

IBM Cloud Pak for Data and Rivery compete in the data management solutions category. Rivery seems to have the upper hand due to its agility and efficient deployment, appealing to those prioritizing flexibility.
Features: IBM Cloud Pak for Data offers comprehensive integration with AI and machine learning tools, focusing on data governance and security. Rivery provides a no-code interface and strong data pipeline automation, enhancing adaptability through operation simplicity.
Ease of Deployment and Customer Service: IBM Cloud Pak for Data generally requires a more complex setup suitable for detailed customization, backed by a sophisticated customer support framework. Rivery stands out with quick deployment and an intuitive setup, offering proactive customer service.
Pricing and ROI: IBM Cloud Pak for Data may involve higher initial setup costs but targets long-term ROI with deep integration capabilities. Rivery features a simpler pricing model, more affordable initial costs, and offers a higher ROI over shorter periods, making it appealing for budget-conscious businesses.
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
It is easy to collect, organize, and analyze data no matter where it is, hence being able to make data-driven decisions.
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.
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
The customer support for IBM Cloud Pak for Data is great and responsive.
I would rate IBM's support at about a seven or eight out of ten because we have good support coverage owing to our long association with IBM.
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.
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
IBM Cloud Pak for Data's scalability is very good; it can be used by any size of organization.
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.
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.
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
IBM Cloud Pak for Data can be improved because processing speeds are sometimes slow.
To improve IBM Cloud Pak for Data, I suggest more out-of-the-box integration.
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.
The setup cost is very expensive.
Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable.
The list price is high, but the flexibility in pricing is adequate.
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.
From there, I can work my way into a more granular level, applying all of that information on top of my actual data to understand what my data looks like, where it came from, and where it went wrong, managing it throughout the cycle.
The benefits of choosing IBM Cognos, in addition to saving on cost, include having institutional knowledge about maintaining this infrastructure and enough people who have developed on Cognos in the past, which creates comfort in its use.
We have been able to save approximately 80 percent of our time. We are not doing data analysis manually, so this relieves our data department of dealing with data.
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.
| Product | Mindshare (%) |
|---|---|
| IBM Cloud Pak for Data | 1.3% |
| Rivery | 0.6% |
| Other | 98.1% |

| Company Size | Count |
|---|---|
| Small Business | 8 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.
Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.
Rivery is a serverless, SaaS DataOps platform that empowers companies of all sizes around the world to consolidate, orchestrate, and manage internal and external data sources with ease and efficiency.
By offering comprehensive data solutions and partnering with complementary technology providers, including Google, Snowflake, Tableau, and Looker, Rivery enables data-driven companies to build the perfect ecosystems for all their data processes.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.