

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
It saved my team time and really reduced manual work, so overall, it improved efficiency.
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.
The incidents have disappeared completely since I have been using Striim.
Striim's continuous pipeline safely processes millions of daily transactions without data loss; my organization had millions of transactions every day and even in real-time, so it was quite good.
Since it is now one to two hours, I would say it has saved employee hours and time.
One significant challenge was implementing custom-built Python scripts using Rivery for transformations.
Customer support is great; they are answering really fast.
The customer support for Rivery is excellent.
They are all knowledgeable about what they do.
When you contact them, they give you a response straight away and help you identify the issue and fix it.
The customer support has been excellent, and their engineers actually understand z/OS architecture and DB2 logs.
It has handled growing data volumes and additional pipelines without major issues.
The focus is on the ability to connect to different sources and to put all the data together.
Our licensing was based on the number of cores, so even if we have a high number of events on any given day, our license cost would not go high.
I would describe the scalability of Striim as very good, as it adapts well.
Striim can handle the data volumes effectively, but it can struggle a little bit if the data volume is too high.
I found the tool very easy to use, allowing me to gain a lot of insights.
The excellent support we received from Rivery team contributes to this perception.
That problem has been completely resolved with Striim.
Striim was very stable.
In my experience, Striim is mostly stable.
As an end-to-end solution for ETL with Snowflake, Rivery has proven to be reliable and efficient in my day-to-day work.
Agentic AI with open source tools can be used to build all configurations automatically for pipelines.
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.
I believe if Striim could use some part of AWS secrets or its own secret mechanism to store the password, that would really save a lot of time so that I don't have to keep updating the password whenever there is any change.
They could improve the documentation by showing how to configure with different platforms.
I think Striim could be improved with better pricing and enhanced documentation.
I found myself asking my stakeholder to make it only five times a day because it was really expensive.
I found the pricing and licensing to be fair and competitive compared to other solutions I have seen.
Licensing was a bit more expensive because Striim has to read from Oracle GoldenGate trail files and also integrate them.
My experience with the pricing, implementation cost, and licensing of Striim is that it is somewhat expensive.
It's very fair.
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.
The main benefit Rivery brought to my organization was the time we were able to save on development.
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.
Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency.
There were significant improvements because once we enabled change data capture, the database was not going down at all.
It reduces manual intervention because it automatically syncs the data from the warehouse.
| Product | Mindshare (%) |
|---|---|
| Rivery | 0.7% |
| Striim | 0.6% |
| Other | 98.7% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
Rivery enhances automation with its built-in pipelines, seamless Snowflake integration, and flexible data management capabilities. It supports extensive connectivity and user-defined functions, aiding efficient data flow management.
Rivery provides a robust platform for automating data ingestion and transformation workflows, integrating effortlessly into data warehouses like Snowflake. Its user-friendly interface and extensive API connectivity simplify data extraction and flow, accommodating diverse needs with custom scripting and user-defined functions. Despite its strengths, improvements are desired in lineage, impact analysis, and advanced visualization, along with better orchestration and logging capabilities. Users also seek price adjustments for smaller organizations and integration with modern AI technologies to elevate analytical capabilities.
What features does Rivery offer?In industries such as retail and finance, Rivery is crucial for managing ETL processes. Retail organizations use it for integrating data from sales channels and customer databases, driving targeted marketing strategies. Finance companies rely on its robust pipelines and Snowflake integration to streamline complex financial data transformations and enhance reporting accuracy.
Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.
Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim facilitates effective data-driven strategies.
What are some key features of Striim?In industries like finance, Striim supports real-time fraud detection by providing uninterrupted data streaming between transaction systems. In healthcare, it enables rapid data processing for patient monitoring, improving service delivery. Manufacturing uses Striim to enhance supply chain visibility through real-time data analytics.
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