External reviews are not included in the AWS star rating for the product.
My main use case for Rivery is classic ETL, bringing data from business data sources into a central data warehouse.
A quick specific example of a business data source I bring in is Salesforce, where I bring in opportunities, accounts, and other data. We then manipulate that data in order to create dashboards, reports, or automations.
The best features Rivery offers include simplicity; it is a very simple tool that I can teach very quickly to my developers and analysts. I do not need classical data engineer skills in order to use it. It is also very friendly for analysts who can understand what is happening in their ETL pipelines and even do some work instead of data engineers.
The second point is flexibility, as I can use Rivery's classic SaaS built-in connectors, and we utilize a lot of custom connectors that are not built into the product, but we write them in a very simple way. We use Python, so we have the whole package from a very simple GUI mechanism into high-level coding if we need it.
Among the features I mentioned, I personally find myself using or appreciating the flexibility the most in my day-to-day work.
Rivery has positively impacted my organization by reducing the need for a big team of data engineers and speeding up the work when we need to connect to a new data source; this can happen really fast. I cannot provide specific numbers on how much time I have saved, but I can say that the team did not grow exponentially as the headcount in the whole company, which is a good sign.
Rivery can be improved by generally investing a bit more depth into the product, mainly in orchestration and specific features comparable to mature products. They also need to improve their logging and orchestration capabilities.
I have been using Rivery for around five to six years.
Rivery is stable.
Rivery handles increasing amounts of data or more complex workloads quite well, as we did not experience any issues with that.
The support is generally great, and while the user interface is acceptable, if I have an issue, it is a bit hard to understand what happened, so many times I need to reach out to support if I have issues; this is a bottleneck.
Customer support is great; they are answering really fast.
We did not use previous solutions; we are Rivery customers from day one.
I think we needed fewer employees and saved time, as I mentioned.
I found the pricing and licensing to be fair and competitive compared to other solutions I have seen.
Rivery is a great product; I recommend checking it out. I would rate Rivery as an eight out of ten because it is generally a great product, but it still needs some improvements.
My main use case for Rivery during those three months was in-depth training as well as assessments on all aspects, specifically the incremental load or CDC. We faced some challenges there. Some bugs prevented us from executing the SCD thing and the incremental load properly, which I reported back to Als Mistry. My efforts mostly involved ingesting data from the source, creating a warehouse, using various transformation logic, and utilizing custom user-defined functions or scripts. The process included Python elements, making it end-to-end from source to target ETL processes.
A specific example of a data flow I set up with Rivery during that time was when we tried to implement the SCD type 2 logic while building our data mart. We explored different approaches using both custom Python scripts and the functions provided by Rivery for the ELT process. I do not remember all the functions we used since it has been more than three years and I only used Rivery for about three months. The key challenges I recall were related to the incremental merging logic, which was not functioning properly.
The best feature Rivery offers is the ability to build custom or user-defined functions. You can even develop Python scripts to perform transformations on your data frames. This flexibility allows you to implement custom requirements, making Rivery more versatile than relying solely on in-built functions.
Regarding features such as the interface, scheduling, or connectors, I found that as of 2022 when I last used it, the monitoring was good, although the debugging process for custom scripts was somewhat challenging. If we encountered issues with custom-built scripts, debugging was difficult since it used to send standard errors rather than specific ones. From what I recall, monitoring worked well, and we could connect to multiple relational and other sources, which was advantageous.
A few of my colleagues and I were able to earn certifications on Rivery, which motivated us, even though we could not pursue or implement Rivery project for clients. The learning experience was very valuable as we had around seven or eight resources participating in those trainings, and they were all excited to learn about this new tool for us at the time.
To improve Rivery, I would compare it with another ETL tool called Informatica. One feature that stood out in Informatica was the ability to see data flowing through each transformation step while debugging, which I felt was missing in Rivery. Incorporating a similar feature would greatly benefit users needing to debug data processing at each step.
Regarding improvements, support from Rivery team was great, but concerning documentation, I am not quite sure about the current state since it has been a while since I last used Rivery. On the support side, we received really good assistance from Rivery team.
I have been using Rivery since August 2022 when our company was about to start a project. They were trying to use Rivery, and our company had a tie-up with Rivery company itself. Als Mistry, the solution architect at Rivery, provided a few days of training followed by assessments on Rivery side. We used Rivery for about two to three months, but unfortunately, we were not able to pursue that project, so I used Rivery for about three months back in 2022 and it has been almost three years since I have not used it after that.
From my experience, Rivery is generally stable. The excellent support we received from Rivery team contributes to this perception. Despite a few glitches, I would still classify Rivery as stable.
During my testing, while processing large amounts of data using Python, I observed that the performance was a bit slow, which is one of my observations. However, I believe it should have improved by now, considering it has been three years since I last worked with it.
The support from Rivery team was great. Despite a few issues with debugging, the overall assistance provided by Rivery team contributed to the stability perception.
The motivation and learning impacted my team significantly. Learning occurs when you face challenges. One significant challenge was implementing custom-built Python scripts using Rivery for transformations. The difficulties we faced could stem from either Rivery limitations or the Python library functions, especially since Python was emerging in 2022 and perhaps not all features were fully supported. Due to these challenges, my team delved deeper into Python, and we reported issues related to the CDC incremental load that we were trying to implement, which were not occurring properly on Rivery side. We received feedback after about a week from Rivery team.
Neutral
I did not use Rivery extensively in real-case scenarios, as I am a consultant working for clients, and whatever tools and technology they employ, I work on that front. In my 14 years of experience, I have not had the opportunity to work with a client who uses Rivery in a real-world scenario.
Anyone looking into using Rivery should know it is a great product. Rivery user interface is very good as well, plus you have the advantage of user-defined functions and many pre-built functions. This makes Rivery a solid choice.
The reason I chose a rating of 7.5 out of 10 is that the pros included the capability to use user-defined functions, custom-built scripts, and the custom-built transformations already provided by Rivery. On the con side, the debugging process was quite hard for specific errors. Common errors were manageable, but debugging specific ones was challenging. If you could, for instance, process a thousand rows and instead sample five to investigate errors, that would be beneficial. I rate Rivery 7.5 out of 10 because there were a few improvements that needed to be addressed, and I am not entirely sure if those have been resolved since.
I have been using Rivery to create and maintain data pipelines in configuration together with Snowflake, and it was very convenient.
I used Rivery for creating a data application that performed system dynamic simulation, and this application was deployed in Snowflake. The application used Python programming language and SQL primarily. The task involved loading data from source to target, confronting the ETL process, and I used Rivery to build the data pipeline.
The process was highly automated on Rivery's side, so I created pipelines to clean up the data as well. The key aspect here is automation.
In my opinion, the most useful features Rivery offers are automated pipelines, data quality checks, and transformations. I would highlight automation again, as everything is built out of the box, and you don't need to spend a lot of time creating underlying code. You focus on logic and what exactly your application needs to do, and that is the essence.
The main benefit Rivery brought to my organization was the time we were able to save on development, and by using all these automation features provided by Rivery together with integration to Snowflake, we were able to solve problems. This was very useful and had the highest impact for our organization because we did not have dedicated data engineers or DevOps at that time.
As an end user of Rivery, I would like to see it be less commercial for users. To me, Rivery does not seem expensive, but it still feels commercial, and the problem is that there are many alternatives that can be built nowadays using Gen AI. For example, agentic AI with open source tools can be used to build all configurations automatically for pipelines.
I think Rivery should be more integrated with modern AI tools such as agentic AI tools and specific protocols for MCPs. This would allow me to use Rivery out of the box from Claude and attach it as MCP for direct deployment.
I have been using Rivery for around six months.
I have not experienced issues with downtime or reliability, as Rivery was working smoothly.
I have not tried scaling up my workloads or projects with Rivery at all.
I have interacted with Rivery's support team, and it was very helpful. One of their specialists even contacted me on LinkedIn, so I really enjoyed this experience.
Negative
I have used a lot of different solutions for data integration. I am quite experienced in data operations, and I mostly used open source tools such as Hive and HDFS to build ETL pipelines, and for transformations, I used Scala applications.
By using Snowflake and Rivery, I was able to set up and complete project goals myself without the necessity to employ additional data engineers or DevOps, so I saved funds on paying for an additional position.
I do not remember how the pricing process was organized regarding the purchases, but I think we signed up for the smallest feasible plan at that time, which was about ten months or a year ago.
One more feature I appreciated was the use case repository of existing use cases, transformations, and examples of how data pipelines are built for social media marketing campaigns. I found them very useful, and I also changed and adapted some of these cases for the project I was working on at that time.
When I chose Rivery, the main criteria was integration with Snowflake because it was the main solution using Snowflake technology. It was mandatory to find something applicable, something simple, and something that could quickly solve all necessary requirements.
My advice would be to look for a specific use case which Rivery can help to solve quickly and consider opportunities to replace specific roles such as DevOps, DataOps, or data engineers by using Rivery. I would rate this review a 7 overall.
My main use case for Rivery involves collecting data from different sources, and with Rivery, I am able to put them together and load the data directly in Snowflake.
The best features Rivery offers for me include the possibility to connect to different data sources, which is really interesting. Sometimes with other tools, there is a challenge in having connectors for specific sources, so this feature really makes my work easier.
Regarding my experience with the connectors, I did not have a lot of problems, and it was nice that I could also directly connect to sources that were NoSQL databases.
Rivery has positively impacted my organization because my colleagues who used it say that it really helps to do a part of the work that with other tools could be more challenging.
I think Rivery could be improved by having more analytical features inside. I do not know if in the latest updates there are some AI tools to use or something related to that.
Sometimes I wish for a small interface that can give an example of the data you are loading on the platform, which is an improvement needed for Rivery that I have not mentioned yet.
I have been using Rivery for a short time, mainly to do some exercises and little projects connecting Rivery to Snowflake.
The process of using Rivery has worked very smoothly for me, and I found the tool very easy to use, allowing me to gain a lot of insights. The tool is really useful and fast and easy to use.
The kind of work that became easier due to Rivery is the same as I already mentioned; the focus is on the ability to connect to different sources and to put all the data together.
Rivery is a really promising tool that could be developed in the future, as it could have a lot of utility for different companies.
My advice to others looking into using Rivery is that it is easy to use, so you do not need a lot of training to use it. I would recommend diving into the different features and looking for what is best. The documentation is easy to find.
I found this interview very smooth, and I think for my experience with Rivery, it is good enough.
I would rate Rivery overall as a nine out of ten.
I have been using Rivery for two years.
My main use case for Rivery is data ingestion and transformation workflows with Snowflake.
A quick, specific example of how I use Rivery with Snowflake in my workflows is managing pipelines, monitoring data loads, and the performance for larger data sets.
The best features Rivery offers are the smooth integration with Snowflake, which does not require much manual setup, and it helped me get up and running quickly. It is easy to manage.
What stands out most to me about the integration with Snowflake and the ease of management is that it is easy to set up and it has a great user interface.
Rivery has positively impacted my organization by helping me streamline my data ingestion and transformation processes, especially in combination with Snowflake. Since I started using it, I have seen more reliable pipelines, faster time to data availability, and less manual effort needed to maintain integrations. It also improved visibility into data flows, which made troubleshooting and day-to-day operations easier for the whole team.
There is still room for advanced customizations and debugging. As an end-to-end solution for ETL with Snowflake, Rivery has proven to be reliable and efficient in my day-to-day work.
I have been working as a software tester for five years.
In my experience, Rivery is stable.
Rivery has scaled well for my needs so far. It has handled growing data volumes and additional pipelines without major issues, and we have not had to make significant changes to our setup as usage increased. Performance has remained stable, which made scaling relatively smooth.
I have not had to reach out to customer support, so I do not have any experience.
I did not previously use a different solution before Rivery.
I do not know if we purchased Rivery through AWS Marketplace as it was not my job to set it up.
I have seen a return on investment from using Rivery. It saved my team time and really reduced manual work, so overall, it improved efficiency.
I do not know about the experience with pricing, setup cost, and licensing as it was not part of my job.
I do not know if we evaluated other options before choosing Rivery because it was not part of my job.
I do not have anything more to add about the features, automation, scheduling, or anything else that comes to mind.
I am not prepared with statistics for now regarding specific outcomes or metrics, such as time saved, reduction in errors, or other improvements I have seen since using Rivery.
I cannot tell if my company has a business relationship with this vendor other than being a customer.
I gave this review a rating of 8.
My main use case for Rivery is to target and source from various data sources.
A specific example of how I use Rivery is that the advantage lies in the ability to approach every data project easily because it has so many connectors. I have used them for taking data from Facebook, Google, YouTube, Instagram, and other platforms.
In addition to my main use case, I also take data from Monday and different databases. I knew that it would not be a problem when I needed to take data from different sources.
The best features Rivery offers include building blocks that make everything much easier and clearer. They have great support, are very fast, and respond quickly, ensuring they do not stop correspondence with you until you get a solution. If needed, I meet with the Israeli team for a quick focused Zoom session, and I knew I would be fine once I chose Rivery.
The building blocks work for me because there is great documentation for every time I want to connect to a source, and most of the time I can handle it myself without any problem. If I get stuck because of a different aspect of security or a setting I did not know about before, I just reach out via the support email, and they answer me and can look at my account to see what I did wrong. Everything is very transparent and therefore easy.
Rivery has positively impacted my organization by enabling us to create a stable pipeline, and we can expand the variety of the data sources as we go very easily, with no worries, and with a lot of confidence in the fact that it can be done. It also has great monitoring of the cost.
In terms of specific outcomes or metrics, before Rivery, connecting to so many APIs would have required us to allocate a developer, which costs a lot of money compared to a data person. It also required a lot of context switching for something that is really easy to productize, which is what Rivery did. Rivery saved time and money because everything was handled in one place by only one or two data people instead of using the resources of a development team, which is great, and all the knowledge is handled in one team.
How Rivery can be improved is a challenging question, but I think something with the graphics could be better because when there is a lot of code inside, it needs to be more user-friendly. I am not talking about the source-to-target, but when we implement a lot of code, you need a different visual of how you use it, such as what you see in DBT.
There is a problem where sometimes I run a river and something in the UI does not get refreshed because I think of browser cache limits, which is annoying. I always need to refresh, and then I am not sure, so I have to go inside the river and look in the log to see if it has finished or not, which takes my time. At the end of the day, when I am running the river, I need it to be done before I go to the next step. Something in the UI could work more smoothly to show it easily. I am not sure why it happens, and I am not sure if there is a solution for it, but if it could be more clear, that would be very nice, such as sending an email once the river is really done.
I have been using Rivery for a total of three years, maybe even a little bit more.
Rivery is very stable in my experience.
Rivery's scalability is great, and I did not have any issues.
The customer support for Rivery is excellent.
I would rate the customer support a nine on a scale from one to ten.
Positive
I did not really use a different solution before Rivery. I worked in another company before this role, and the job was done by a backend developer who coded the ETL solution.
My experience with pricing, setup cost, and licensing was unfortunately not so good in my recent data project. Before that, it was fine. When I had to connect to databases, the cost was very fair and reasonable, and we could use it in a way that we could scale with it.
However, once I had to integrate source-to-target with Monday, for example, I was very surprised by the cost, which was very high. I found myself asking my stakeholder to make it only five times a day because it was really expensive. He was expecting real-time data, so the gap was too big and hard to negotiate. At the end of the day, we decided to do it every hour, and we knew it would be very expensive, so that was a hard decision we had to make.
My advice for others looking into using Rivery is to read the documentation once you start. If you have any questions or need a person to help you at first, you can always reach out to customer success or support. They will guide you and give you the proper how-tos and support.
It can be a bit stressful to start with this kind of system because you might be afraid of billing too much when you run the river. Once you get started, you know how to manage it. You know how to do tests of streaming new data without charging too much money. It is really easy to use, and once you do it one time, the second time is really easy.
There may be a barrier the first time for those who do not know the concept, but if you have been doing ETL as a SaaS, I believe the transition, whether to save money or because they have more features you like, will not be a problem, even for a junior data person, because it is very easy. I would rate this product an eight overall.
