# IBM watsonx.data Reviews **Vendor:** IBM **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.4/5.0 **Total Reviews:** 164 ## About IBM watsonx.data IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial ## IBM watsonx.data Pros & Cons **What users like:** - Users praise the **ease of use** of IBM watsonx.data, appreciating its unified platform and swift data access. (67 reviews) - Users appreciate the **speed and ease of use** of IBM watsonx.data, enhancing their data management and integration capabilities. (47 reviews) - Users value the **flexibility and AI readiness** of IBM watsonx.data, enhancing data management across hybrid environments. (41 reviews) - Users appreciate the **easy integration with other IBM tools** , enhancing workflow efficiency and simplifying data management. (33 reviews) - Users appreciate the **unified lakehouse feature** of IBM watsonx.data, simplifying analytics by consolidating all data in one platform. (31 reviews) - Users value the **flexibility** of IBM watsonx.data, enabling efficient management and analysis of diverse datasets seamlessly. (31 reviews) - Efficiency (30 reviews) - Easy Integrations (27 reviews) - Large Datasets (27 reviews) - Performance (26 reviews) **What users dislike:** - Users face a **steep learning curve** with watsonx.data, making setup and feature mastery challenging for newcomers. (38 reviews) - Users find the **complexity** of IBM watsonx.data to be a barrier, particularly for beginners needing intuitive guidance. (25 reviews) - Users find the **pricing to be high** , making it challenging for small teams and startups to commit. (20 reviews) - Users find the **difficult setup** of IBM watsonx.data frustrating, hindering adoption and effective use of the platform. (17 reviews) - Users report **difficulty in understanding the documentation** and navigating the platform, especially for beginners. (17 reviews) - Users experience significant **integration issues** with IBM watsonx.data, especially when connecting to various data sources. (16 reviews) - Steep Learning Curve (16 reviews) - Setup Difficulty (14 reviews) - Users face **integration challenges** with Watsonx.data, especially when working with legacy systems and custom connectors. (13 reviews) - Poor Documentation (13 reviews) ## IBM watsonx.data Reviews ### 1. Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve **Rating:** 4.0/5.0 stars **Reviewed by:** Arkajit D. | Chief Technology Officer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 19, 2026 **What do you like best about IBM watsonx.data?** One feature that stood out for us was the query performance optimization, especially for large reporting and analytics workloads. We process high-volume financial and customer behavior data, and the platform handled complex queries much more efficiently than our previous setup. I also appreciate the interoperability with existing tools and open formats. Our engineering team didn’t have to completely rebuild pipelines or retrain users from scratch, which made adoption smoother internally. Another big advantage has been governance and data visibility. In a regulated fintech environment, having stronger control over data access and lineage tracking became extremely important, especially for audit and compliance requirements. From a business perspective, watsonx.data helped reduce infrastructure inefficiencies while improving access to analytics across teams. Analysts, data engineers, and operations teams were able to work from a more unified environment instead of constantly moving data between disconnected systems. **What do you dislike about IBM watsonx.data?** One challenge with IBM watsonx.data is that the platform can feel quite complex during the initial onboarding phase, especially for teams that are newer to lakehouse architectures or hybrid data environments. There are a lot of capabilities available, but understanding how to configure and optimize everything properly takes time. We also experienced a steeper learning curve around setup, integration, and governance policies compared to some lighter-weight analytics platforms we evaluated. Certain workflows required more technical involvement from our data engineering team than we originally expected. Another area that could improve is the user experience within parts of the interface. While the platform is powerful, some administrative and configuration tasks don’t always feel as intuitive or streamlined as newer cloud-native tools in the market. Performance has generally been strong for large workloads, but during early implementation we had to spend time tuning queries and optimizing storage configurations to get consistent results across different environments. Pricing and infrastructure planning can also become a consideration for organizations scaling large enterprise deployments. Smaller teams without dedicated data engineering resources may find adoption more challenging initially. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data helped us solve a major issue around fragmented data management and slow analytics processing across multiple business systems. Before implementation, our teams were pulling data from separate cloud platforms, transactional databases, and reporting tools, which created delays, duplication, and inconsistent reporting. One of the biggest problems was handling growing volumes of financial and operational data efficiently without constantly increasing infrastructure costs. Traditional warehouse scaling was becoming expensive, especially as our analytics workloads expanded across departments. With watsonx.data, we were able to centralize access to structured and semi-structured data while still keeping flexibility in how the data was stored and queried. That significantly improved reporting speed and reduced the amount of manual data movement our engineering team had to manage. A major benefit for us has been faster analytics and better visibility across teams. Earlier, generating large operational or customer-risk reports could take hours because data pipelines were fragmented. After implementation, analysts were able to query datasets more efficiently and collaborate from a more unified environment. ### 2. Unified Data Management with Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Anchal P. | Process Executive, Small-Business (50 or fewer emp.) **Reviewed Date:** May 15, 2026 **What do you like best about IBM watsonx.data?** What I like most about IBM watsonx.data is its ability to unify data from multiple sources without complex migrations or duplication, which saves time and reduces storage costs. Its open lakehouse architecture delivers strong performance for analytics, reporting, and AI workloads while remaining cost-efficient and scalable. I also appreciate the clean and organized UI/UX, which makes navigating datasets, managing workloads, and monitoring data operations more efficient for enterprise teams. The built-in governance, hybrid cloud flexibility, and smooth integrations further simplify data management and support scalable AI and analytics initiatives across environments. **What do you dislike about IBM watsonx.data?** One area IBM watsonx.data could improve is the initial setup and configuration, which can feel complex for new users or smaller teams. Some integrations and advanced features also come with a learning curve and would benefit from clearer, more detailed documentation. In certain situations, query performance and troubleshooting can take extra effort, especially when working with very large or highly diverse data environments. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to manage and analyze large data sets across hybrid cloud environments. It streamlines integration, boosts query performance, and provides trusted data access for AI. It simplifies complexity, enhances team collaboration, and controls costs across multiple sources. ### 3. Efficient and Scalable Lakehouse Platform for Modern Data Analytics **Rating:** 4.0/5.0 stars **Reviewed by:** Yash P. | Assistant System Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** April 23, 2026 **What do you like best about IBM watsonx.data?** What I like most about IBM watsonx.data is how it lets us query and manage data across multiple sources without needing complex data movement. Its open lakehouse architecture makes it easier to work with structured and unstructured data side by side, which has improved performance and reduced storage duplication for our analytics workloads. The integration with AI and analytics tools also helps teams process large datasets more quickly and generate insights more efficiently. Another major advantage is its scalability and governance. The platform reliably supports high-volume enterprise data workloads while also providing strong security controls and solid data governance features. **What do you dislike about IBM watsonx.data?** One area where IBM watsonx.data could improve is the initial setup experience and the learning curve for new users. While the platform is powerful, configuring integrations and optimizing workloads can sometimes require advanced technical knowledge, especially for teams that are new to lakehouse architectures. Clearer onboarding documentation, along with more guided setup workflows, would make adoption smoother and reduce the effort needed to get started. I also think some UI workflows and monitoring features could be more intuitive. At times, troubleshooting performance issues or managing integrations across different environments takes extra effort than it should. Additionally, pricing and resource consumption can become expensive for large-scale deployments, so more transparent cost-optimization tools and simpler management features would help improve the overall experience. **What problems is IBM watsonx.data solving and how is that benefiting you?** Before using IBM watsonx.data, we struggled to manage and analyze large volumes of data distributed across multiple systems and cloud environments. Moving data between platforms was time-consuming and costly, and it often introduced delays in our reporting and analytics workflows. We also found it challenging to maintain consistent governance and reliable performance while working with a mix of structured and unstructured data. With IBM watsonx.data, we can now query data across different sources more efficiently, without unnecessary duplication or migration. This has improved analytics performance, lowered storage and operational costs, and helped our teams reach insights faster to support decision-making. The platform’s scalability, along with its integration with AI and analytics tools, has also boosted productivity by simplifying big data processing and enabling quicker development of data-driven solutions. Overall, it has helped us streamline our data architecture while strengthening governance, flexibility, and operational efficiency. ### 4. Powerful, Secure, and Scalable Platform with Easy Data Migration **Rating:** 5.0/5.0 stars **Reviewed by:** Konjengbam M. | BDR, Financial Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 15, 2026 **What do you like best about IBM watsonx.data?** The best I love about this platform is the data security it provides by not relying on a single platform for storage. This is an extremely powerful platform with much scalable option. One more thing I love about this platform is the ability of this platform to migrate the data without much complexity when needs arises. I also love the way how the data is stored in this platform. The access control is also provided which further enhances the security of this platform. There is also infrastructure manager in this platform which enhances visibility of the infrastructure components. It provides better understanding and effectiveness. The capability of its AI assistant in this platform is also good and can ease the task with its assistance. One best part of this platform is the IBM Ecosystem of this platform that makes this platform more robust. **What do you dislike about IBM watsonx.data?** I love most part of this platform but I feel that the complexity of this platform is high so training from someone who had already used this platform would make the use of this platform more efficient. I also wish that this platform updates a bit more faster. **What problems is IBM watsonx.data solving and how is that benefiting you?** This platform solves data management issues by avoiding most hurdles faced before. It also enables teams to collaboratively work together on the platform which improves efficiency and productivity. ### 5. IBM watsonx.data: Flexible Lakehouse SQL on Object Storage with Iceberg Support **Rating:** 4.5/5.0 stars **Reviewed by:** Swamy G. | Founder, Mid-Market (51-1000 emp.) **Reviewed Date:** February 18, 2026 **What do you like best about IBM watsonx.data?** I used IBM watsonx.data in several client projects over the past few months, mainly for data-heavy tasks where we needed a lakehouse-style setup. What I liked most is that it allowed us to keep data in object storage while still querying it with SQL, without needing to move everything into a traditional warehouse. This cut down on a lot of unnecessary data duplication. The support for open formats like Iceberg was truly helpful. In one project, we had schema changes halfway through. Being able to manage versioning without disrupting existing queries saved us time. **What do you dislike about IBM watsonx.data?** The initial setup took us some time, especially when it came to configuring storage and access controls. It’s not exactly plug-and-play, so there is a learning curve for teams new to lakehouse architectures. We also needed to review the documentation closely to understand some configuration steps. Once it was set up, it worked well. However, onboarding could definitely be smoother. **What problems is IBM watsonx.data solving and how is that benefiting you?** In some of our projects, we faced scattered data across various storage systems. This made analytics and reporting slower and more difficult to manage. With watsonx.data, we centralized data in object storage and could query it directly without having to move it into separate warehouse systems. This reduced data duplication and simplified our pipeline design. It also allowed our team to run analytical queries faster and prepare datasets for ML workflows more efficiently. Overall, it improved collaboration between data engineers and analysts, as everyone could work on the same governed data layer. ### 6. Scalable Platform with Robust Analytics, Needs Setup Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Rahul S. | Assistant System Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 23, 2026 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data to centralize and manage both structured and unstructured data in a unified lakehouse for analytics and AI workloads. I like its ability to combine the flexibility of a data lake with the performance of a data warehouse in a single platform. It helps me access, process, and analyze data across hybrid environments to generate faster insights and support data-driven decisions. It also offers strong query optimization and supports open data formats, making it easy to scale analytics across hybrid environments. Additionally, it integrates well with BI tools for visualization, helping turn processed data into actionable insights. Transitioning to IBM watsonx.data helped me gain more flexibility and scalability, handle growing data volumes more efficiently while reducing costs, and support modern analytics and AI workloads. **What do you dislike about IBM watsonx.data?** The setup and initial configuration can be a bit complex, especially for teams new to lakehouse architectures. Additionally, improving documentation, UI intuitiveness, and integration with some third-party tools would make the overall experience smoother. The initial setup was moderately complex and required some familiarity with data architecture and cloud environments. While the documentation helps, the process can be time-consuming, especially when configuring integrations and optimizing performance for specific workloads. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to centralize data in a unified lakehouse for analytics, solving the challenge of managing large data volumes by unifying lakes and warehouses. It improves query performance and reduces costs with efficient data access and workload optimization. ### 7. Scalable Data Management with IBM watsonx.data **Rating:** 5.0/5.0 stars **Reviewed by:** Preeti Y. | Senior Analyst- HR Operations , Consulting, Enterprise (> 1000 emp.) **Reviewed Date:** April 22, 2026 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data as a unified data platform to manage, access, and analyze large volumes of structured and unstructured data. I like its ability to unify data across multiple sources without requiring heavy data movement, which makes it easier to access and analyze data efficiently while maintaining performance. I also appreciate the scalability and flexibility it offers for handling large and diverse datasets. The platform supports both analytics and AI workloads in a structured way. Its data governance capabilities help ensure data reliability and security, enabling more efficient and data-driven decision-making. The initial setup was relatively smooth with proper planning and guidance, providing a structured setup process that made it easier to configure core components and connect data sources. **What do you dislike about IBM watsonx.data?** IBM watsonx.data is a strong and scalable platform overall. Some advanced features may require initial familiarity to fully utilize, so a bit of onboarding or guidance can be helpful. Additionally, having more simplified out-of-the-box configurations for certain use cases could further enhance ease of use. Overall, these are minor areas, and the platform continues to evolve with improvements that enhance usability and performance. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to unify and manage large volumes of data across systems without needing to move it, reducing silos and improving efficiency. It supports data-driven decision-making and analytics, enabling AI applications with scalable, reliable data. ### 8. Powerful Analytics, Steep Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2026 **What do you like best about IBM watsonx.data?** I like IBM watsonx.data for its ability to analyze a large scale of data and unify data from multiple sources into a single platform. It's flexible, scalable, and works well for both analytics and AI use cases. The fast delivery of queries and overall performance are impressive. It saves me time by avoiding the need to manage separate systems, making everything accessible in one place. This efficiency helps me get insights quickly. **What do you dislike about IBM watsonx.data?** The initial setup of IBM watsonx.data was not very easy. I had to go through a lot of documentation, and setting up was moderately complex. It required some time to understand the architecture and configurations, especially during integrations. The initial learning curve is high, and it feels a bit complex initially. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to manage and analyze large datasets, unify data from multiple sources, support analytics and AI insights, and improve workflow efficiency with fast query performance. ### 9. Streamlines Data Management with Robust Features **Rating:** 4.0/5.0 stars **Reviewed by:** Jay N. | Programmer Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 20, 2026 **What do you like best about IBM watsonx.data?** I like how IBM watsonx.data simplifies the process of working with distributed data, allowing me to query it in a unified way and making my workflow much smoother. I really appreciate the performance aspect, as handling large datasets feels much faster and more efficient compared to traditional data warehouse setups I've used before. The flexibility is another benefit; it works well with different data formats and integrates nicely with existing tools, so I didn't have to completely change my workflow. I find the query engine based on Presto/Trino very helpful because I can run SQL directly on data sitting in different sources without moving it first. The data virtualization capability is quite useful for creating a unified view across multiple datasets, and the open table format support, like Iceberg, is a big plus for managing large datasets reliably. The governance features also stand out, as they make managing access controls and ensuring proper data usage straightforward. Overall, these features reduce a lot of manual effort and let me focus more on building useful data models and insights rather than handling infrastructure. **What do you dislike about IBM watsonx.data?** It's solid overall, but there are a few areas that could definitely be better. One challenge is the initial learning curve. If you're new to the ecosystem, it takes some time to understand how everything fits together, especially with concepts like data virtualization and open table formats. Performance is generally good, but for very complex queries or heavily concurrent workloads, it can sometimes need extra tuning to get the best results. It’s not always “plug and play” in those scenarios. The UI and overall user experience could also be more intuitive. Some workflows feel a bit clunky, and finding certain settings or configurations isn’t always straightforward. Integration is good, but not always seamless with every external tool—sometimes you need additional setup or workarounds depending on your stack. Lastly, documentation is decent but could be more practical and example-driven. Having more real-world use cases and clearer guides would make onboarding much smoother. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to centralize scattered data for easy access and analytics, saving time on data prep and improving performance for large datasets. It simplifies governance and control, letting me focus more on analysis rather than data wrangling. ### 10. Powerful Platform with Complex Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Kshitij P. | Assistant System Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 20, 2026 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data primarily because of its ability to provide a unified access layer to data across multiple sources without the need for heavy data movement. I like the flexibility it offers with multiple query engines, which optimizes both performance and cost for different workloads. The data virtualization feature is valuable as it allows me to access data across different sources without moving it, saving time and reducing duplication. I also find the governance and metadata management features important as they provide better control, data lineage, and trust in the data used for analytics and AI. **What do you dislike about IBM watsonx.data?** Some areas of IBM watsonx.data could definitely be improved. The initial setup and configuration can be a bit complex, especially when integrating multiple data sources and engines. Also, performance tuning and troubleshooting can sometimes require deeper expertise, and the UI/UX isn’t always very intuitive, which makes it slightly harder for new users to get comfortable quickly. The main challenge during setup was the complexity of integrating multiple data sources and query engines— it often requires a lot of manual configuration, handling credentials, and understanding how different components interact. Getting everything (like storage, compute engines, and access policies) aligned correctly can take time, especially without clear step-by-step guidance. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to eliminate data silos, enabling unified data access across sources without heavy data movement. It enhances performance and governance, making it easier to prepare reliable, analytics-ready data for BI and AI use cases. ### 11. Robust Data Security with a Learning Curve **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2026 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data as a central data platform, which is great for storing, accessing, and analyzing data, especially in data engineering and AI-related tasks. I find the built-in governance and security features very helpful; they give me confidence that the data is well-managed and secure. The access control feature is particularly useful as it allows me to decide who can view or modify specific data, reducing the risk of data misuse. I also appreciate the data lineage and tracking capabilities, as they help me understand where the data is coming from and how it is being transformed—this is very useful when debugging issues or validating data for reports. Furthermore, the data quality and governance policies ensure that the data I use is reliable and consistent across different datasets, which is crucial for analytics and decision-making. **What do you dislike about IBM watsonx.data?** IBM watsonx.data is powerful, but it has a learning curve, and the initial setup can be complex. It would also benefit from better documentation, a more intuitive UI, and simpler performance tuning. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to solve data silos, cost, performance, and complexity issues, streamlining data engineering and analytics. ### 12. Effortless Data Management, Inclusive Governance **Rating:** 4.0/5.0 stars **Reviewed by:** Sourabh M. | Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** April 16, 2026 **What do you like best about IBM watsonx.data?** I like that with IBM watsonx.data, data governance is integrated, allowing me to see who accessed what and apply security rules across all data sources, which usually feels like a boring chore when separate. I enjoy how it simplifies the setup of data sources and engine configurations through conversational interactions guided by official documentation. I also appreciate using standard ANSI SQL to join data from disparate sources, making interactive analysis effective. Setting it up was very easy for me. **What do you dislike about IBM watsonx.data?** I think more 'one click' templates for common use cases, like standard RAG, would be helpful to bridge the gap for non-experts. Also, for small to medium enterprises, the prices can feel high and difficult to predict. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to curate and vectorize data for Generative AI, moving less-used data to cheaper storage. It integrates data governance seamlessly, manages data sources, and facilitates engine setup. I can use standard SQL to join disparate sources, enhancing data analysis. ### 13. IBM watsonx.data: Solving Data Silos and Accelerating AI with a Unified Lakehouse Platform” **Rating:** 5.0/5.0 stars **Reviewed by:** Sairam B. | Engineer trainee, Enterprise (> 1000 emp.) **Reviewed Date:** February 19, 2026 **What do you like best about IBM watsonx.data?** What stands out to me about IBM watsonx.data is the flexibility. You can run different query engines based on your workload, which helps optimize performance and cost. I also like that governance is built in — that’s really important for enterprises. **What do you dislike about IBM watsonx.data?** Because watsonx.data supports multiple engines and hybrid environments, sometimes tuning performance or cost requires more expertise than simpler, opinionated platforms. It’s powerful — but you do need time to get the most out of it. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data is mainly solving the problem of scattered, expensive, and untrusted enterprise data. In many organizations, data is stored in multiple silos—different clouds, on-prem databases, and data warehouses. This makes it hard to access, analyze, and use data for AI. watsonx.data brings all that data into one unified lakehouse platform so teams can access it from a single place without constantly moving or duplicating it. IBM designed it to simplify data engineering, analytics, and AI development on top of trusted data. ### 14. Efficient Data Management with Powerful Analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Sai pavan kumar D. | Intern, Small-Business (50 or fewer emp.) **Reviewed Date:** February 18, 2026 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data to handle and access large amounts of data, and it's great for fast querying and analytics. I really like that the platform helps me handle large and complex datasets and does a good job with storage optimization, which helps decrease computational costs. The efficiency of the system is impressive, particularly with the lakehouse architecture, which supports high performance use. I appreciate the platform's integration with different AI tools, which enhances its utility for me. The analytics tools are strong, helping me monitor heavy workloads. It also enables easy extraction of insights from raw data and supports training and deploying machine learning models within the lakehouse. The BI tools assist in creating dashboards for outputs across developed models and usages. **What do you dislike about IBM watsonx.data?** Most of all the whole platform and usability were good but what I feel could be improved is the platform's documentation. In the initial times, I found it hard to understand the documentation which is not fully understandable for new users. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to handle large datasets efficiently. It optimizes storage, reduces computational costs, and supports fast querying. The platform's integration with AI tools enhances insight extraction and model deployment. I switched from MongoDB Atlas for improved performance and easier data export. ### 15. Scalable Analytics Platform with Smooth AI Integration **Rating:** 4.0/5.0 stars **Reviewed by:** K S. | Engineer Trainee, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** February 17, 2026 **What do you like best about IBM watsonx.data?** I like IBM watsonx.data for its scalability, which lets me manage growing datasets without needing to redesign my systems. Its high analytics performance speeds up the process of gaining insights, and the smooth AI/ML integration makes building and running models on the same dataset much simpler. I also appreciate the support for open data formats, as it helps avoid vendor lock-in, while keeping storage and processing costs efficient. **What do you dislike about IBM watsonx.data?** Some things that could be improved in IBM watsonx.data are better documentation for advanced use cases, simpler initial setup and configuration, and more out-of-the-box integrations with third-party tools to reduce onboarding time. Improvements could be made in UI simplicity, faster onboarding tutorials, clearer cost visibility, and more real-world sample use cases to help teams adopt and use the platform more effectively. The initial setup was moderately challenging — it required careful configuration of cloud resources and permissions. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data for centralized data storage and analytics. It solves problems like handling large-scale data efficiently, reducing data silos, improving query performance, and supports AI/ML workloads with scalable and cost-efficient data access. ### 16. Hybrid Data Solution with Room for Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Bala C. | System Analyst **Reviewed Date:** February 17, 2026 **What do you like best about IBM watsonx.data?** I like IBM watsonx.data's ability to unify data across hybrid environments while controlling costs and supporting both structured and unstructured data for AI. Its open architecture and strong integration capabilities provide flexibility and prevent vendor lock-in, making it easier to turn diverse data into actionable insights. These capabilities allow us to centralize fragmented data across environments, reduce infrastructure costs, and efficiently power AI models with diverse datasets for faster and more informed decision making. **What do you dislike about IBM watsonx.data?** Some areas for improvement include simplifying initial setup and configuration, enhancing performance tuning guidance, and providing more intuitive management and monitoring tools. Improve documentation, simplify deployment, enhance performance, and strengthen governance tools. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to overcome data silos and high storage costs, unifying data from various environments. It supports AI by leveraging both structured and unstructured data, centralizing fragmented data for informed decision-making while controlling infrastructure costs. ### 17. Flexible, High-Performance Lakehouse for Modern Analytics at Scale **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** April 15, 2026 **What do you like best about IBM watsonx.data?** What I like best about IBM watsonx.data is its flexibility and strong performance for modern analytics workloads. It combines lakehouse capabilities with open formats and AI-ready architecture, which makes it useful for organizations managing large and diverse datasets. The UI is clean and well organized, so it is easier to navigate than many enterprise data platforms, and the integration options make it fit well into existing ecosystems. What has been most helpful is the way it reduces complexity when working across multiple data environments. It improves productivity by making data more accessible without creating unnecessary movement or duplication. Performance has been solid for large-scale querying, and the platform’s AI-focused design is a major plus for teams building analytics and machine learning workflows. From an ROI perspective, it can help control costs by improving efficiency and reducing manual effort. Support, documentation, and onboarding are also strong enough to make adoption smoother for enterprise teams. **What do you dislike about IBM watsonx.data?** One thing I found a bit challenging with IBM watsonx.data is the learning curve for advanced features. While the UI looks clean at first, once you start working with complex queries or configurations, it can get a little overwhelming, especially if you’re new to this kind of platform. Integrations are powerful but not always straightforward to set up, and sometimes require extra effort from the data engineering side. Performance is generally good, but in some cases, you still need to fine-tune things manually to get the best results. Pricing can also be a concern for smaller teams, as the value is more noticeable at scale. During onboarding, documentation is helpful but could be more practical with real-world step-by-step examples. On the AI side, the foundation is strong, but I feel there’s still room for improvement in terms of smarter automation and more intuitive recommendations. **What problems is IBM watsonx.data solving and how is that benefiting you?** Before using IBM watsonx.data, we struggled with managing data across different sources and systems. A lot of time was spent moving data between platforms, and querying large datasets was slow and inefficient. It also made it harder to get quick insights, especially when working with both structured and unstructured data. With watsonx.data, we’re now able to access and query data across multiple environments without heavy data movement. This has simplified our workflow a lot. The UI makes it easier to explore datasets, and integrations with existing tools mean we didn’t have to rebuild our entire setup. Performance has improved noticeably for large queries, which has reduced turnaround time for analytics. From a business perspective, this means faster decision-making and less dependency on manual data handling. On the AI side, having data in a more organized and accessible format has made it easier to prepare for analytics and machine learning use cases. It’s not fully automated yet, but it definitely reduces the effort required to get data ready. Overall, it has helped us save time, reduce complexity, and improve efficiency when working with large-scale data, which directly impacts productivity and long-term cost optimization ### 18. Complex Setup and Rising Costs at Scale Despite a Strong Lakehouse Foundation **Rating:** 2.0/5.0 stars **Reviewed by:** Sunandan G. | DevOps Engineer I, Mid-Market (51-1000 emp.) **Reviewed Date:** April 26, 2026 **What do you like best about IBM watsonx.data?** its open lakehouse architecture, which lets you query data across multiple sources without moving it. It also delivers strong performance with built-in query optimization and integrates easily with existing data tools, making analytics faster and simpler. **What do you dislike about IBM watsonx.data?** setup and configuration can feel complex, especially for smaller teams without strong data engineering support. It can also become expensive at scale, particularly when handling large workloads or advanced features. **What problems is IBM watsonx.data solving and how is that benefiting you?** solves the problem of scattered data by letting you access and query data across different storage systems without moving it into one place. This benefits you by reducing data duplication, lowering costs, and enabling faster, more efficient analytics and decision-making. ### 19. Flexible Lakehouse Platform with Good Performance and Scalability **Rating:** 4.5/5.0 stars **Reviewed by:** Atul K. | Devops Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** April 22, 2026 **What do you like best about IBM watsonx.data?** What I like most about IBM watsonx.data is how it brings together a lakehouse approach without making things overly complicated. It feels flexible enough to handle both structured and unstructured data, and the performance with query engines is quite solid, especially when working with large datasets. **What do you dislike about IBM watsonx.data?** Initial setup can feel a bit complex, especially for new users. Also, performance tuning and cost optimization sometimes require extra effort compared to more mature, plug-and-play platforms. **What problems is IBM watsonx.data solving and how is that benefiting you?** It helps consolidate data from multiple sources into one platform, reducing silos and improving data accessibility. This makes analysis faster and more reliable, which ultimately supports better decision-making and reduces overall data management costs. ### 20. Flexible Integration, Complex Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Bhavya S. | Student, Small-Business (50 or fewer emp.) **Reviewed Date:** April 22, 2026 **What do you like best about IBM watsonx.data?** I like that IBM watsonx.data allows us to access data from multiple sources and can run on cloud and hybrid environments. I also appreciate its open and flexible architecture. It helps me connect data across sources and manage it effectively. **What do you dislike about IBM watsonx.data?** The initial learning can be complex for beginners, could be made simple with instruction steps. Fix AWS S3, need more stable and plug and play connectors. The setup was not instant, it was somewhat complex. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to search and organize data. It lets me connect data across sources and manage it effectively. ### 21. A Unified, Scalable Data Lakehouse **Rating:** 4.5/5.0 stars **Reviewed by:** SWAPNIL S. | DevOps Engineer, Financial Services, Mid-Market (51-1000 emp.) **Reviewed Date:** November 25, 2025 **What do you like best about IBM watsonx.data?** IBM Watsonx.data makes it easy to manage structured and unstructured data in one place. I really like the open lakehouse architecture - it gives us flexibility to store data in different formats while still enabling fast analytics. The built in governance, metadata management and seamless integration with open-source engines like Presto and Spark have significantly improved our query performance. **What do you dislike about IBM watsonx.data?** The inititial setup required some learning, especially for configuring connectors and access policies. Also the pricing can be a bit confusing if you are not familiar with IBM's consumption model **What problems is IBM watsonx.data solving and how is that benefiting you?** It helped us centralize our data, eliminate data silos, and dramatically improve query performance. Reporting has become faster, governance is more consistent, and overall decision-making has improved. Cost optimization through workload separation has also been a major benefit. ### 22. Enterprise-Ready Data Platform with Flexible Hybrid Support and Built-In Governance **Rating:** 4.5/5.0 stars **Reviewed by:** Faizan N. | Software Developer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** February 17, 2026 **What do you like best about IBM watsonx.data?** like how IBM watsonx.data feels built for real world enterprise needs. It’s flexible enough to run across hybrid environments, supports open formats, and doesn’t lock you into one engine. What really stands out is the built in governance and AI readiness, which makes managing and using data at scale feel much more practical and streamlined **What do you dislike about IBM watsonx.data?** watsonx.data can be a little complex to get started with **What problems is IBM watsonx.data solving and how is that benefiting you?** What I like about IBM watsonx.data is that it tackles the messy reality of scattered, siloed data and makes it easier to bring everything together in one place. It also reduces the fear of vendor lock-in. For me, that means spending less time dealing with infrastructure headaches and more time actually getting useful insights from the data ### 23. Unified Lakehouse with Room for Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Ganesan C. | Senior associate consultant , Enterprise (> 1000 emp.) **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I truly appreciate the unified lakehouse feature of IBM watsonx.data, as it allows me to keep all types of data in a single platform, which significantly simplifies analytics and eliminates the hassle of juggling multiple tools. I love the cost-efficient queries; being able to choose the best engine for the workload helps to reduce compute costs and boosts performance, which is a major asset. The strong governance capability is another aspect I value greatly, as it provides centralized access control and data cataloging. This ensures that data remains secure, compliant, and trusted—qualities crucial for enterprise environments. Additionally, the easy access to data across both cloud and on-premises systems without needing to relocate it is incredibly time-saving and reduces the effort required for data queries. Overall, these features make IBM watsonx.data an invaluable resource for managing and analyzing enterprise data. **What do you dislike about IBM watsonx.data?** Setting up IBM watsonx.data can be complex, requiring skilled expertise, which creates a barrier for new users. The user interface has a learning curve and needs refinement to be more user-friendly. While integration works well with IBM tools, it needs improvement for non-IBM ecosystems, particularly in supporting multi-cloud systems, which can slow down adoption for organizations using diverse technological environments. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to manage and analyze enterprise data efficiently in a unified lakehouse, reducing costs and improving performance with cost-efficient queries. Its strong governance and easy access to data enhance security and compliance, streamlining AI application development. ### 24. Data Ingestion **Rating:** 3.5/5.0 stars **Reviewed by:** firdous ahmad B. | cloud architect, Mid-Market (51-1000 emp.) **Reviewed Date:** September 30, 2025 **What do you like best about IBM watsonx.data?** Architecturally watsonx.data is best where you could add as many as catalogs and federation has been made easy. access control makes a big difference so does the multi engines like presto,spark,db2warehouse etc. **What do you dislike about IBM watsonx.data?** I wish the integration part is tightly attached to watsonx.data UI in order to run jobs directly from watsonx.data UI rather than going into other services like integration and run job from there.I found many issues with Presto c++ when inserting the data , i dont know if that is limitation: Limited File Format Support Restricted Table Creation Syntax & Compatibility Issues Catalog Limitations Data Ingestion Challenges:Can't load CSV directly into tables using code: Presto/Trino vs Other Technologies: Feature Presto/Trino Spark SQL Databricks DML Operations ❌ Limited ✅ Full ✅ Full CSV Support ❌ Limited ✅ Full ✅ Full Delta Lake ❌ No ✅ Yes ✅ Native Table Creation ❌ Restricted ✅ Full ✅ Full Data Ingestion ❌ Complex ✅ Easy ✅ Easy **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data solved the data silos ### 25. Reliable Tool for Handling Large and Mixed Data **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** November 23, 2025 **What do you like best about IBM watsonx.data?** What I like most about IBM watsonx.data is that it brings different types of data into one place in a clean and organized way. The interface is simple to understand, so it didn’t take me long to get comfortable using it. It also handles larger datasets quite well, which is useful when working on analytics or reporting tasks. I also appreciate that it comes with helpful features around governance and access control. Setting up permissions is easy, and it feels well integrated with other IBM tools, so I don’t have to jump between platforms. Overall, it makes daily data work smoother. **What do you dislike about IBM watsonx.data?** The main thing I don’t like is that the initial setup takes some time, especially for someone not familiar with IBM’s overall environment. A few parts need multiple configuration steps, and I had to go through the documentation several times to understand how certain features worked. Customer support is helpful, but sometimes the documentation could be clearer, which would reduce the need to contact support in the first place. Once everything is set up, though, the system works steadily without much trouble. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data helps me organize different types of data in one central place, which makes daily data work much easier. Before this, data was spread across different systems and it was time-consuming to manage access and keep everything consistent. With watsonx.data, I can quickly process large datasets and run analytics without waiting too long. The built-in governance tools also help control who can access what, so data stays secure and well managed. Overall, it saves time, reduces manual work, and makes it easier to use data for reporting and analysis. ### 26. Unified Data Access with Smooth AI Integration **Rating:** 3.5/5.0 stars **Reviewed by:** Siddhant K. **Reviewed Date:** November 23, 2025 **What do you like best about IBM watsonx.data?** I enjoy how IBM watsonx.data lets me access all my information from a single spot, regardless of where it's saved across various setups. It handles both organized and unstructured data smoothly, allowing for fast operations and cost savings while enabling me to uncover answers more quickly. I also appreciate its smooth AI integration and strong governance features that keep my data secure and well-managed. Furthermore, it provides a unified environment where I can use SQL, analytics, and AI tools together, simplifying work processes for different teams. The platform's support for federated queries makes analyzing data faster without the need for heavy ETL processes, and its ability to handle complex and messy data alongside clean tables under one roof is highly beneficial. **What do you dislike about IBM watsonx.data?** Some aspects of IBM watsonx.data could be improved. Initially, it's challenging to navigate due to a steep learning curve, and the layout can be overwhelming for beginners. Integrating it with certain external applications requires more steps than necessary, which could be streamlined. A cleaner design and smoother setup process would facilitate easier adoption for new users. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data consolidates scattered data, reduces cost with a lakehouse approach, simplifies handling unstructured data, unifies tool usage, and enhances governance. It speeds up insights with federated queries, supporting both structured and unstructured data retrieval efficiently. ### 27. Easily Integrate with Powerful Exploration Features **Rating:** 4.5/5.0 stars **Reviewed by:** RUPESH R. **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I really appreciate the capabilities of IBM watsonx.data in supporting my recent project, particularly for creating a function to change the localhost to an IP address on a local machine. This tools adds to my confidence and allows me to explore and gain deep knowledge on specific topics and points relevant to my coding needs. I find it incredibly valuable for running complex concepts and generating PDFs on a Linux server, which is highly beneficial for my project requirements. The integration simplicity particularly stands out for me, as it's very easy to integrate, especially when using AI agents. It supports creating chatbots or AIAgents seamlessly, which expands my ability to implement AI solutions efficiently. **What do you dislike about IBM watsonx.data?** I faced a challenge with the key generator during the initial setup of IBM watsonx.data, which made the setup process difficult. **What problems is IBM watsonx.data solving and how is that benefiting you?** I find IBM watsonx.data simplifies my project tasks by transforming localhost to IP addresses and facilitates deep exploration and understanding of complex concepts, especially for generating PDFs and creating AI agents efficiently. ### 28. Fast and Intuitive, Yet Needs Streamlined UI **Rating:** 4.0/5.0 stars **Reviewed by:** Shweta B. **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I like that IBM watsonx.data is fast and easy to use. It allows me to store all my data in one place and execute quick queries, which is essential for managing large datasets. I appreciate its seamless integration with AI and analytics tools, simplifying my workflow. The platform helps me manage big data efficiently, reducing my struggles with slow queries and messy data. I value how it works well with cloud storage and BI tools like Power BI and Tableau, facilitating organized data visualization in BI dashboards. The initial setup was relatively smooth, aided by comprehensive documentation, which made the transition hassle-free. **What do you dislike about IBM watsonx.data?** Sometimes the platform feels heavy when switching between different tools or views. A few advanced features take extra clicks to reach, and I think the UI could be more streamlined. Also, integrations outside the IBM ecosystem could be a bit smoother. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to manage large datasets effectively, improving my experience by reducing issues with slow queries and messy data, and enhancing my work with fast, easy-to-use features that integrate well with AI, analytics tools, and BI dashboards. ### 29. Intuitive UI, With Some Response Delays **Rating:** 3.5/5.0 stars **Reviewed by:** KARTIK J. **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I like the intuitive user interface of IBM watsonx.data, as it makes the setup process fairly easy and minimizes the need to overthink due to its well-organized layout. The UI placement is very thoughtful, ensuring that I can navigate the software without having to search around or get confused about where things are. This ease of use and intuitive design are significant benefits when working with data structuring, as I use IBM watsonx.data for normal factoring of data. Overall, the product's UI stands out as a highly positive aspect that contributes to a streamlined and efficient user experience. **What do you dislike about IBM watsonx.data?** I find there is a significant delay in the response time, which can be frustrating. Additionally, IBM watsonx.data sometimes encounters issues with my proxy server, causing it to get blocked, which disrupts my workflow. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data for normal factoring and structuring of data, benefiting from its intuitive UI that simplifies data management without the need to think much about placement. ### 30. User-Friendly Interface and Seamless Open-Source Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Madhav M. | Associate DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 15, 2025 **What do you like best about IBM watsonx.data?** The user-friendly interface makes it easy to work with data, and the platform’s foundation on open-source software helps avoid vendor lock-in. This also allows for seamless integration with existing data stored in other cloud storage solutions, such as Azure Blob. **What do you dislike about IBM watsonx.data?** The lack of support for JSON or multi-file inputs in batch deployment jobs is a significant drawback. Additionally, some data types, such as char and time, are not handled properly, and there are also restrictions on the maximum length allowed for varchar fields. **What problems is IBM watsonx.data solving and how is that benefiting you?** We use multiple cloud providers, which has resulted in our data being fragmented across different cloud zones. This fragmentation makes it challenging to obtain a unified view of our data, and we are unable to rely on a single storage system because clients sometimes require us to use the cloud service they already have, such as data stored in azure blob or aws s3. watsonx data addresses this issue by providing a single point of entry to access all our data. ### 31. Reliable Data Access with a Steep Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Aman K. | Programmer Analyst Trainee **Reviewed Date:** November 14, 2025 **What do you like best about IBM watsonx.data?** I use IBM watsonx.data primarily for training my AI models, and it significantly aids me in my learning purposes. The standout feature for me is its reliability, which provides governed, high-performance, and consistent access to data across hybrid environments. The platform's ability to use open formats along with robust metadata management is a huge advantage. I appreciate that I can access data from anywhere in a very hassle-free manner, which solves a common problem for me because, in my experience, similar models tend to require a lot of information, making them ultimately unusable. These aspects make IBM watsonx.data an excellent tool for my requirements. **What do you dislike about IBM watsonx.data?** I find that IBM watsonx.data could improve its ease of use. It has a steep learning curve which makes it less accessible for beginners. The user interface is not very intuitive, adding to the difficulty of using the software effectively. Setting up the application is complex unless you thoroughly understand the necessary steps. Moreover, there is limited seamless integration with non-IBM tools, which could hinder its use in environments that rely on diverse software solutions. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data for training AI models. It solves access issues by enabling data access from anywhere with high performance and consistent data across environments, reducing the hassle compared to other models. ### 32. Flexible, High-Performance Platform with Outstanding Value **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** This platform is extremely flexible and cost-effective. It combines the adaptability of a data lake with the high performance of a data warehouse. Additionally, it offers built-in support for multiple engines tailored to different workloads. **What do you dislike about IBM watsonx.data?** It is still relatively new and lacks the maturity found in platforms like Snowflake and Databricks. The user interface is not very polished and can be slow at times. Additionally, the setup process can occasionally be complex. **What problems is IBM watsonx.data solving and how is that benefiting you?** Our data is scattered across multiple systems, both in the cloud and on-premises. We have faced challenges with siloed data in these environments. watsonx.data allows us to query data wherever it resides, eliminating the need to move or duplicate it. Another issue we've encountered is that traditional data warehouses are costly and have limited scalability. watsonx.data has helped us reduce costs, as we only pay for what we use. ### 33. Real-Time Data Analysis with Effortless Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Urvish T. **Reviewed Date:** November 14, 2025 **What do you like best about IBM watsonx.data?** I greatly appreciate IBM watsonx.data's real-time data analysis capabilities and robust storage. They are essential for handling data related to active user interactions, enabling me to generate real-time recommendations that enhance user experience. The platform effectively solves data streaming and storage issues for my applications, which focus on monitoring user behaviors and interactions with stories. The initial setup was straightforward, making it easy to create data flow pipelines, contributing to its user-friendly nature. Because of these features and overall performance, I find it to be a complete solution that meets my needs without the need to look elsewhere, rating it highly at 9.5 out of 10. **What do you dislike about IBM watsonx.data?** I haven't used any other service, and I liked what I used **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data for data streaming, storage, and real-time analysis, which enhances user behavior insights and allows us to generate immediate recommendations. ### 34. Unified Data Access that Streamlined Our Data Fragmented Environment **Rating:** 4.5/5.0 stars **Reviewed by:** Charan S. | (ISC)² Volunteer, Enterprise (> 1000 emp.) **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I would say IBM watsonx.data has stopped us from juggling between different tools. We as a team get the data from different sources - customer interaction data, transaction logs and unstructured documents then watsonx.data brings it all together in an open lakehouse format. Now we have the ability to query this data with different engines, it greatly reduced our turnaround time. **What do you dislike about IBM watsonx.data?** Only thing which our team had difficulty in performance tuning for watsonx.data. As it offers different engines for different workload, some of our team members ran a heavy job on wrong engine which isn't suitable for that task then we had to standardize our internal guidelines. **What problems is IBM watsonx.data solving and how is that benefiting you?** As we were handling sensitive customer data, governance was one of the time consuming task for us. Watsonx.data has the built in governance, policy enforcement which makes the compliance easier. ### 35. Effective Workloads Optimization and Nice Real-Time Data Analytics Platform. **Rating:** 4.5/5.0 stars **Reviewed by:** Christine G. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** December 03, 2025 **What do you like best about IBM watsonx.data?** This system simplifies data accessibility and data storage and the ability to migrate multiple IT projects data its excellent and easy optimize workloads. Effective data management solution via Hybrid Cloud infrastructure and easy secure business data using this IBM platform and the real time data analytics generation this product is the master. **What do you dislike about IBM watsonx.data?** Useful and very friendly system to get used to and its implementation process is quite simple, and no serious training needed during the initial point. **What problems is IBM watsonx.data solving and how is that benefiting you?** Helpful on Cloud data secure storage and easy to access all the required business data and even to integrate across other platforms is amazing and excellent solution metadata easy management and creating clean and reliable real time data reports and analytics through this IBM tool is effective. ### 36. A seamless backend for building powerful AI agents with Langflow + AstraDB **Rating:** 5.0/5.0 stars **Reviewed by:** Marc K. | CEO, Small-Business (50 or fewer emp.) **Reviewed Date:** July 15, 2025 **What do you like best about IBM watsonx.data?** DataStax made it incredibly easy to build and scale our AI agent with Langflow. AstraDB’s serverless architecture meant we didn’t have to worry about provisioning infrastructure, and the integration with vector search made RAG workflows lightning-fast. We especially loved how well AstraDB plugged into Langflow – it felt like building with building blocks. The documentation is clean, the UI is intuitive, and support was responsive and helpful whenever we had questions. If you’re building anything AI-driven with persistent memory, AstraDB is a no-brainer. **What do you dislike about IBM watsonx.data?** While AstraDB is incredibly powerful, the learning curve can be a bit steep for first-time users — especially around schema design and understanding CQL for more complex queries. We also noticed that some SDKs or tooling examples lag behind the latest feature releases, which required digging through docs or GitHub issues. That said, the support team and community are active and helpful when you hit a wall. **What problems is IBM watsonx.data solving and how is that benefiting you?** We needed a scalable, low-latency vector database to power our AI agent’s memory and retrieval workflows. DataStax Astra DB gave us exactly that — without the DevOps burden. It helps us manage embeddings efficiently and query them with speed, enabling real-time search and personalized responses inside our Langflow-based LLM app. It’s saved us significant engineering time while allowing us to ship faster and more reliably. ### 37. Total Flexibility for Queries in Multiple Engines and Open Formats **Rating:** 5.0/5.0 stars **Reviewed by:** Vincenzo M. | Freelance Digital Marketing Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** December 18, 2025 **What do you like best about IBM watsonx.data?** I like that IBM watsonx.data allows querying the same data with different engines (for example, SQL with Presto and processing with Spark) on open formats like Iceberg, without duplicating datasets. I also highly value those that have multiple support channels. **What do you dislike about IBM watsonx.data?** Sometimes the least comfortable thing is that, being so flexible, it requires a little more judgment at the beginning to clearly define the "path" (engines, catalog, and data governance). **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data reduces the complexity of having data spread across the data lake, the warehouse, and operational systems—each with its own access and governance—and unifies it into a lakehouse-type experience for analytics and AI. ### 38. Cost-Effective and Flexible, Needs UI/UX Improvements **Rating:** 4.0/5.0 stars **Reviewed by:** amir a. | Senior Software Developer **Reviewed Date:** February 16, 2026 **What do you like best about IBM watsonx.data?** I like using IBM watsonx.data because it is cost-effective and flexible for working in a hybrid cloud environment. It makes it easy for me to restructure my data and organize unstructured data, which helps me understand the correct picture of the business. Additionally, setting up IBM watsonx.data was quite easy. **What do you dislike about IBM watsonx.data?** Sometimes performance and also UI/UX need to be improved. **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to organize unstructured data, helping me understand the correct picture of business. ### 39. Effortless Data Integration and Performance for Real Teams **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Media Production | Small-Business (50 or fewer emp.) **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** The best thing about IBM watsonx.data is how easy it makes working with data from different sources without forcing everything into one system. I like that I can query data across warehouses, lakes, and other storage layers in one place instead of constantly moving or duplicating data. The performance is solid even with large datasets, and the integration with open formats like Iceberg is a big plus because it keeps things flexible and not vendor-locked. The UI isn’t flashy, but it’s clean and practical — it’s easy to onboard new people without a huge learning curve. Overall, it feels like something built for real data teams rather than just a marketing buzzword tool. **What do you dislike about IBM watsonx.data?** What I dislike about IBM watsonx.data is that some parts of the platform still feel like they’re evolving. Features are there, but not always as polished or smooth as you’d expect. The setup requires a bit more configuration compared to newer SaaS data platforms, and if you’re not already familiar with the IBM ecosystem, the learning curve can feel steeper than needed. Documentation is good but scattered, so sometimes you’re switching between pages to figure things out. It’s a powerful tool once everything is in place, but getting to that point takes more time and effort than I expected. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data helps solve the challenge of working with data stored across different platforms without constantly moving it around. It lets me query everything in one place, which saves time, reduces storage costs, and speeds up analytics and decision-making. ### 40. Precision and Ease with IBM watsonx.data **Rating:** 4.0/5.0 stars **Reviewed by:** Anupkumar Y. | Cloud Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** November 22, 2025 **What do you like best about IBM watsonx.data?** I find IBM watsonx.data particularly valuable for its reasoning and precision in handling responses, especially when developing AI agents like chatbots for improving user experience in a hotel management system. The initial setup was quite good, which made the integration process smooth and efficient. Although I'm still in the development phase, my early impressions are positive, and the system works well with MCP, the other tool I use. Overall, I'd confidently rate it an 8 out of 10 in terms of likelihood to recommend to a friend or colleague. **What do you dislike about IBM watsonx.data?** Nothing, I just tried **What problems is IBM watsonx.data solving and how is that benefiting you?** I use IBM watsonx.data to develop chatbots for hotel management systems, enhancing user experience with precise and well-reasoned responses. ### 41. Datastax and Langflow - Interconnected Systems to Build and Prototype RAG Applications Easily **Rating:** 5.0/5.0 stars **Reviewed by:** Krishna G. | Chief Operating Officer, Small-Business (50 or fewer emp.) **Reviewed Date:** July 12, 2025 **What do you like best about IBM watsonx.data?** As a company building and stress-testing RAG pipelines daily, the combination of DataStax Astra DB and Langflow has been a game-changer. DataStax delivers scalable, high-speed vector search with excellent integration via the Astra DB and LangChain ecosystem—perfect for low-latency, high-volume workloads. Langflow, on the other hand, makes LLM orchestration visual and intuitive. It accelerates prototyping while still being customizable enough for production-grade workflows. Together, they reduce dev time significantly and let me focus more on refining prompts and grounding logic, rather than infrastructure. Pros: Astra DB’s fast vector search and native LangChain support Langflow’s drag-and-drop interface for rapid experimentation Easy integration with OpenAI, Cohere, and other providers Scales well without overcomplicating the stack **What do you dislike about IBM watsonx.data?** Langflow is Currently in Preview which might limit deployment to Production Environments **What problems is IBM watsonx.data solving and how is that benefiting you?** Helping us build and iterate RAG Workflows at scale with simple UI and Testing ### 42. Effortless Data Management and Seamless Integration in One Platform **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2025 **What do you like best about IBM watsonx.data?** What I appreciate most is how smoothly it manages large amounts of data without slowing down. Being able to connect various data sources, explore them, and run queries seamlessly is a big plus. I also value how everything is organized in a single platform—there’s no need to switch between multiple tools just to accomplish a straightforward task. This not only saves me time but also spares my patience. That’s really what made it stand out for me. **What do you dislike about IBM watsonx.data?** The main drawback for me is the initial learning curve. If you’re not already familiar with IBM’s ecosystem, it can take some time to get a handle on how everything is organized—the setup, the integrations, and the governance layers all require some adjustment. Another issue is that certain features seem a bit too dispersed. At times, you have to navigate through several menus just to reach settings that should be easier to find. While this isn’t a deal-breaker, it does slow you down when you need to work quickly. Finally, although the platform generally performs well, I think the documentation could benefit from more real-world examples and guidance for edge cases. For enterprise-level tasks, those specifics are important. Overall, there’s nothing seriously negative, but these minor issues do create some friction for everyday users. **What problems is IBM watsonx.data solving and how is that benefiting you?** The main challenge this solves for me is managing scattered data. Rather than switching between multiple storage systems and tools, watsonx.data provides a single platform where I can handle everything querying, governance, access control, and analytics. This consolidation alone saves significant time and helps minimize errors. It also addresses performance concerns. Previously, running large queries across data from different sources was often slow or unreliable, but the engine here processes heavy workloads much more efficiently. Governance is another area where I’ve seen improvement. Keeping track of who has access to which datasets and maintaining compliance is typically a complex task, but watsonx.data simplifies this with centralized policy management. In summary, it reduces manual effort, keeps my data well-organized, and allows me to spend more time on analysis rather than constantly managing backend processes. ### 43. Effortless Data Handling and Seamless IBM Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Warehousing | Mid-Market (51-1000 emp.) **Reviewed Date:** November 14, 2025 **What do you like best about IBM watsonx.data?** i like the watsonx.data for handling data sources kinda easy. lakehouse stlye feels smooth and query performs pretty solid even data is large. also i love how it integrates with other IBM tools, so we do not have to do extra jugglin. UI part is not perfect but clean and feels friendly UI-UX for my daily work. it is easy to implement other IBM tools and work with it personally we didn't need to use Customer Support but I am sure it is best as per IBM profile. we are using for one project only in our organization, in addition we are making decision to use for other projects as well. **What do you dislike about IBM watsonx.data?** i personally dislike setting up and onboarding, it takes time to connects other data sources and configuring things properly and sometime docs feels confusing. pricing also bit higher for small teams like us, it is getting hard to make decision just because of pricing. **What problems is IBM watsonx.data solving and how is that benefiting you?** for us IBM watsons is solving major problem of handling too many different data sources in one place, earlier we had to manage all the data sources separately and now we are planning to move our other project in this platform also. it helps to reduce infra overhead cost because scaling storage and compute is smooth, because of this our analytics team can give faster responses and dev team can quickly implement solution for it. overall it saves time and saves some cost but it is bit costly also. ### 44. Reliable Data Platform ,Still Evolving Though **Rating:** 4.0/5.0 stars **Reviewed by:** Akash J. | Business and Integration Arch Senior Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** August 10, 2025 **What do you like best about IBM watsonx.data?** 1.Flexible data handling and fast searches. 2. Queries run quickly and handle diverse data well. 3. I like how fast it processes and manages big data. **What do you dislike about IBM watsonx.data?** 1. Learning takes time for a beginner. 2. Initial setup also takes time. **What problems is IBM watsonx.data solving and how is that benefiting you?** For me, IBM watsonx.data helps address two main challenges — ensuring data quality and making it easily accessible for AI and analytics. In Responsible AI work, having a trusted, well-governed dataset is critical to avoiding bias and ensuring compliance. The platform’s governance tools make it easier to maintain lineage, manage permissions, and apply consistent policies across multiple data sources. It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound. ### 45. Reliable **Rating:** 3.0/5.0 stars **Reviewed by:** Anandu R. | Data Analyst, Small-Business (50 or fewer emp.) **Reviewed Date:** August 10, 2025 **What do you like best about IBM watsonx.data?** What I really like about IBM watsonx.data is its ability to handle and analyze large amounts of structured and unstructured data from different sources all in one place. It’s flexible, integrates well with existing tools, and helps turn raw data into meaningful insights much faster. I also appreciate how it’s built for scalability, so it can grow with the business needs **What do you dislike about IBM watsonx.data?** One thing I’ve noticed is that, because IBM watsonx.data is such a powerful and feature-rich platform, there can be a learning curve for new users to fully leverage all its capabilities. Also, depending on the size of the datasets and complexity of queries, performance tuning might be needed to get the best results. But once you get familiar with it, the benefits outweigh the initial challenges **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data is solving the problem of having data scattered across multiple systems and formats. Instead of spending a lot of time moving and preparing data, I can query and analyze it directly from where it resides, whether it’s in a data lake, warehouse, or external source. This saves time, reduces duplication, and makes it easier to get real-time insights for decision-making. It’s also helping improve collaboration, since different teams can work off the same unified view of the data ### 46. IBM Watsonx Usage Experience **Rating:** 4.0/5.0 stars **Reviewed by:** DEEPAK REDDY K. | Senior Associate, Small-Business (50 or fewer emp.) **Reviewed Date:** August 09, 2025 **What do you like best about IBM watsonx.data?** I have Watsonx for IBM Call for code as it is a Pre-requisite of the Competiton to use the IBM Watsonx. IBM Watsonx has a wide range of AI Products which aligns well with the different usecases. It has it's own Foundation Models Like Granite which we used in our IBM Cal for code Project it's integration with the multiple other models is also easy liek for example Hugging Face Repo and DB Connections as well code Deployment in IBM Cloud. One good thing was the have documentation and walkthrough docs/videos for each and every AI model/functionalty Implementation. These docs/videos helped reduce some time in getting started as they are to the point. Talking about the customer support it is very quick i got problem with my account and got resolved in within a day or so. I have used these IBM Watsonx Three times and alway feel the Functionality and the power of AI integerated tools is amazaing. **What do you dislike about IBM watsonx.data?** The things that i felt could have been more better is the limited Third party Resources and integrations though it has few popular tools and integration for some use cases the watsonx does not support them. The Pricing is more compared to other open resources example if i need Large Model Training or multi model usage in watsonx AI the cost increases there is no proper tanspaernecy in Cost upfront as comaprted to AWS. If i want to use the Watsonx AI with non IBM Tools custom connectors which by user needs to be build up is required which is time taking and some times the implementation goes waste. **What problems is IBM watsonx.data solving and how is that benefiting you?** The AI Models it has huge computatuion and capable of Handeling Large amounts of data sets, example : Granite Models. These Granite Models already pretrianed with large amounts of data and for use case we have used LLM for passing our use case data as context for the Training Models to generate the results for us. The Results are 75-80% accurate. Teh IBM Granite Models have language support where it support large number of Languages across the world. Since it is integrated with the IBM Cloud everything becomes easy from development to Deployment But, if we want get the Third part tools which not supported by IBM is a bit complex to get it working. Rest it is dtraight forward approach if we are using everything like tools, models and apps from the IBM Cloud. ### 47. Makes working with data much easier **Rating:** 4.0/5.0 stars **Reviewed by:** amar c. | Associate Information Security Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** August 09, 2025 **What do you like best about IBM watsonx.data?** I like how easy it is to manage and search large datasets using the platform. The AI-assisted data preparation tools help me clean and organize data much faster than doing it manually. The interface is user-friendly, and the integration with other IBM products makes it easy to fit into our existing workflow. It also handles large amounts of data without slowing down, which is a big plus for my team. **What do you dislike about IBM watsonx.data?** Some of the more advanced analytics features have a steep learning curve and require extra training to use effectively. Also, the cost might be on the higher side for smaller companies. Lastly, it needs a stable internet connection for most operations, so offline work is limited. **What problems is IBM watsonx.data solving and how is that benefiting you?** IBM watsonx.data helps us centralize and manage large volumes of data from multiple sources in one platform. It reduces the time needed for data preparation, cleaning, and organization, allowing our team to focus on analysis and decision-making instead of manual processing. The platform’s AI-driven tools improve the accuracy of our datasets, which leads to more reliable insights for our business. Overall, it has increased productivity and made our data operations much smoother. ### 48. Data as a service, i think this is something fresh and new **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** August 09, 2025 **What do you like best about IBM watsonx.data?** The reason i explored IBM watsonx is, in my current org, we were also building a similiar kind of product, not at this scale but many of the funcitonalitier are common, the feature i liked specially is their prompt lab and how well it is easy to implement, and that actually provides a very good simulation for building different kinds of usecases a person may have. in terms of integration, the data source integration feels seemless a wide variety mainstream connectors are present and easy to integrate, didnt ineracted with the customer support as i didnt have to use it much **What do you dislike about IBM watsonx.data?** This not a beginner friendly tool, a person should be well aware of the current AI-scenario, technical terms and how LLMS works upto some level, the UI is clean and minimal but many time i found a bit of difficulty in navigation between different screens, and sometimes i felt everything is given to me, and that made me confused what should i pick, the point is since there is big chunk of business and non-tech professionals are also adopting the use of LLMs into their workflows, and they could be a user of this platfrom, then the platform should hide some of the configuration and handle it via some assumptions, although this is just an opinion i am not very sure of the target audiene of watsonx. for my use i dont see much of use within my team, and current org, there are already many tools which are free and opensource for instance openmetadata, people who want production ready and readiness to scale within their org as they have that much data to take leverage, and exclusive proprietary platform, which is catered for them then this could be a good choice. **What problems is IBM watsonx.data solving and how is that benefiting you?** the first is its proprietary nature with ease of integration with my data, that will help organization to quickly bootstrap their products, next is the fine tuning and its simulation with prompt labs, this will actually gives the user an idea how his model will behave without wasting much of his resources on billing and computing, ### 49. Helped Us Cut Down Client Onboarding Time at Citi **Rating:** 4.0/5.0 stars **Reviewed by:** Bali R. | Assistant Vice President, Enterprise (> 1000 emp.) **Reviewed Date:** August 08, 2025 **What do you like best about IBM watsonx.data?** I work as an Assistant Vice President in Citi’s client onboarding team, where we handle large volumes of client data from multiple sources — regulatory checks, KYC documents, transaction history, and internal risk systems. Before using watsonx.data, this information was spread across different tools, which made it slow and sometimes frustrating to pull together for verification. We needed a single platform to bring everything into one place so we could move faster while meeting strict compliance requirements. Watsonx.data has given us a dependable central platform for storing and querying client data. Queries that previously took minutes now return results much faster, even with complex joins and large datasets. I also value its tight integration with IBM’s governance and security features, which means compliance checks happen in the background without extra manual work. Sharing consistent, up-to-date data across teams has also become much easier. **What do you dislike about IBM watsonx.data?** The initial setup was the most challenging part. Mapping our existing sources into watsonx.data wasn’t straightforward, and a few integrations needed help from IBM’s support team. The interface works fine but could be more intuitive, especially for new users who don’t have prior experience with enterprise data platforms. **What problems is IBM watsonx.data solving and how is that benefiting you?** In Citi’s client onboarding team, where I work as an Assistant Vice President, we deal with huge amounts of data from different sources — regulatory checks, KYC documents, transaction history, and internal risk systems. Before IBM watsonx.data, this information was scattered across multiple tools, which meant a lot of manual effort to bring it together and verify. Watsonx.data has solved this by giving us a single, governed platform where all of this data can be stored, queried, and shared securely. Now we can run complex queries across large datasets in minutes, and compliance checks are much smoother because the governance features are built in. This has directly helped us cut our client onboarding time from nearly two days to less than a day, which not only improves efficiency for our team but also gives new clients a faster, better experience. ### 50. Senior Analyst **Rating:** 5.0/5.0 stars **Reviewed by:** Abhijeet Kumar A. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** March 26, 2024 **What do you like best about IBM watsonx.data?** It is data management solution for collecting, storing and analyzing enterprise data with a single unified platform. **What do you dislike about IBM watsonx.data?** Starting with the negatives or downs, Watson is : It is available only in English (Limits areas of use) It Seems as disruptive technology similar to that of AI tools. **What problems is IBM watsonx.data solving and how is that benefiting you?** watsonx. data could allow us to easily access and analyze our expansive, distributed data and maximize our resource utilization to deliver superior user experiences. IBM watsonx. data and AWS are enhancing cloud-based analytics and AI, enabling organizations to accelerate their data modernization strategies. It helps enterprises automate the business workflows, streamlining IT process and internal business processes, protecting them against threats and vulnerabilities, and tackling the sustainability goals. It also includes a data store, built on lakehouse architecture, and an AI governance toolkit. IBM Watson performs analytics on vast repositories of data that it processes to answer human-posed questions, often in a fraction of a second. - [View IBM watsonx.data pricing details and edition comparison](https://www.g2.com/products/ibm-watsonx-data/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-12+02%3A56%3A40+-0500&secure%5Bsession_id%5D=29e2d501-4ffb-4edd-bbf6-112f192750b5&secure%5Btoken%5D=d1832dcdc8c6248bd2524b8e6010465076772ba5b651a4171f4c16300c2f32ff&format=llm_user) ## IBM watsonx.data Integrations - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [Amazon Simple Storage Service (S3)](https://www.g2.com/products/amazon-simple-storage-service-s3/reviews) - [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews) - [Apache Spark for Azure HDInsight](https://www.g2.com/products/apache-spark-for-azure-hdinsight/reviews) - [Apache SystemML](https://www.g2.com/products/apache-systemml/reviews) - [Automation Anywhere Agentic Process Automation](https://www.g2.com/products/automation-anywhere-agentic-process-automation/reviews) - 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[Spark](https://www.g2.com/products/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [The Jupyter Notebook](https://www.g2.com/products/the-jupyter-notebook/reviews) ## IBM watsonx.data Features **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations **Management** - Reporting - Auditing **System** - Data Ingestion & Wrangling **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage **Data Management** - Data Migration - Managing Data - Secured Data Storage **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Model Development** - Feature Engineering **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Security** - Access Control - Roles Management - Compliance Management **Data as a Service** - Self-Service Isights - DaaS Quality **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Maintainence** - Data Quality Management - Policy Management **Architecture** - Data Fabric Creation - DaaS Architecture **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Generative AI** - AI Text Generation - AI Text Summarization **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making **Generative AI** - AI Text Generation - AI Text Summarization **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top IBM watsonx.data Alternatives - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (705 reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (780 reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,148 reviews)