# Data Warehouse Design for SyteLine ERP

> Source: https://netray.co/resources/syteline-data-warehouse-design-erp
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Knowledge Base

## Short answer

Design a data warehouse for Infor SyteLine ERP data. Build ETL pipelines, star schema models, and historical analytics from SL tables for manufacturing BI.

## Category

syteline

## Content

**Intro:** A dedicated data warehouse separates SyteLine's operational database from analytical workloads, enabling complex queries and historical trend analysis without impacting ERP performance. The data warehouse extracts data from SyteLine's SL-prefixed transaction tables, transforms it into a dimensional model optimized for analytics, and loads it into a separate database that BI tools like Power BI, Birst, and Tableau can query at full speed. For manufacturers with multi-site SyteLine deployments, the data warehouse also consolidates data across instances.

**Sections:**

#### ETL Pipeline Design for SyteLine Data

**Body:** The ETL (Extract, Transform, Load) pipeline moves data from SyteLine's OLTP schema into the warehouse's dimensional model. Extract jobs connect to SyteLine's SQL Server using change detection strategies: timestamp-based for tables with RecordDate columns (SLCos, SLItems), transaction log-based for high-volume tables (SLInvTrans, SLGlTrans), and full refresh for slowly changing reference tables. SSIS (SQL Server Integration Services) is the most common ETL tool for SyteLine environments.

**Bullets:**

- Design incremental extract using RecordDate columns on SLCos, SLItems, SLJobs for change-only data capture
- Implement CDC (Change Data Capture) on SLInvTrans and SLGlTrans for high-volume transaction tables
- Build SSIS packages with parameterized connection managers for multi-site SyteLine database extraction
- Transform SyteLine's normalized schema into denormalized fact and dimension tables during the load phase
- Schedule ETL execution: nightly full refresh for dimensions, hourly incremental for fact tables

#### Star Schema Model for Manufacturing Analytics

**Body:** The warehouse star schema centers on fact tables that capture business events: FactSalesOrder (from SLCos/SLCoitems), FactProductionJob (from SLJobs/SLJobRoutes), FactPurchaseOrder (from SLPoHdr/SLPoitems), FactInventoryTransaction (from SLInvTrans), and FactGeneralLedger (from SLGlTrans). Shared dimension tables (DimItem, DimCustomer, DimVendor, DimWarehouse, DimDate) provide consistent filter and group-by attributes across all fact tables.

**Bullets:**

- Design FactSalesOrder with grain at the order line level from SLCoitems: order_qty, ship_qty, unit_price, unit_cost
- Build FactProductionJob at the operation level from SLJobRoutes: planned_hours, actual_hours, setup_time, scrap_qty
- Create DimItem from SLItems with Type 2 slowly changing dimension for cost, product_code, and planner_code history
- Implement DimDate with fiscal calendar alignment from SLGlPeriods including manufacturing shift patterns
- Add DimWarehouse from SLWhse with site hierarchy for multi-site rollup reporting and geographic analysis

#### Historical Analytics and Multi-Site Consolidation

**Body:** The data warehouse enables analyses impossible in operational SyteLine: multi-year trend analysis, cross-site benchmarking, customer lifetime value, and predictive forecasting. Snapshot fact tables capture inventory positions and order backlog at regular intervals, enabling point-in-time analysis that SyteLine's transactional database overwrites. For multi-site deployments, the warehouse normalizes site-specific codes into enterprise-standard dimensions.

**Bullets:**

- Create daily inventory snapshot table capturing SLWhseitems.qty_on_hand, qty_alloc, and unit_cost per item per warehouse
- Build order backlog snapshot capturing open SLCos by customer, item, and promised date for delivery trend analysis
- Normalize multi-site item codes using a master cross-reference table for enterprise-wide item analytics
- Implement aggregate tables for common query patterns: monthly revenue by customer, quarterly production by work center
- Design data retention policy: full detail for 2 years, monthly aggregates for 5 years, annual summaries for 10+ years

**Cta:** Design a data warehouse that transforms your SyteLine data into strategic intelligence: Netray's architects build it right. Talk to us.

## Related Slugs

- syteline-power-bi-integration-guide
- syteline-birst-integration-analytics
- syteline-kpi-manufacturing-metrics

## Search terms

SyteLine data warehouse design, Infor SyteLine ETL pipeline, CloudSuite Industrial data warehouse, SyteLine star schema manufacturing, SyteLine SSIS data warehouse

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