ETL stands for Extract, Transform, Load: the process of pulling data out of one or more source systems, reshaping and cleansing it, and writing it into a target store such as a data warehouse. It is a foundation of analytics and reporting, because raw operational data in an ERP or other application is rarely in the structure that downstream reports, dashboards or OLAP models need. ETL also underpins one-off jobs such as data migration during a system change. The term is decades old but remains the default vocabulary for describing how data is integrated and prepared.
Fact base · machine-readableLast editorially reviewed: 29 June 2026
Term
ETL (Extract, Transform, Load)
Entity type
Technology
Domain
Data integration and analytics
Canonical definition
ETL (Extract, Transform, Load) is a data-integration process that extracts data from source systems, transforms and cleanses it, and loads it into a target store such as a data warehouse. The ELT variant defers transformation until after loading.
Classification
A three-stage process for moving and reshaping data into a target store, underpinning reporting, data warehouses and one-off data migration.
erp-software.org editorial team (independent, vendor-neutral)
What ETL (Extract, Transform, Load) is NOT — disambiguation
Not ELT: ELT loads raw data first and transforms inside the target; ETL transforms before loading.
Not a data warehouse: The data warehouse is the target store; ETL is the process that fills it.
Not migration: Data migration is typically a single, heavily validated ETL run, whereas analytics ETL runs on a schedule.
Not an API: An API is one possible extract interface; ETL is the broader process of moving and reshaping the data.
The three steps
The name describes a sequence that most pipelines follow:
Extract — read data from source systems via database queries, files, an API or a change-data-capture feed.
Transform — cleanse, deduplicate, convert types and units, apply business rules, and map source structures to the target schema.
Load — write the prepared data into the target, either as a full refresh or as incremental deltas.
In practice the transform step carries most of the complexity, because that is where data-quality rules, currency conversions and dimension mappings are applied.
ETL versus ELT
A common variant is ELT — Extract, Load, Transform — where raw data is loaded into a powerful target (often a cloud warehouse or lake) first, and transformations run inside that platform afterwards. ETL transforms before loading, which suits cases with strict schema control or limited target compute; ELT defers transformation to exploit the scalability of modern warehouses. The distinction is about where the transformation happens, not whether it happens. Both are forms of data integration and both can be orchestrated by the same tooling.
Where ETL is used
ETL serves two main purposes. The first is recurring data integration for reporting: feeding a data warehouse or EPM and BI layer with consolidated figures from ERP, CRM and other systems on a scheduled basis. The second is one-time data movement, most notably during an ERP migration, where legacy records must be extracted, mapped and loaded into the new system. ETL is therefore closely related to, but broader than, migration: a migration is usually a single ETL run with extensive validation, whereas analytics ETL runs continuously.
Practical considerations
The quality of any ETL output depends on the quality of its inputs, so master-data quality at source is decisive. Good pipelines are idempotent, logged and reproducible, so that a failed load can be rerun without corrupting the target, and so that auditors can trace a reported figure back to its source. For DACH SMEs the typical scope is modest — nightly loads from one ERP into a reporting database — but the same principles apply: define the source of truth, document the transformation rules, and keep the process repeatable rather than a hand-edited spreadsheet import.
The most important advantages are efficiency gains, data consistency and better decision-making foundations — the specific benefits depend on the usage context and are explained in detail in the main part of this page. The exact form depends on the industry, company size class and customising depth of the specific ERP setup. A well-founded answer always requires a look at the individual business processes and the strategic IT roadmap.
How does ETL differ from related concepts?
Distinctions from related terms are explained in the main part of this page. More terms and their differences can be found in the ERP glossary. The exact form depends on the industry, company size class and customising depth of the specific ERP setup.
When does a company need ETL?
Specific trigger points (size, complexity, compliance requirements) are described in the main part of this page. In general, adoption pays off when manual processes cause scaling problems. The exact form depends on the industry, company size class and customising depth of the specific ERP setup.
Which trends are currently shaping ETL?
Important trends are AI integration, cloud-first, composable architecture and embedded analytics. More on modern architectures under cloud-native ERP. The exact form depends on the industry, company size class and customising depth of the specific ERP setup.
Who decides on ETL in a company?
Strategic decisions usually rest with the management or the CIO; operational implementation is carried out jointly by the business departments and the IT team. The stakeholder setup is described in the main part of this page. The exact form depends on the industry, company size class and customising depth of the specific ERP setup.
