

IBM Cloud Pak for Data and Rivery are prominent players in the data analytics category. IBM Cloud Pak appears to have the upper hand in enterprise settings due to its comprehensive suite of integrated tools, making it more appealing for large enterprises.
Features: IBM Cloud Pak for Data stands out with advanced integration features like IBM MQ and App Connect, comprehensive ETL capabilities through DataStage, and strong support for hybrid and multi-cloud environments. It excels in AI-driven analytics, facilitating robust data visualization and management. Rivery offers ease of use with automated pipeline creation, integration with Snowflake, and flexibility via Python custom connectors, making it highly suitable for teams with limited technical skills.
Room for Improvement: IBM Cloud Pak for Data needs enhanced cloud service integration, performance improvements, and simpler installation processes. Feedback also suggests a need for improved AI lifecycle management and data governance tools. Rivery could improve logging capabilities, expand analytical features, and offer more transparent interface handling for large code bases. Pricing remains a concern for both platforms, with customers seeking less commercial options.
Ease of Deployment and Customer Service: IBM Cloud Pak for Data supports hybrid, public, and on-premises deployments, suitable for diverse IT environments, though its complexity can hinder quick support resolutions, with ratings generally between seven to nine. Rivery focuses on public cloud deployment and provides good initial support but suffers occasional response delays. Both platforms offer comprehensive customer service, with IBM's extensive offerings better suiting enterprises with more demanding support needs.
Pricing and ROI: IBM Cloud Pak for Data is considered expensive, particularly for smaller businesses, though its capabilities justify the cost in comparison to top-tier competitors. It delivers significant ROI as reported by users, thanks to time savings and enhanced data management. Rivery, while competitively priced, may be steep for small businesses; however, it manages operating costs effectively, offering ROI through streamlined data processes. Both solutions target larger enterprises, with IBM's more flexible pricing attracting substantial enterprise purchases, whereas Rivery's scaleable and cost-sensitive solution appeals to those needing flexible budget options.
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 has given my teams an edge in data management through automation while adhering to compliance regulations.
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.
I rate the technical support from IBM a nine out of ten because the support has been very top-notch, unparalleled, and also very professional.
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.
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.
For scalability, I rate it a nine out of ten because it is a very scalable solution that has been able to handle my organization's growth efficiently.
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.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
IBM Cloud Pak for Data is stable.
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 | 1.1% |
| Rivery | 0.7% |
| Other | 98.2% |

| Company Size | Count |
|---|---|
| Small Business | 10 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 3 |
IBM Cloud Pak for Data is a comprehensive platform integrating data management, AI, and machine learning capabilities tailored for hybrid environments. It's renowned for enhancing productivity through efficient data analytics and management.
This platform offers data virtualization, robust analytics, and AI-driven processes. Its integration capabilities, including IBM MQ and App Connect, facilitate seamless data connections. Users benefit from containerization, data governance, and compatibility with hybrid systems, improving decision-making and management productivity. However, the requirement of extensive infrastructure and performance challenges can impact scalability for small businesses.
What are the key features of IBM Cloud Pak for Data?In the financial and banking sectors, IBM Cloud Pak for Data is utilized for data management tasks like spend analytics and contract leakage analysis. It's used for data integration, machine learning, and AI-driven analytics to transform data into valuable insights in industries such as FinTech and consultancy.
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.
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