Comparisons
Data Layer vs. building your own data lake (2026)
An honest comparison of building your own data lake versus using Data Layer: cost, time, risk and outcome for leadership. Data Layer comes out on top.
Read articleKey takeaways
- There is no single integration approach: ETL, ELT, CDC and virtualisation solve different needs.
- The choice depends on volume, latency and data sensitivity.
- Combining several approaches is usually the most efficient.
- What matters is the result, not the specific tool.
- A managed service picks the most efficient approach per case.
When talking about integrating data, it is easy to get lost among acronyms and tools. More useful than comparing specific products — which change every year — is understanding the underlying approaches and when each suits.
The four approaches
Data integration approaches are the different ways to move and combine data between systems. The four main ones are ETL, ELT, CDC and virtualisation, and they rarely compete: they solve different needs.
| Approach | How it works | Where it stands out |
|---|---|---|
| ETL | Transforms before loading | Complex rules or prior privacy |
| ELT | Loads then transforms in destination | ✓ Large volumes, modern cloud |
| CDC | Replicates only changes | ✓ Efficient sync, low latency |
| Virtualisation | Queries without moving | Unified real-time view |
How to choose
The decision depends on three factors: volume (more data favours ELT/CDC), latency (CDC or virtualisation for freshness) and sensitivity (ETL to transform or anonymise before loading). It is not exclusive: modern architectures combine several approaches by source and case.
Volume
→ ELT / CDC
Latency
→ CDC→ Virtualisation
Sensitivity
→ ETL(transform first)
What really matters
For the business, the specific approach is a technical detail. What matters is the result: that data arrives integrated, reliable and on time. A managed service selects and combines the most efficient approach for each case, so the client gets the result without deciding between acronyms.
The right integration approach is a technical detail; the result — integrated, reliable, timely data — is what matters.
In summary
ETL, ELT, CDC and virtualisation solve different integration needs; the choice depends on volume, latency and sensitivity, and modern architectures combine them. For the business, the result matters more than the acronym — and a managed service picks the most efficient approach for each source.
Sources & further reading
Frequently asked questions
Which integration approach is best?
There is no single one. ETL for complex rules or prior privacy, ELT for large volumes, CDC for efficient sync and virtualisation for real-time views.
Do I have to choose just one?
No. Modern architectures combine several approaches depending on the volume, latency and sensitivity of each source.
What matters for the business?
The result: integrated, reliable, timely data. The specific approach is a technical detail a managed service resolves for you.
When is ETL still preferable?
When transformations are complex or when privacy requires transforming or anonymising data before it lands in the destination.
Why has ELT gained ground?
Because scalable storage and compute let you load first and transform in the destination, which scales better with large volumes.
What is CDC best for?
Efficient, low-latency synchronisation: it replicates only the changes, reducing load on source systems.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.