# Data Warehouse (ERP Reporting)

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

## Short answer

A data warehouse is a separate database that copies data from ERP and other systems into a modeled structure, with history, built for reporting and analytics. It keeps heavy queries off the transactional ERP and lets you combine sources.

## Aliases

- ERP data warehouse
- enterprise data warehouse
- EDW
- reporting database

## Category

integration

## Full Definition

ERP databases are designed for transactions: many small inserts and updates, normalized into hundreds of tables. Analytical questions need the opposite, scanning large history and joining across subject areas. A data warehouse reshapes ERP data into fact tables (shipments, inventory movements, production completions) and dimension tables (item, customer, work center, date), often in a star schema.

Data arrives through ETL or ELT pipelines on a schedule or near real time through change data capture. Transformation work includes mapping codes, resolving keys across systems, handling units of measure and currency, and storing history so you can report what was true on a past date, for example slowly changing dimensions for item cost or customer territory.

ERP specifics matter. SyteLine data sits in SQL Server with IDO-based logic and custom user-defined fields; Infor LN and Baan use their own table and session structure with company numbers; NetSuite exposes data through SuiteAnalytics Connect or saved searches. Extracting cleanly means respecting those structures, and heavy direct queries against the live database can slow transaction users.

You do not always need one. If reports come from one ERP, volume is modest and the vendor's embedded analytics or a read replica serves the questions, a warehouse is extra cost. It earns its place when you combine ERP with MES, quality, CRM or sensor data, need long history, or serve many dashboards and AI assistants that should not touch production.

## Why It Matters

- It protects ERP performance by moving analytical load to a separate store.
- It joins ERP, MES, quality and other data into one consistent view of KPIs such as OEE and OTIF.
- Stored history supports trend analysis that live ERP tables overwrite.
- A governed, modeled layer is the safest foundation for natural-language and AI analytics.

## In Practice

A plant's SyteLine reports slow down every month-end because finance runs large cost queries. The team replicates job, item and ledger tables nightly into a SQL Server warehouse, builds a shipments fact table and item and customer dimensions, and points BI dashboards there. Month-end queries no longer compete with shop floor transactions, and they can now add MES downtime data to the same model.

## Faq

### Do I need a data warehouse for ERP reporting?

**Answer:** Not always. For a single ERP with modest volume, embedded reporting or a read replica may be enough. A warehouse helps when you combine multiple sources, need history, or run heavy analytics.

### What is the difference between a data warehouse and a data lake?

**Answer:** A warehouse stores modeled, structured data for reporting. A data lake stores raw data of any format cheaply and applies structure later. Many organizations use both.

### Can a data warehouse run on-premises?

**Answer:** Yes. A warehouse can run on your own SQL Server or open-source database, which suits manufacturers who must keep ERP data inside their own network, including for private AI use.

## Search terms

ERP data warehouse, data warehouse vs data lake, star schema ERP, ETL for ERP, ERP reporting database, SyteLine data warehouse, manufacturing data warehouse, ERP analytics architecture

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