What Is Cloud-Native ETL and How Does It Differ from Traditional ETL and ELT?

Updated

Cloud-native ETL is extract-transform-load processing built and run on elastic cloud infrastructure rather than on dedicated on-premises servers. You should treat a tool as genuinely cloud-native when it scales compute independently of storage, is operated and updated by the vendor, and connects broadly to cloud and SaaS sources. It differs from traditional ETL mainly in where and how it runs, and from cloud-native ELT in where the transformation happens: ETL transforms before loading, ELT loads raw data first and transforms it in the target warehouse or lakehouse.

Defining cloud-native ETL

The "cloud-native" part describes the architecture, not just the hosting. A cloud-native ETL service runs on the provider's managed infrastructure, so capacity can grow or shrink with workload instead of being capped by a fixed server. The vendor handles patching, availability, and scaling; your team designs pipelines and governs data.

Informatica positions its platform as cloud data management spanning integration, governance, master data management, data quality and observability, and cataloging, with 300+ connectors for data integration and connectivity across 50,000+ sources. That breadth is the practical meaning of "cloud-native" for buyers: pipelines reach cloud warehouses, SaaS apps, and databases without building a connector for each one.

Cloud-native ETL vs. traditional on-premises ETL

Dimension Traditional on-premises ETL Cloud-native ETL
Infrastructure Fixed servers you procure and maintain Elastic, vendor-managed cloud capacity
Scaling Add hardware; slow and capital-intensive Scale compute up or down with demand
Maintenance Your team patches, upgrades, and tunes Vendor handles platform updates
Cost shape Upfront hardware and licensing Consumption-aligned, but verify contract terms
Reach Connectors built and maintained in-house Large prebuilt connector libraries

The trade-off is control versus operational burden. On-premises ETL can suit workloads that must stay in a specific data center; cloud-native ETL fits organizations whose sources and targets are increasingly cloud and SaaS.

ETL vs. ELT in cloud settings

Both patterns move data from sources to an analytics target; they differ in sequencing.

  • ETL: transform data in the pipeline before it lands in the target. Fits when the target should receive only cleansed, conformed data, or when transformation logic must run outside the warehouse.
  • ELT: load raw data into a cloud warehouse or lakehouse, then transform using its compute. Fits when the target has ample elastic compute and you want raw history preserved for reprocessing.

In the cloud the line blurs: many platforms support both patterns, and the choice often comes down to where transformation compute is cheapest and which team owns the logic. Informatica markets both cloud-native ETL and cloud-native ELT capabilities, so the decision is about pipeline design rather than a product limitation.

Capabilities that matter in a cloud-native ETL solution

Use these as evaluation criteria, not a checklist to maximize:

  • Connectors: coverage of your actual sources and targets, including SaaS apps and cloud warehouses.
  • Data quality and observability: automated profiling, cleansing, and monitoring so pipelines don't silently degrade.
  • Governance and catalog: lineage, metadata, and policy enforcement so you know where data came from and how it is used.
  • Master data management: a single trusted record for customers, products, or suppliers when conflicting versions exist across systems.
  • Developer access: APIs or headless delivery that let engineers work inside existing tools and agent frameworks.
  • Scale evidence: vendor-reported volume, such as Informatica's cited 132 trillion+ monthly cloud transactions, is a signal to validate against your own workload.

Practical steps to evaluate or adopt

  1. Inventory sources and targets. List systems, data volumes, and latency needs. This determines whether connector coverage or custom development dominates.
  2. Choose ETL or ELT per pipeline. Decide where transformation should run based on target compute and whether raw history must be retained.
  3. Pilot one real pipeline. Build an end-to-end flow with production-like volume and measure runtime, failure handling, and cost.
  4. Test governance early. Confirm lineage, cataloging, and quality monitoring work on the pilot, not just in a demo.
  5. Check operational fit. Ask who patches the platform, how scaling behaves under load, and what the contract's consumption terms are. Pricing details are not published on the vendor page, so confirm them directly.

A common pitfall is treating "cloud-native" as a checkbox: a tool hosted in the cloud but requiring manual server management delivers little of the elasticity the term implies. Verify the architecture during the pilot rather than from marketing language alone.