Lima Backbone reads Oracle NetSuite through suiteAnalytics Connect, types the data correctly on landing, writes it into Microsoft Fabric as lakehouse delta tables in onelake (raw, conformed, history), v-ordered for direct lake, and keeps it current from the source’s changes. Every run ends with a row count and a control total per table.
Sizing loads against Fabric capacity so a large landing does not slow reporting.
The first load is assembled from these objects through suiteAnalytics Connect. Change capture (by last-modified date) keeps the daily volume small afterwards.
| Planning object | Source objects | Destination in Microsoft Fabric |
|---|---|---|
| Items | Item records | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Bills of material | Assembly item members · BOM revisions | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Routings | Manufacturing routings | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Stock | Inventory balance by location | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Open purchase orders | Purchase order lines | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Open sales orders | Sales order lines | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Work orders | Work order records | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Forecasts | Demand plan records | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Customers and suppliers | Customer and vendor records | Lakehouse raw Delta → conformed Delta, V-Ordered |
| Calendars | Location calendars | Lakehouse raw Delta → conformed Delta, V-Ordered |
Every check either passes a row or turns it into an exception record with an owner. Nothing is dropped silently.
| Check | Kind | What it catches, with an example |
|---|---|---|
| Types set from the source | typing | SAP dates and times stored as text become dates and times; currency decimals shifted by the currency's rule |
| Keys complete and unique | keys | client or organisation columns kept in the key; zero-padded keys preserved |
| Deletes applied | consistency | rows deleted in the source are marked or removed in the copy |
| Late changes land in order | consistency | a change that arrives after a later one is applied by its source timestamp |
| Wide and cluster tables complete | completeness | every column of a wide table lands; cluster tables read through the released module |
| Partitions cover the range | completeness | every date partition present; gaps reported |
| Counts and control totals match | reconciliation | rows and amounts per table compared between source and target |
| Schema changes noticed | drift | a new or changed column is flagged before it breaks a downstream query |
After the first landing, Microsoft Fabric receives the source's changes: by last-modified date. A migration month costs the same as a quiet month on the same tier.
The tables you name, landed in Microsoft Fabric with types set, keys complete and a count and control total per table, then kept current. Scoped in one call.
Offers and principles →Two weeks on your largest tables: terabytes moved, rows per minute, reconciliation variance and cost per terabyte against your current tool.
The offer →Days for the first tables; a full-history migration is sized by table count and volume, and read in partitions inside the run windows your administrators allow.
Counts per step, the check that stopped a row, the owner and the replay point, and the lineage of any number back to its source column.
Your team, on the schedule you set, with the same report after every run; or your systems integrator; or Lima's team under the subscription.
One call scopes the dataset, the run window and the tier. The reconciliation report at the end is the acceptance test.
