Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- Data virtualisation lets you query scattered sources without replicating them.
- It offers a unified, real-time view without moving data.
- It cuts duplication, but depends on the original sources’ performance.
- It is an alternative or complement to replication.
- Choose by case: virtualise or replicate.
Unifying data does not always mean copying it to a common place. Sometimes it is better to leave it where it is and query it at the source. That is the proposition of data virtualisation.
What it is
Data virtualisation lets you access and query data from multiple sources through a unified layer, without physically replicating it in a central repository. The user sees a single view; the data stays at its origin.
Advantages
- No duplication: no copies to maintain and sync.
- Real time: you query the current data at source.
- Fast to implement: avoids long loading processes.
- Less storage: the data is not replicated.
Sources
Stay in place
Virtual layer
Unified viewReal-time query
User
Single viewNo copies
Limitations
Virtualisation is not free: as it queries the original sources in real time, its performance depends on them, and heavy queries can penalise production systems. It is also not ideal when large transformations or history the source does not keep are needed.
Virtualise or replicate
Virtualise when you need a unified real-time view and moving data is not worth it; replicate when you need intensive transformations, history or to isolate analytical load. Many architectures combine both.
Virtualise to see data in real time without moving it; replicate when you need to transform, keep history or isolate load.
In summary
Data virtualisation queries scattered sources through a unified layer without replicating them, giving a real-time view with no duplication — but its performance depends on the sources. It is an alternative or complement to replication, chosen case by case.
Sources & further reading
Frequently asked questions
Does virtualisation replace replication?
Not always. It is an alternative or complement: virtualising avoids moving data and gives real-time views, but replicating is better for intensive transformations, history or isolating analytical load.
What is its main limitation?
Its performance depends on the original sources, so heavy queries can penalise production systems.
When does virtualising suit?
When you need a unified real-time view of several sources and moving or duplicating the data adds no value.
What are its advantages?
No duplication to maintain, real-time access to current data, fast implementation and less storage.
When should I replicate instead?
When you need intensive transformations, historical data the source does not keep, or to isolate analytical load from production.
Can I combine both?
Yes. Many architectures virtualise some sources and replicate others, choosing per case for the best balance.
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