables its users to build Data Ingestion pipelines without setting up additional infrastructure. In the latest version, one can build he entire pipeline and schedule it with an SQL Like framework.
What do you dislike about the product?
Upsolver can look to integrate more Data stores for ingestion as well as data dumps.
What problems is the product solving and how is that benefiting you?
Upsolver is a Big Data Ingestion tool that provides a one-stop solution for building and orchestrating data pipelines. It manages all the infrastructure requirements under the hood.
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An ETL tool which facilitates data ingestion
What do you like best about the product?
* Ease of use
* UI simplicity
* Support team availability & professionalism
* UI simplicity
* Support team availability & professionalism
What do you dislike about the product?
* Sometimes, it takes time for the UI to get updated
* Couldn't find a simple login page;)
* Couldn't find a simple login page;)
What problems is the product solving and how is that benefiting you?
Preparing semi-structured log files, to be later queried by BI analytics tools
As a startup - upsolver offers a great platform to transfer , manage and transform data at ease!
What do you like best about the product?
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.
The best feature, In my opinion, is the fact that I can duplicate a "Flow" and send it to another destination.
What do you dislike about the product?
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
What problems is the product solving and how is that benefiting you?
Transferring raw data from streams, modeling the data into an aggregated data model (enriching it with Lookup Tables), and sending it to multiple resources using.
Working as Needed
What do you like best about the product?
Simplicity of performing transforms with SQL-like development language.
What do you dislike about the product?
Better documentation and examples of function use that is more helpful to understand the inputs and outputs.
What problems is the product solving and how is that benefiting you?
Transform of various data sources to the data warehouse for analysis. Separation of data engineering development to work on other higher value and pressing concerns.
Simplify cloud data pipelines, reduce TCO and TTM
What do you like best about the product?
Upsolver makes it easy to manage your cloud data pipelines and build your lake, warehouse, or other cloud database quickly and with increased agility.
What do you dislike about the product?
Nothing really, Upsolver has been fulfilling their promise
What problems is the product solving and how is that benefiting you?
We have been using Upsolver to build our data pipelines, data-lakes, data warehouses, and other databases, all with a single holistic architecture and used by different teams throughout the company. With upsolver have not only managed to proving data internally across the company, but also been using it to build new data-powered features into our product.
Data & BI Senior Manager
What do you like best about the product?
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.
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.
What do you dislike about the product?
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
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
What problems is the product solving and how is that benefiting you?
Analytics big-data processing:
Joining stream-sets to memory-based lookups
Aggregating hourly data
Data enrichment
Upsolver is extremly extensive and quick for utillization in order to solve complex requirements which in the past required working around them or installing external components to acheive similar outputs.
Joining stream-sets to memory-based lookups
Aggregating hourly data
Data enrichment
Upsolver is extremly extensive and quick for utillization in order to solve complex requirements which in the past required working around them or installing external components to acheive similar outputs.
Amazing product, Incredibly brilliant team
What do you like best about the product?
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.
What do you dislike about the product?
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,
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,
What problems is the product solving and how is that benefiting you?
We use Upsolver to ingest CDC data from all operational data sources into our centerlized data lake for analytical purposes.
A good solution handled by capable and available engineers
What do you like best about the product?
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.
What do you dislike about the product?
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 repetative when trying to duplicate the use-cases. We eventually had to fallback to do most of our pipeline generation, using our own tools.
The UI can be quite cumbersome and repetative when trying to duplicate the use-cases. We eventually had to fallback to do most of our pipeline generation, using our own tools.
What problems is the product solving and how is that benefiting you?
We mostly use it to generate CDC real time tables and views. We benefit from the option to share a scalable cluster across multiple streaming pipelines and to option to use seperated replay units when needed. The API enabled us to define pipelines in a frictionless manner without needing to introduce the system for more users.
Recommendations to others considering the product:
Use Upsolver engineers to share your vision with. They are highly capable and are able to provide the best fit for you problem which may require additional work on Upsolver's side.
A great solution for providing fast data
What do you like best about the product?
What I like best on Upsolver is the speed which can return large amounts of data to our platform.
What do you dislike about the product?
I dislike the limits on filtering data at the lookup tables.
What problems is the product solving and how is that benefiting you?
Our problem was to delivery fast and detailed statistics to our web application. Upsolver helped us with that and the scalability that we needed to get bigger customers.
DataLake Made Easy
What do you like best about the product?
* 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
* 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
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Automatic update of datasets in different customer projects
Recommendations to others considering the product:
Great modern data tool with the right flexibility to support multiple data use cases when starting or scaling up cloud datalake platforms.
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