ETL Pipeline Design for SyteLine Data
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.
- 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
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.
- 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
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.
- 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
Try it here, free
SyteLine Database Tables Reference
Searchable lookup of the main SyteLine SQL tables, what each holds and its usual business key, for writing queries and reports.
Design a data warehouse that transforms your SyteLine data into strategic intelligence: Netray's architects build it right. Talk to us.