IBM watsonx.data PayGo
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IBM watsonx.data: Solving Data Silos and Accelerating AI with a Unified Lakehouse Platform”
What do you like best about the product?
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 the product?
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 the product 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.
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.
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.
Scalable Analytics Platform with Smooth AI Integration
What do you like best about the product?
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 the product?
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 the product 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.
Enterprise-Ready Data Platform with Flexible Hybrid Support and Built-In Governance
What do you like best about the product?
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 the product?
watsonx.data can be a little complex to get started with
What problems is the product 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
Hybrid Data Solution with Room for Improvement
What do you like best about the product?
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 the product?
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 the product 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.
Powerful Data Analytics and Visualization Tool
What do you like best about the product?
It is very useful for data analytics and visualization.
What do you dislike about the product?
It is taking time when updating data in the system.
What problems is the product solving and how is that benefiting you?
It is solving data processing and data management.
Scalable Lakehouse with Lightning-Fast Query Performance
What do you like best about the product?
Scalable lakehouse with fast query performance
What do you dislike about the product?
Steep learning curve and complex setup initially
What problems is the product solving and how is that benefiting you?
It solves scattered data and slow analytics by centralizing storage in a scalable lakehouse, helping us run faster queries, reduce data movement, and make quicker data-driven decisions.
Cost-Effective and Flexible, Needs UI/UX Improvements
What do you like best about the product?
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 the product?
Sometimes performance and also UI/UX need to be improved.
What problems is the product solving and how is that benefiting you?
I use IBM watsonx.data to organize unstructured data, helping me understand the correct picture of business.
Total Flexibility for Queries in Multiple Engines and Open Formats
What do you like best about the product?
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 the product?
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 the product 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.
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