What Is ETL (Extract, Transform, Load)?
Also known as: extract transform load, ELT, data pipeline, ERP data extraction
Definition
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.
ETL (Extract, Transform, Load) Explained
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.
Frequently Asked Questions
What is the difference between ETL and ELT?
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?
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?
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?
Timestamp-based loads only see rows that exist. Deletes need change data capture, soft-delete flags or periodic key reconciliation.
Related Terms
Data Mapping
Data mapping is the process of defining how each field in a source system corresponds to a field in a target system, including the transformation rules, value translations, default values, and conditional logic needed to move data correctly between them.
Middleware
Middleware is software that sits between applications and handles the communication, transformation, routing, and reliability concerns of moving data between them, so that the applications themselves do not need to know about each other directly.
iPaaS
iPaaS (Integration Platform as a Service) is a cloud-hosted platform that provides prebuilt connectors, data mapping, orchestration, and monitoring so organizations can build and operate integrations between applications without running their own integration infrastructure.
API (Application Programming Interface)
An API (Application Programming Interface) is a defined contract that lets one software system request data or trigger functions in another, specifying the available operations, the structure of requests and responses, and the rules for authentication and error handling.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an architecture that searches your own document store for passages relevant to a question, inserts them into the LLM prompt, and instructs the model to answer only from that retrieved evidence.
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Free tools
- ETL Pipeline Modernization CalculatorEstimate the cost to rebuild legacy ETL jobs into a modern orchestrated pipeline, and the payback period from reduced maintenance and failure time.
- SyteLine Incremental Extract Query GeneratorWrites the watermark table, delta SELECT, watermark update, delete detection and MERGE for loading one SyteLine table into a staging database with SSIS or any ETL tool.
- ERP Data Extract Interface Specification TemplateA one-page spec for any scheduled ERP extract: source, file layout, schedule, error handling, monitoring and owners.
ERP fixes and how-tos
- ERP Integration Architecture: API vs iPaaS vs ETLHow a CTO should choose between direct ERP APIs, an iPaaS and ETL: a decision rule by data direction, latency and volume, plus the failure modes of each.
- Infor ION Scripting and ETL: Transformation Options ExplainedInfor ION scripting and ETL explained: mapping and transformation in document flows, where scripting fits, and when to use Data Lake flows or another ETL tool.
- IFS Cloud: How to Extract Data Directly From a TableWant to pull IFS Cloud data straight from a table? See what direct access means, why views and projections are safer, and the supported extraction options.
Guides
- Data Warehouse Design for SyteLine ERPDesign a data warehouse for Infor SyteLine ERP data. Build ETL pipelines, star schema models, and historical analytics from SL tables for manufacturing BI.
- ETL Tools for ERP Data MigrationCompare ETL tools for ERP migration including Informatica, Talend, SSIS, and Apache NiFi. Select the right tool based on complexity, budget, and scale.
- Infor LN Data Migration: ETL Strategies and Best PracticesPlan and execute Infor LN data migrations with ETL best practices. Data Migration Framework, staging tables, validation rules, and cutover strategies for LN projects.