# ETL (Extract, Transform, Load)

> Source: https://netray.co/glossary/etl
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Glossary

## Short answer

ETL (extract, transform, load) is the process of copying data out of source systems such as an ERP, reshaping and cleaning it, and loading it into a data warehouse, lake or other target for reporting, analytics and AI.

## Aliases

- extract transform load
- ELT
- data pipeline
- ERP data extraction

## Category

integration

## Full Definition

Extract pulls data from the source. For an ERP that can mean reading database tables or views, calling an API or OData service, or consuming change events. Transform converts the raw data into a usable shape: joining tables, converting units and currencies, deduplicating, applying business rules and masking sensitive fields. Load writes the result into the target, such as a warehouse, lake or search index.

ELT reverses the last two steps. Raw data is loaded first into a warehouse and transformed there with SQL. This is common with cloud warehouses where compute is cheap and raw history is worth keeping. ETL still fits when data must be cleansed or filtered before it leaves a secure network, which matters for regulated or air-gapped environments.

Incremental loading is the main design question. A full copy of a large ERP every night is slow and heavy. Better options are change data capture from the database log, a last-modified timestamp column, or event-driven loads. Each has traps: not every ERP table has reliable timestamps, deletes are invisible to timestamp loads, and some ERPs update rows in place without history.

ERP specifics matter. SyteLine data sits in SQL Server with site-specific tables and IDO-based business logic. Infor LN tables are organized by package and company number. NetSuite limits direct database access and favors SuiteQL, saved searches or a connector. A good pipeline respects these quirks and does not assume a clean star schema exists.

## Why It Matters

- Reporting and AI against the live ERP can slow transactions, so many teams replicate data out first.
- Transform rules encode business definitions such as what counts as on-time delivery, so they need ownership and documentation.
- Incremental design decides whether the pipeline finishes in minutes or hours and whether deletes are captured.
- A governed data copy makes it easier to apply access control and masking before data reaches analytics or an LLM.

## In Practice

A manufacturer loads sales orders, job orders and inventory transactions nightly to a warehouse. The first design copies whole tables and takes six hours. Switching to a last-updated watermark on the large tables cuts the run to under an hour, but cancelled order lines disappear from the ERP and stay in the warehouse. The team adds a weekly reconciliation of row counts and keys to catch these deletes.

## Faq

### What is the difference between ETL and ELT?

**Answer:** In ETL data is transformed before loading. In ELT raw data is loaded first and transformed inside the target, usually a cloud warehouse.

### How often should ERP data be loaded?

**Answer:** It depends on the use. Finance reporting is often daily, shop floor and planning dashboards may need minutes, and AI assistants often need near real-time reads for a few tables.

### Can I run ETL on-premises?

**Answer:** Yes. Many open-source and commercial tools run entirely inside your network, which suits regulated and air-gapped environments.

### Why do deleted records go missing in ETL?

**Answer:** Timestamp-based loads only see rows that exist. Deletes need change data capture, soft-delete flags or periodic key reconciliation.

## Search terms

ETL, what is ETL, ETL vs ELT, ERP data extraction, ERP to data warehouse, change data capture ERP, ETL tools manufacturing, ETL pipeline example

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