---
title: Extracting and transforming ERP data with Zero Copy Connector for ERP extraction tables
description: Use ERP extraction tables in Zero Copy Connector for ERP to schedule processes that extract large volumes of ERP data into local tables on the ServiceNow AI Platform. Then the data is transformed with table maps and can be used in ERP models and applications.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/integrate-applications/erp-integration-framework/erp-canvas-extraction-tables.html
release: brazil
product: ERP Integration Framework
classification: erp-integration-framework
topic_type: concept
last_updated: "2026-10-22"
reading_time_minutes: 2
keywords: [extraction table, zero copy connector for erp, erp extraction table, erp data extraction, standard extraction tables, transform table map, erp etl, import set, scheduled data extraction]
breadcrumb: [Configuring, Zero Copy Connector for ERP, Workflow Data Fabric]
---

# Extracting and transforming ERP data with Zero Copy Connector for ERP extraction tables

Use ERP extraction tables in Zero Copy Connector for ERP to schedule processes that extract large volumes of ERP data into local tables on the ServiceNow AI Platform. Then the data is transformed with table maps and can be used in ERP models and applications.

## Data in extraction tables

Use extraction tables, which are ETL \(extract, transform, and load\) data sources, to extract large amounts of data regularly.

For example, you can extract all open purchase orders for the current month from an SAP system into a Glide table once a day at a set time. You can then access that data through ServiceNow AI Platform, such as Source-to-Pay Operations or a PO workspace created in App Engine Studio.

After the extraction process is run, use import sets to map imported data into ServiceNow AI Platform tables. For more information, see [Import sets](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/integrate-applications/system-import-sets/import-sets-landing-page.md).

## Extraction tables use transform table maps

ERP extraction tables save data to a local transform table on the ServiceNow AI Platform. The transform table is a temporary table that holds data during the data integration or transformation process. Transform tables are an intermediary step before data is further processed, cleaned, or loaded into the target destination. For example, after import, the extraction table can run a Glide query on a transform table and save the output to several different target tables.

If you use a custom transform table, you must first create the table on the ServiceNow AI Platform, and the table must be in the application scope. For more information on creating table transform maps, see [Create a transform map](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/integrate-applications/system-import-sets/t_CreateATransformMap.md).

ETL processes in Zero Copy Connector for ERP \(Enterprise Resource Planning\) are configured in the ServiceNow AI Platform app that needs the data, not Zero Copy Connector for ERP. For example, you can configure an extraction process in a flow in Workflow Studio.

## Automatically available standard extraction tables

When you install Zero Copy Connector for ERP, the ServiceNow AI Platform automatically loads a standard set of ERP extraction tables, including sales, delivery, and procurement tables. For a list of standard extraction tables, see [Standard extraction tables for Zero Copy Connector for ERP](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/integrate-applications/erp-integration-framework/erp-canvas-standard-extraction-tables.md).

If you installed ERP Semantic Mining, that app populates some additional extraction tables, such as ERP application activity, Collector directory data, and Namespace data.

**Important:** Starting with the Zurich release, ERP Semantic Mining is being prepared for future deprecation. It will be hidden and no longer activated on new instances but will continue to be supported.