

IBM Cloud Pak for Data and Fivetran are prominent in the data management and integration sector. IBM Cloud Pak for Data seems to hold an advantage with its comprehensive suite of features addressing a broader range of functionalities, unlike Fivetran, which focuses on simplifying the data integration process.
Features: IBM Cloud Pak for Data provides data visualization, centralized analytics, and robust AI/ML capabilities. It effectively supports ETL processes, data governance, and hybrid and multi-cloud compatibility. Fivetran offers simplicity, scalability, and efficient data replication with its seamless connectors and real-time syncing.
Room for Improvement: IBM Cloud Pak for Data could enhance integration with cloud service providers like Cloudera, refine performance to avoid cloud issues, and simplify installation. Fivetran needs more connector availability, real-time data processing improvements, and better customization options.
Ease of Deployment and Customer Service: IBM Cloud Pak for Data supports deployment across hybrid, public, and on-premises environments but faces complexity and support response time issues. Fivetran is recognized for ease of setup in public and hybrid cloud environments, though its customization options are restricted. Both receive mixed reviews on customer support.
Pricing and ROI: IBM Cloud Pak for Data can be costly for small enterprises but offers substantial ROI through data quality improvement. Fivetran is considered pricey with its database-driven pricing model, yet users benefit from significant ROI in time savings and process optimizations. IBM favors larger organizations, while Fivetran is suited for straightforward data integration needs.
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.
It has given my teams an edge in data management through automation while adhering to compliance regulations.
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.
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.
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.
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.
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.
IBM Cloud Pak for Data is stable.
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 | 1.1% |
| Other | 97.1% |


| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 7 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Large Enterprise | 20 |
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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