ETL, ELT, Data Integration, Orchestration, iPaaS, Reverse ETL: What Each Term Means

Vendors describe the same product as an ETL tool, a data integration platform, an orchestrator and an iPaaS, sometimes on the same page. The terms do mean different things. This post defines each one in a sentence, explains how they overlap, and ends with a short guide to which you need.

The definitions

ETL (extract, transform, load) is the process of reading data from source systems, reshaping it, and writing it into a destination built for analysis. It names the work, not a product. See what is ETL.

ELT (extract, load, transform) is ETL with the transformation done inside the destination after loading, usually in SQL on a cloud warehouse. Raw data lands first.

Data pipeline is an implementation of ETL or ELT for one purpose: a sequence of steps with a schedule, dependencies, error handling and monitoring.

Data integration is the goal of making data from different systems usable together. ETL is one technique for it; others are replication, change data capture, APIs, message streams and virtualization. A "data integration platform" is a product that offers several of these.

Data orchestration is deciding what runs, in what order, when, and what happens on failure. An orchestrator can schedule ETL jobs, scripts, reports, model training and API calls. Some orchestrators also move data; many only run code that does. See what is data pipeline orchestration.

Change data capture (CDC) is a technique for reading changes from a database's own log so that inserts, updates and deletes are streamed as they happen, rather than discovered by querying.

Replication is copying data from one system to another with minimal transformation, often continuously, to keep a copy in sync. CDC is the usual mechanism.

Reverse ETL is moving data from the warehouse back into operational tools such as a CRM or a support desk, so that a segment computed in analytics appears where the sales team works. The direction is reversed; the mechanics are the same.

iPaaS (integration platform as a service) is a hosted service for connecting applications to each other, typically event-driven and record-at-a-time: when a form is submitted, create a ticket. It is built for application workflows, not for moving tables of data.

Data virtualization presents data from several systems through one query interface without copying it. Useful for exploration; expensive and fragile for heavy reporting.

Data catalog is an inventory of the datasets an organization has, with their meaning, ownership, lineage and access rules. It does not move data; it records what the data is.

Lakehouse is a storage architecture that keeps data in open table formats on object storage with warehouse-style management on top: transactions, schema enforcement, governed access.

How they fit together

Think of it as layers:

A single product can span layers. Revoluzen Flow is both the technique layer (it moves data through its connectors) and the control layer (it schedules, orders, retries and records runs). Revoluzen Catalog is the storage and knowledge layers. An iPaaS sits beside all of this rather than inside it.

Which one do you need?

"We need last night's orders in the warehouse every morning." ETL or ELT, run by an orchestrator. One tool that does both is simplest.

"The warehouse must track the database within minutes, including deletes." CDC, feeding replication into the warehouse.

"Sales wants the churn score from analytics inside the CRM." Reverse ETL.

"When a customer signs up, create them in billing and support." iPaaS or application workflow automation, not a data pipeline.

"We have thirty pipelines and nobody knows which ones are broken." Orchestration with monitoring, and a reliability layer that raises findings rather than leaving you to read logs.

"An auditor asked which inputs produced a figure." None of the above by itself. You need a run record with inputs, outputs, code version and result, ideally signed so it can be verified without you. See Revoluzen Evidence.

Frequently asked questions

Is an orchestrator the same as a scheduler?

A scheduler starts jobs at times. An orchestrator also manages dependencies between jobs, retries, resuming failed runs, and the record of what ran. Every orchestrator contains a scheduler; the reverse is not true.

Is iPaaS a kind of ETL?

No. iPaaS connects applications event by event; ETL moves and transforms sets of data on a schedule or stream. Using an iPaaS to move tables works for small volumes and becomes expensive and slow quickly.

Do I need a data catalog if I have an ETL tool?

Not at the start. A catalog becomes necessary when more than one team consumes the data and questions about meaning, ownership and lineage outnumber questions about the pipelines themselves.

What is the difference between replication and ETL?

Replication copies data with little or no transformation to keep systems in sync. ETL reshapes data for a different purpose, usually analysis. Many pipelines replicate first and transform afterwards.

Can one tool cover data integration and orchestration?

Yes. Tools that ship connectors and also schedule, order and monitor runs cover both; tools that only orchestrate code need a separate movement layer. Revoluzen Flow covers both in one self-hosted process.