

IBM Cloud Pak for Data and Fivetran compete in the data integration and management space. IBM Cloud Pak for Data has the advantage in terms of support and pricing satisfaction, while Fivetran stands out with its robust features that justify its cost.
Features: IBM Cloud Pak for Data integrates diverse data sources, providing a unified platform for AI-driven insights. It offers advanced analytics capabilities and strong governance features. In contrast, Fivetran is known for its seamless data pipeline automation and efficient data connectors, simplifying the ETL process. Its automation and simplicity significantly differentiate it from competitors.
Room for Improvement: IBM Cloud Pak for Data could enhance its deployment simplicity and reduce its complexity for businesses with limited IT resources. Its customer service could benefit from more streamlined processes. Fivetran might improve in providing more personalized support and enhancing its flexibility for complex data processes. The cost structure could be more adaptive to different scales of data usage.
Ease of Deployment and Customer Service: IBM Cloud Pak for Data requires a comprehensive setup and a detailed deployment strategy, which can be complex. It features a notably hands-on customer service providing in-depth assistance. Fivetran, contrastingly, offers a straightforward deployment with plug-and-play functionality, focusing on ease and efficiency. Its customer service is efficient but more tailored to automated processes rather than personalized support.
Pricing and ROI: IBM Cloud Pak for Data typically involves a more substantial initial investment but delivers strong long-term ROI through extensive capabilities and scalable infrastructure. Fivetran, with a lower upfront cost, provides attractive quick return benefits due to its efficient data handling, but its ROI is highly dependent on the volume and complexity of data processes, potentially making it a more costly solution over time.
Fivetran provides time savings, cost reductions, and improvements in data quality.
It saves us the effort of having one to two data engineers managing the tasks that Fivetran handles.
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.
If they could provide support more quickly, that would be great.
The technical support provided by Fivetran has generally been good, with a response time and competence that I would rate as good.
Customer support from Fivetran is quite good; it's really nice and responsive.
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.
The response time for IBM's technical support is excellent.
Fivetran's scalability has been tested effectively, and it has been working well for our organization's growing data needs.
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.
They have 99.9% accuracy on the data load and they maintain transparency.
In my experience, Fivetran is stable with very few instances of downtime or reliability issues.
During the duration of the time that we used Fivetran, it was highly stable.
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good.
From a cost perspective, if the number of connectors is lesser, then Fivetran is not the most cost-efficient option.
I want more flexibility during ingestion, specifically for transformations needed beforehand.
Fivetran could improve by adapting more for technical users and by providing more options for such users.
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.
Our current yearly contract for Fivetran is approximately $70,000.
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.
The most valuable feature of Fivetran is its built-in connectors for a wide range of data sources.
The real-time data replication is what I see best in the market where it reduces the overhead of customers needing to maintain the pipeline.
The ability to seamlessly integrate with a large variety of data sources is valuable.
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.
| Product | Mindshare (%) |
|---|---|
| Fivetran | 1.8% |
| IBM Cloud Pak for Data | 1.2% |
| Other | 97.0% |


| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 7 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Large Enterprise | 15 |
Fivetran offers a seamless, scalable data integration platform with strong connectors and real-time synchronization. Tailored for managing ETL workflows and integrating with DBT, it appeals to organizations seeking efficient data management.
Fivetran distinguishes itself through its intuitive interface and extensive scalability, allowing businesses to manage entire ETL workflows seamlessly. Its robust connectors ensure smooth integration with multiple data sources, while transparent logging and minimal coding requirements enhance accessibility. With real-time data synchronization, organizations benefit from up-to-date insights for analytics and engineering purposes. While some users point out areas for improvement like better documentation and expanded integration options, Fivetran remains a cherished tool for centralizing data in data warehouses such as supporting change data capture, migrations, and synchronizations from systems like Salesforce, NetSuite, and Google Analytics. Operating within an ELT framework, it empowers businesses to streamline data processes without complex extraction logic.
What are the key features of Fivetran?In industry-specific implementation, Fivetran is integral for businesses requiring robust data integration to power analytics. Retailers utilize it to consolidate e-commerce data for sales insights, while finance firms rely on its capabilities to merge financial data for reporting. In the tech sector, it supports engineering teams by providing a reliable data pipeline that fuels app development and performance monitoring.
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.
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