IBM watsonx.data as a Service
IBM SoftwareExternal reviews
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Great tool to support our future
What do you like best about the product?
it gives a view into data at an enterprise level
What do you dislike about the product?
a little clunky especially at the beginning. not very intuitive either.
What problems is the product solving and how is that benefiting you?
Accelerate growth and speed to market with new product offerings
Great tool for our future
What do you like best about the product?
When paired correctly it can be very powerful
What do you dislike about the product?
At first use it's a bit clunky and didn't give us what we really expected
What problems is the product solving and how is that benefiting you?
We want to use it to identified structured and unstructured documents easily and get their data.
WatsonX.data review
What do you like best about the product?
Integration with all variety of data bases
What do you dislike about the product?
Still exploring, will find more as our organization use it more
What problems is the product solving and how is that benefiting you?
Generating data insights
watsonx.data review
What do you like best about the product?
that we can use the milvus database and that it connects easily to the rest of the ibm services.
What do you dislike about the product?
Milvus: milvus admin permissions suck a little, whoever makes it has total control and access is totally seperate from rest of account permissions.
What problems is the product solving and how is that benefiting you?
Solving the problem of holding our data for our project.
Great experience with automation
What do you like best about the product?
it gets many unknown information from your data
What do you dislike about the product?
sometimes it is to slow and cosumes to much energy
What problems is the product solving and how is that benefiting you?
it gets a lot of our data and many unknown information
Data Ingestion
What do you like best about the product?
Architecturally watsonx.data is best where you could add as many as catalogs and federation has been made easy. access control makes a big difference so does the multi engines like presto,spark,db2warehouse etc.
What do you dislike about the product?
I wish the integration part is tightly attached to watsonx.data UI in order to run jobs directly from watsonx.data UI rather than going into other services like integration and run job from there.I found many issues with Presto c++ when inserting the data , i dont know if that is limitation:
Limited File Format Support
Restricted Table Creation
Syntax & Compatibility Issues
Catalog Limitations
Data Ingestion Challenges:Can't load CSV directly into tables using code:
Presto/Trino vs Other Technologies:
Feature Presto/Trino Spark SQL Databricks
DML Operations ❌ Limited ✅ Full ✅ Full
CSV Support ❌ Limited ✅ Full ✅ Full
Delta Lake ❌ No ✅ Yes ✅ Native
Table Creation ❌ Restricted ✅ Full ✅ Full
Data Ingestion ❌ Complex ✅ Easy ✅ Easy
Limited File Format Support
Restricted Table Creation
Syntax & Compatibility Issues
Catalog Limitations
Data Ingestion Challenges:Can't load CSV directly into tables using code:
Presto/Trino vs Other Technologies:
Feature Presto/Trino Spark SQL Databricks
DML Operations ❌ Limited ✅ Full ✅ Full
CSV Support ❌ Limited ✅ Full ✅ Full
Delta Lake ❌ No ✅ Yes ✅ Native
Table Creation ❌ Restricted ✅ Full ✅ Full
Data Ingestion ❌ Complex ✅ Easy ✅ Easy
What problems is the product solving and how is that benefiting you?
IBM watsonx.data solved the data silos
Reliable Data Platform ,Still Evolving Though
What do you like best about the product?
1.Flexible data handling and fast searches.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
What do you dislike about the product?
1. Learning takes time for a beginner.
2. Initial setup also takes time.
2. Initial setup also takes time.
What problems is the product solving and how is that benefiting you?
For me, IBM watsonx.data helps address two main challenges — ensuring data quality and making it easily accessible for AI and analytics. In Responsible AI work, having a trusted, well-governed dataset is critical to avoiding bias and ensuring compliance. The platform’s governance tools make it easier to maintain lineage, manage permissions, and apply consistent policies across multiple data sources.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
Reliable
What do you like best about the product?
What I really like about IBM watsonx.data is its ability to handle and analyze large amounts of structured and unstructured data from different sources all in one place. It’s flexible, integrates well with existing tools, and helps turn raw data into meaningful insights much faster. I also appreciate how it’s built for scalability, so it can grow with the business needs
What do you dislike about the product?
One thing I’ve noticed is that, because IBM watsonx.data is such a powerful and feature-rich platform, there can be a learning curve for new users to fully leverage all its capabilities. Also, depending on the size of the datasets and complexity of queries, performance tuning might be needed to get the best results. But once you get familiar with it, the benefits outweigh the initial challenges
What problems is the product solving and how is that benefiting you?
IBM watsonx.data is solving the problem of having data scattered across multiple systems and formats. Instead of spending a lot of time moving and preparing data, I can query and analyze it directly from where it resides, whether it’s in a data lake, warehouse, or external source. This saves time, reduces duplication, and makes it easier to get real-time insights for decision-making. It’s also helping improve collaboration, since different teams can work off the same unified view of the data
IBM Watsonx Usage Experience
What do you like best about the product?
I have Watsonx for IBM Call for code as it is a Pre-requisite of the Competiton to use the IBM Watsonx. IBM Watsonx has a wide range of AI Products which aligns well with the different usecases. It has it's own Foundation Models Like Granite which we used in our IBM Cal for code Project it's integration with the multiple other models is also easy liek for example Hugging Face Repo and DB Connections as well code Deployment in IBM Cloud. One good thing was the have documentation and walkthrough docs/videos for each and every AI model/functionalty Implementation. These docs/videos helped reduce some time in getting started as they are to the point. Talking about the customer support it is very quick i got problem with my account and got resolved in within a day or so. I have used these IBM Watsonx Three times and alway feel the Functionality and the power of AI integerated tools is amazaing.
What do you dislike about the product?
The things that i felt could have been more better is the limited Third party Resources and integrations though it has few popular tools and integration for some use cases the watsonx does not support them. The Pricing is more compared to other open resources example if i need Large Model Training or multi model usage in watsonx AI the cost increases there is no proper tanspaernecy in Cost upfront as comaprted to AWS. If i want to use the Watsonx AI with non IBM Tools custom connectors which by user needs to be build up is required which is time taking and some times the implementation goes waste.
What problems is the product solving and how is that benefiting you?
The AI Models it has huge computatuion and capable of Handeling Large amounts of data sets, example : Granite Models. These Granite Models already pretrianed with large amounts of data and for use case we have used LLM for passing our use case data as context for the Training Models to generate the results for us. The Results are 75-80% accurate. Teh IBM Granite Models have language support where it support large number of Languages across the world. Since it is integrated with the IBM Cloud everything becomes easy from development to Deployment But, if we want get the Third part tools which not supported by IBM is a bit complex to get it working. Rest it is dtraight forward approach if we are using everything like tools, models and apps from the IBM Cloud.
Makes working with data much easier
What do you like best about the product?
I like how easy it is to manage and search large datasets using the platform. The AI-assisted data preparation tools help me clean and organize data much faster than doing it manually. The interface is user-friendly, and the integration with other IBM products makes it easy to fit into our existing workflow. It also handles large amounts of data without slowing down, which is a big plus for my team.
What do you dislike about the product?
Some of the more advanced analytics features have a steep learning curve and require extra training to use effectively. Also, the cost might be on the higher side for smaller companies. Lastly, it needs a stable internet connection for most operations, so offline work is limited.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data helps us centralize and manage large volumes of data from multiple sources in one platform. It reduces the time needed for data preparation, cleaning, and organization, allowing our team to focus on analysis and decision-making instead of manual processing. The platform’s AI-driven tools improve the accuracy of our datasets, which leads to more reliable insights for our business. Overall, it has increased productivity and made our data operations much smoother.
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