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Flexible Lakehouse Platform with Good Performance and Scalability
What do you like best about the product?
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 the product?
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 the product 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.
Flexible Integration, Complex Learning Curve
What do you like best about the product?
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 the product?
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 the product 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.
Streamlines Data Management with Robust Features
What do you like best about the product?
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 the product?
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 the product 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.
Powerful Platform with Complex Setup
What do you like best about the product?
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 the product?
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 the product 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.
Robust Data Security with a Learning Curve
What do you like best about the product?
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 the product?
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 the product 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.
Powerful Analytics, Steep Learning Curve
What do you like best about the product?
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 the product?
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 the product 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.
Effortless Data Management, Inclusive Governance
What do you like best about the product?
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 the product?
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 the product 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.
Flexible, High-Performance Lakehouse for Modern Analytics at Scale
What do you like best about the product?
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 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 the product?
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.
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 the product 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
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
Efficient Data Management with Powerful Analytics
What do you like best about the product?
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 the product?
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 the product 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.
IBM watsonx.data: Flexible Lakehouse SQL on Object Storage with Iceberg Support
What do you like best about the product?
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.
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 the product?
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 the product 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.
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.
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