Pretty good but theres room to grow
Updated November 10, 2025
Pretty good but theres room to grow
Score 7 out of 10
Vetted Review
Verified User
Overall Satisfaction with IBM watsonx.data
Many of our clients come with disjointed data estates: a bit of snowflake here, some redshift there and tons of legacy onprem sql. IBM watsonx.data makes it possible to federate across those without forcing everything into one physical storage layer
Pros
- the biggest one is the open lakehouse architecture.
- a federated query engine
Cons
- it's tricky to see where query latency is creeping in when multiple engines are in play
- for one automation project, we managed to cut cloud storage costs by a third through IBM watsonx.data's lakehouse optimization
- data integration projects have had a 20 % reduction in turnaround times. Can only imagine how that will improve with the Claude partnership
Metadata cataloging and policy enforcement
We can finally query and analyze data across multiple storage systems without having to move or duplicate it. This used to be one [...] of a headache
Do you think IBM watsonx.data delivers good value for the price?
Yes
Are you happy with IBM watsonx.data's feature set?
Yes
Did IBM watsonx.data live up to sales and marketing promises?
Yes
Did implementation of IBM watsonx.data go as expected?
Yes
Would you buy IBM watsonx.data again?
Yes
Using IBM watsonx.data
| Pros | Cons |
|---|---|
Like to use Well integrated Consistent | Unnecessarily complex Requires technical support Slow to learn Cumbersome Lots to learn |
- How seamless it makes query federation across multiple data sources. It's one of those things that looks simple on paper but turns into a nightmare in real life
- policy enforcement and access governance
- Ibm really nails schema discovery
- troubleshooting performance issues in federated queries
- setting up new connectors for niche data sources
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