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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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
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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
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.
Swamy G.
IBM watsonx.data: Flexible Lakehouse SQL on Object Storage with Iceberg Support
Reviewed on Feb 18, 2026
Review provided by G2
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.
K S.
Scalable Analytics Platform with Smooth AI Integration
Reviewed on Feb 17, 2026
Review provided by G2
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.
Faizan N.
Enterprise-Ready Data Platform with Flexible Hybrid Support and Built-In Governance
Reviewed on Feb 17, 2026
Review provided by G2
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