Sold by: IBM SoftwareÂ
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.
4.3
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-dataÂ
to learn more about our consumption model and product editions.
For more information on IBM watsonx.data visit https://www.ibm.com/products/watsonx-dataÂ
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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Updated weekly
By IBM Software
By Databricks, Inc.
By Dremio
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In Data Warehouses
Top
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In Databases & Analytics Platforms, ML Solutions, Data Analytics
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In Data Warehouses
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
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Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Data Lakehouse Architecture
Built on an open data lakehouse architecture that combines data lake flexibility with data warehouse performance and structure
Multi-Engine Query Support
Supports multiple fit-for-purpose query engines like Presto and Spark across different IT environments
Vector Database Integration
Integrates DataStax Astra DB for enterprise-grade vector database capabilities and multi-model data support
Open Format Compatibility
Natively supports open data formats like Parquet and Iceberg for seamless data sharing and combination
Hybrid Cloud Data Access
Enables unified data access through a single point of entry with a shared metadata layer across hybrid cloud environments
Data Platform Architecture
Unified platform integrating data engineering, analytics, business intelligence, data science, and machine learning on a single architecture
Open Source Foundation
Built on open source data projects with support for open standards and data formats
Lakehouse Infrastructure
Provides a common data management approach using a lakehouse architecture running on Amazon S3
Data Intelligence Engine
Advanced engine capable of interpreting organizational data context and enabling broad data access across teams
Collaborative Workflow
Native collaboration capabilities enabling cross-functional data and AI workflow integration
Data Lake Query Performance
Provides sub-second query response times using SQL query service on data lake platforms
Open Standards Support
Utilizes community-driven standards like Apache Iceberg and Apache Arrow for processing engines
Multi-Source Data Integration
Enables joining data from data lakes and external databases without data movement
Compute Engine Management
Automatically handles compute engine lifecycle including provisioning, scaling, pausing, and decommissioning
VPC-Based Data Processing
Deploys compute engines within customer's Amazon Virtual Private Cloud for secure data processing
Validated by AWS Marketplace
FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
No security profile
No security profile
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Standard contract
No
No
No
Customer reviews
NamanJain
Faced a steep learning curve but have streamlined end-to-end machine learning workflows
Reviewed on Dec 02, 2025
Review from a verified AWS customer
What is our primary use case?
My main use case for IBM Watson Studio is the end-to-end ML life cycle.
A specific example of a project where I used IBM Watson Studio for the end-to-end machine learning life cycle is that I built a data pipeline that takes a Kafka stream and loads the data into our S3 bucket catalog. Then I built a data pipeline on top of it which parses the catalog and provides structured data for our in-house NER recognition model.
What is most valuable?
The best features IBM Watson Studio offers are that it is good for big and complex organizations, it is multi-cloud, it has an on-prem facility, and it also has strong visual tools.
The strong visual tools have helped my organization, which is currently cloud supported and has an on-prem server as well, as IBM Watson Studio serves as a use case for both of them.
IBM Watson Studio has positively impacted my organization by increasing work efficiency. I have noticed the work efficiency increase as it is an end-to-end ML pipeline in one place.
What needs improvement?
I think IBM Watson Studio can be improved.
I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task. Also, I think pricing is a bit high.
For how long have I used the solution?
I have been working in my current field for three plus years.
What do I think about the stability of the solution?
IBM Watson Studio is stable in my experience.
How are customer service and support?
I have not had any experience with customer support for IBM Watson Studio as I have not used it.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We are still using Google AI Studio before IBM Watson Studio, and we have not particularly switched completely to IBM Watson Studio.
We are using Google AI Studio as another option, but I did not evaluate other options before choosing IBM Watson Studio.
What other advice do I have?
I would advise others looking into using IBM Watson Studio to just use the tutorials available to see all the features. I rate this product a 4.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Simon R.
IBM watson.data is a Reliable Data Integration and Secure Hybrid Cloud Management System.
Reviewed on Nov 27, 2025
Review provided by G2
What do you like best about the product?
IBM watsonx.data is an effective multiple projects data access across Hybrid Cloud and very secure data storage platform management platform and even its workload balancing capability is incredible and very reliable big data integration system.
What do you dislike about the product?
This IBM system is very powerful and very friendly platform with simple implementation and easy to configure the functionalities.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data allows easy data accessibility, secure storage for the various IT projects and very useful data integration tool and the real time data analytics creation through the platform is excellent.
SWAPNIL S.
A Unified, Scalable Data Lakehouse
Reviewed on Nov 25, 2025
Review provided by G2
What do you like best about the product?
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.
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 the product?
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
Also the pricing can be a bit confusing if you are not familiar with IBM's consumption model
What problems is the product 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.
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.
Information Technology and Services
Reliable Tool for Handling Large and Mixed Data
Reviewed on Nov 23, 2025
Review provided by G2
What do you like best about the product?
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.
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 the product?
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.
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 the product 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.
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.
Goutam T.
Flexible Platform with Powerful Integration and Hybrid Architecture
Reviewed on Nov 23, 2025
Review provided by G2
What do you like best about the product?
I appreciate this platform for its hybrid and open architecture, as well as its support for multiple engines. I also like that it integrates with tools such as IBM Knowledge Catalog.
What do you dislike about the product?
My main issues are the high rates and the complexity involved in learning how to use it.
What problems is the product solving and how is that benefiting you?
I am abel to access my data from one place and it's AI ready as it simplifies finding and organizing the data.