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.

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.

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.

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.

My company has started to use the Rivery extract data from Hive. It is like a project management sort of program, and we started to use Rivery to get the data from there over into Mavenlink, so we were just trying to find data solutions for what we did with the tool for a little while. The tool was a sort of ETL extracting data product, and getting it over to the other programs and using it as a sort of a data cleanup and flow sort of thing was needed.

The solution's most valuable features are that it is quick to connect and simple to use.

I don't know what could be improved in terms of what my company was used to previously or after moving over to Rivery. I have not had much experience with platforms other than Rivery. For me, Rivalry was a way to step up from what we used. To be honest, I am not really sure what improvements could be made in Rivery.

Pricing is a little steep for smaller organizations, I would say. The product's pricing model could be a little bit better. I am not aware if there are additional packages for smaller organizations, but if there are no packages available, then maybe that would be a good way to introduce something new in the tool.

I have been using Rivery for a year. I am a customer of the tool.

So far, the tool's stability has been good.

It is a scalable solution. I think the tool can easily connect you to more solutions. We don't have that many tools, but I think an organization that did could very easily get all of their data hooked up within Rivery and have it flow out to whether that looked like it's flowing into Snowflake, so it just gets into a database, or whether that looked like it's flowing directly into Tableau. There is also connectivity available with Rivery.

The company is pretty large, but most of it is customer-facing. We have three people on our internal teams. There are a couple of other teams with ten to fifteen people using the platform.

I haven't personally used the solution's technical support, but I know that my supervisor had contacted them, and according to him, it seems to be pretty good.

The product's initial setup phase is fairly simple. Once you actually get the software, you just sit down to read a couple of the articles available on the tool's website.

The solution was deployed with the help of a three-person team, including a senior operations and data analyst, a senior operations analyst, and a supervisor, who I think is considered an operations manager. He is just a supervisor above us who runs the operations department. He used to run the resource management department, but now he only has business operations that fall under him.

The solution can be deployed in a few days.

The tool requires some maintenance to ensure that we double-check and do our due diligence to check connections, which is just a typical part of our process. In terms of other software programs that the organization uses, there is just another team, and they are mostly some of our in-house software programs, whereas I am a part of the team and pretty much responsible for data transfer.

The tool's use has had a large savings on time. I would say that in terms of cost-benefit, we have used just an in-house solution before. I would say for other organizations, using a program that may be something different before can save costs using Rivery. It's definitely very beneficial in terms of overall productivity. We're able to get quite a bit more done. Within the same time frame as usual. With Rivery, I can save 15 to 20 percent time.

The tool's price can be a little steep for a small organization. I rate the tool's price as six out of ten if I consider the lowest price to be one and the highest price to be ten.

Speaking about how the automation capabilities of the product have improved the data processing tasks, I would say that there was a lot of manual work that we had to do in our company. Originally, beforehand, we were downloading files, and then we had a sort of an in-house solution to which the files that we downloaded were uploaded. The in-house solution was integrating the data, but with the automated features, it kind of eliminated all of that, at least most of that process, and we would still go in and try to make sure that the data that came over was accurate. My company was involved in double-checking since we wanted to ensure that the data flowed inaccurately. In terms of the actual automation, it did save a lot of time compared to having a manual process in place.

I would recommend the tool to others because our experience with it has been such that it came in when we were hoping to dramatically improve our process flows. Rather than going in and having to do a lot of manual work, we now can save that time and go in and maybe do periodic reviews to ensure everything is still connected well and syncing well and ensuring the data is the same as in the source platform. I think that an organization would want to connect even more software platforms to Rivery as it would certainly save some time.

The tool handles data security and compliance because now, with just one source, we are carrying data over through that one pipeline. It definitely allows us to have more control over who sees that data and who actually has access to it.

In our company, we don't really do a whole lot of AI-driven projects internally. The external side is, believe it or not, where a lot of those AI projects are happening in my company, but I can't really speak about it.

I rate the tool an eight out of ten.