Upsolver Reviews

My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data from web, mobile, and microservices sources from Amazon Kinesis with semi-structured JSON and frequent schema changes. The goal was to deliver near-real-time analytics on S3 and Redshift while reducing the complexity and fragility of existing custom Spark pipelines.

A specific example of how I used Upsolver in that project is that it handled the schema changes seamlessly. A new or modified JSON field did not break pipelines, which significantly improved stability in an agile environment. I used Upsolver for automatic schema evolution, and it was very useful for us.

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code.

My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines.

Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams.

A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

I think that Upsolver can be improved in orchestration because it is not a full orchestration tool. I believe it could be better in this regard. The cost needs attention at a very large scale. I think improvements regarding cost are important. Upsolver may be less suitable for very complex batch transformation, which could be an area to improve in the future.

I have been using Upsolver since 2023.

In my opinion, Upsolver is stable.

Upsolver's scalability is good because it demonstrated strong scalability in production environments. As a fully managed cloud-native platform, it automatically scaled to handle increasing data volume and throughput without requiring manual intervention or infrastructure tuning. During peak loads, the system was able to process higher event rates while maintaining stable latency.

I did not need the customer support, so I do not have experience with it.

Neutral

I did not previously use a different solution. This is the first time for me that I use this kind of solution.

My company does not have a business relationship with this vendor other than being a customer.

I have seen a return on investment because we reduced a lot of Spark code, and the time on onboarding new sources was reduced from weeks to days. The data engineering operational effort decreased by 30 to 40 percent.

My experience with pricing, setup cost, and licensing was a very good experience, but it is not a direct experience because it was not my responsibility. It was in charge of the customer. However, in general, it was a very good experience.

Before choosing Upsolver, I did not evaluate other options.

My advice to others looking into using Upsolver is that I would recommend it to organizations working with streaming and semi-structured data in the cloud. I would rate this product an 8.5 out of 10.

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself, which reduces the times for maintaining pipelines by up to 50%.

I think the best feature Upsolver offers is automatic schema evolution. The automatic schema evolution feature has helped my team by allowing Upsolver to automatically recognize and update the schema in our data warehouse on the go.

Upsolver has positively impacted my organization by reducing time to market.

I think Upsolver can be improved with deeper integration with external orchestration out of the box.

I would appreciate more clear dashboards with billing in real time as a needed improvement.

I have been using Upsolver for more than four years.

Upsolver is stable.

I would say Upsolver's scalability is eight out of 10 because of pricing.

Upsolver's customer support is great, rated at 10 out of 10.

I have been using Upsolver from the beginning.

I purchased Upsolver through the AWS Marketplace.

My company does not have a business relationship with this vendor other than being a customer.

I have definitely seen a return on investment, as I saved time and costs on DevOps.

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

Before choosing Upsolver, I evaluated a standard tool by AWS; at that time, it was not sufficient, but currently Airflow is strong enough and I could switch.

I have saved 50 to 60% on maintaining pipelines since using Upsolver. I advise others looking into using Upsolver that if you are comfortable with the scalability of the pricing, then use Upsolver. I do not think any changes are needed for the future. I have given this review a rating of 9.

I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs. 

We primarily serve financial service customers in India and around the globe. We use Upsolver as an ETL tool to move data from different sources into one destination quickly and at scale.

The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies. Upsolver does this in a quick time, unlike traditional processes which are time-consuming. 

Additionally, it offers scalability for large volumes of data, with performance and ease of cloud-native integration.

There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. 

Furthermore, allocating CPU and memory resources for complex queries could improve efficiency.

I started using Upsolver from the end of 2023 or the beginning of 2024.

On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it.

Upsolver's scalability is rated around eight out of ten. It performs well with large volumes of data and is designed for cloud-native applications.

Customer service is excellent, and I would rate it between eight point five to nine out of ten. They offer quick support in the Asia region and have multiple support centers globally. They maintain regular communication with customers through webinars and are very connected.

Positive

In Fidelity, I used different data products like Snowflake. We used other ETL tools to move data across platforms. I did not use Upsolver there since it was not mature enough at that time.

The initial setup is relatively easy, rated nine out of ten. It is cloud-based, eliminating the need for installation on a local system.

Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.

I rate Upsolver nine out of ten overall. 

It works well for medium to enterprise-level customers. If a company is managing a heterogeneous data environment, Upsolver could be beneficial, but smaller companies with simple needs might not receive as much value.

When I test-drove Upsolver for a consulting company, I used it in POC to stream and ingest data. The goal was to move data from a source, possibly SQL Server, into a destination like Snowflake or Redshift. The POC aimed to evaluate Upsolver against StreamSets, the competition for ETL tasks. The use case involved data aggregation, ingestion rules, landing data into a data lake, and handling ETL processes for a data warehouse.

It has simplified prices, time to set up, and ease of use.

It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality. Some customization was available, and I attended developer training that allowed me to explore it in more depth. At the time of my evaluation, features like iceberg tables were not present on the platform but are now included based on your current website.

It was easier to use than the competition. I liked the real-time ingestion and transformation features. The wizard-based guidance was particularly notable.

Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future.

I have been using Upsolver since 2020.

There were no stability issues.

It is very scalable. We didn't foresee any issues.

Some prior knowledge is helpful. A novice user might still encounter confusion, especially if they skip steps during tenant setup and run into permissions errors. 

It was competitively priced and within the customer's budget.

I recommend test-driving it for ease of use and peace of mind. If you’re exploring ETL tools and want to guide your curiosity and market survey, it's a good idea to leap for a test drive.

Overall, I rate the solution an eight out of ten.