Rimo Lakehouse

Overview

How data is ingested

Data from the Rimo source system is ingested into the rimo_bronze_lh Lakehouse, following SPL's bronze-layer convention of preserving raw data with minimal intervention. As with all bronze Lakehouses, no transformations are applied during ingestion — ensuring that the data reflects the source system as closely as possible.

However, due to type incompatibilities between the source schema and Lakehouse constraints, certain columns may be stored using alternate data types. These adjustments are system-driven and occur by default to maintain ingestion continuity.

To ensure pipeline resilience, the orchestration process is designed to skip non-compatible rows rather than fail the entire ingestion job. This prevents a single malformed record from disrupting ingestion across multiple tables.

Whenever a row is skipped due to schema or type mismatches, the event is logged in a dedicated folder within the fabric-logs directory of the northlakepmo2 Azure Data Lake Storage account. These logs provide traceability for data quality issues and support downstream reconciliation and monitoring.

flowchart LR
    RIMO[(Rimo DWH
source system)] -->|Fabric Data Pipeline| LH[(rimo_bronze_lh
Bronze Lakehouse)]
    LH -->|consumed by| SIL[Silver Lakehouse]
    LH -.->|skipped rows| LOGS[(northlakepmo2
fabric-logs)]

    classDef src fill:#e3f2fd,stroke:#1976d2,stroke-width:1px
    classDef brz fill:#f4a261,stroke:#b85c00,stroke-width:1px,color:#000
    classDef sil fill:#c0c0c0,stroke:#555,stroke-width:1px
    classDef log fill:#fff3cd,stroke:#b85c00,stroke-width:1px

    class RIMO src
    class LH brz
    class SIL sil
    class LOGS log

Naming convention

When the rimo_bronze_lh Lakehouse was provisioned, Microsoft Fabric had not yet introduced support for custom schemas within Lakehouses. At that time, all tables were automatically assigned to the default dbo schema, regardless of their source schema in the Rimo Data Warehouse.

As a result, tables originating from Rimo's public, dim, and custom schemas were all ingested into the Lakehouse under the dbo schema. To preserve schema lineage and avoid ambiguity, a prefix-based naming convention was adopted.

For example:

• public_sdmsalesorder
• dim_sdmperson_historic
• custom_sdmperson_fte

This approach ensures that:

• Source-schema context is retained despite the schema flattening.
• Governance and reconciliation processes remain schema-aware.

Schema overview

The 94 tables in rimo_bronze_lh come from three origin schemas:

List of tables in rimo_bronze_lh

Operational notes

• No transformations: Tables in Bronze stay as close to source as Fabric allows. Cleansing happens in Silver.
• Resilience: Single non-compatible rows are skipped, not failed. The job keeps running.
• Traceability: Skipped rows are logged in northlakepmo2 → fabric-logs for downstream reconciliation.
• Access: Restricted to the Fabric team. Power-BI-Reports consume from Silver/Gold, not directly from Bronze.

Related pages

• Bronze Lakehouse (Hub)
• Rimo Daten fuer Power User
• Fabric Lakehouse Hub
• Microsoft Fabric Datenarchitektur im SPL
• Silver Lakehouse
• Gold Lakehouse

Verwandte Themen

Kuratiert — kann direkt im Editor ergänzt werden://

• Bronze Lakehouse
• Fabric Data Pipelines
• Rimo-DWH-Info

Automatisch vorgeschlagen über gemeinsame Tags (architektur, fabric, rimo): 

• BMD Bronze Lakehouse
• Board Bronze Lakehouse
• H3A Bronze Lakehouse
• SharePoint Bronze Lakehouse
• Bronze Lakehouse
• Microsoft-Fabric-Datenarchitektur im SPL

Status: Migriert — Team-Review ausstehend · Owner: (festlegen) · Letzter Review: 2026-06-11