Sold by: IBM Software
Deployed on AWS
Built on a lakehouse architecture, IBM watsonx.data is an open, hybrid, and governed data store optimized for all data, analytics, and AI workloads.
4.4
Overview
IBM watsonx.data is an open, hybrid, and governed data store built on an open data lakehouse architecture. The data lakehouse is an emerging architecture that offers the flexibility of a data lake with the performance and structure of a data warehouse. Watsonx.data is an enterprise-ready data store that enables hybrid cloud analytics workloads such as data engineering, data science and business intelligence, through open-source components with integrated IBM innovation.
Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines such as Presto and Spark across IT environments.With the integration of DataStax Astra DB, watsonx.data now extends beyond analytics to support real time operational workloads and advanced AI applications. Astra DB brings enterprise-grade vector database capabilities and multi-model data support, enabling organizations to build generative AI applications, real time recommendation engines, and high-performance operational systems,all within the same unified platform. This integration eliminates the need for separate operational databases and provides seamless data flow between transactional and analytical workloads. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. It also offers built-in governance, automation and integrations with an organization's existing databases and tools to simplify setup and user experience.
Db2 Warehouse and Netezza on AWS natively integrate with watsonx.data with shared metadata and support for open formats such as Parquet and Iceberg to share and combine data for new insights without ETL. Watsonx.data allows customers to augment data warehouses such as Db2 Warehouse and Netezza and optimize workloads for performance and cost.
For trials and customized IBM watsonx.data pricing contact your IBM Sales Representative or email us at watsonx_on_AWS@wwpdl.vnet.ibm.com Visit https://www.ibm.com/products/watsonx-data
to learn more about our consumption model and product editions.
For more information on IBM watsonx.data visit https://www.ibm.com/products/watsonx-data
Highlights
- Access all your data across hybrid-cloud: Access all data through a single point of entry with a shared metadata layer across clouds and on-premises environments.
- Get started in minutes: Connect to storage and analytics environments in minutes and enhance trust in data with built-in governance, security, and automation.
- Reduce the cost of your data warehouse by up to 50% through workload optimization: Optimize costly data warehouse workloads across multiple query engines and storage tiers, pairing the right workload with the right engine.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Extra-small Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 2000 Resource Units | $2,000.00 |
Small Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 20000 Resource Units | $20,000.00 |
Medium Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 50000 Resource Units | $50,000.00 |
Large Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 100000 Resource Units | $100,000.00 |
Dimension | Cost/unit |
|---|---|
Overage charge for overconsumption of contracted resource units | $1.10 |
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
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AI generated from product descriptions
Lakehouse Architecture
Open data lakehouse architecture combining the flexibility of data lakes with the performance and structure of data warehouses
Multi-Engine Query Processing
Support for multiple fit-for-purpose query engines including Presto and Spark across IT environments
Vector Database Integration
Enterprise-grade vector database capabilities through DataStax Astra DB integration enabling generative AI applications and real-time operational workloads
Open Data Format Support
Native support for open formats such as Parquet and Iceberg enabling data sharing and combination without ETL
Unified Metadata Layer
Shared metadata layer across hybrid-cloud environments providing single point of entry for data access across clouds and on-premises
Lakehouse Architecture
Built on a lakehouse foundation providing unified data storage and governance across data engineering, analytics, BI, data science, and machine learning workloads
Open Source Integration
Constructed on open source data projects and open standards to maximize flexibility and interoperability across the data ecosystem
Data Intelligence Engine
Powered by a Data Intelligence Engine that enables organizational access to data and insights across diverse user roles and technical skill levels
Unified Data Platform
Consolidates data, analytics, and AI workloads on a single common platform running on Amazon S3, eliminating traditional data silos
Collaborative Capabilities
Provides native collaboration features enabling data teams to work together across the entire data and AI workflow
Workload Auto-scaling
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics capabilities with unified analytic functions to eliminate data silos.
Shared Data Experience (SDX)
Implements security and governance policies that are set once and applied consistently across all data and workloads, with portability across supported infrastructures.
Data Lifecycle Management
Manages complete data lifecycle functions including ingestion, transformation, querying, optimization, and predictive analytics across multiple cloud environments.
Unified Security and Governance
Ensures all workloads share common security, governance, and metadata with capabilities for data discovery, curation, and self-service access controls.
Standard contract
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Customer reviews
Sourabh M.
Effortless Data Management, Inclusive Governance
Reviewed on Apr 16, 2026
Review provided by G2
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.
Information Technology and Services
Flexible, High-Performance Lakehouse for Modern Analytics at Scale
Reviewed on Apr 16, 2026
Review provided by G2
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
Konjengbam M.
Powerful, Secure, and Scalable Platform with Easy Data Migration
Reviewed on Apr 15, 2026
Review provided by G2
What do you like best about the product?
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.
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 the product?
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 the product 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.
Sairam B.
IBM watsonx.data: Solving Data Silos and Accelerating AI with a Unified Lakehouse Platform”
Reviewed on Feb 19, 2026
Review provided by G2
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.
Sai pavan kumar D.
Efficient Data Management with Powerful Analytics
Reviewed on Feb 18, 2026
Review provided by G2
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.