---
title: Fabric Lakehouse - Load data - Native Load Control
description: Load the deployed Fabric Lakehouse solution with the native load control by running the notebook <project>_MultithreadingLoadExecution.
---

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3. [Load data with a native load control](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-a-native-load-control)

# Fabric Lakehouse - Load data - Native Load Control

**Applies to**  
Platform: Fabric Lakehouse

Load the deployed Fabric Lakehouse solution with the native load control by running the notebook `<project>_MultithreadingLoadExecution`. The notebook runs every loader notebook in dependency order, four at a time, and writes each step to the execution log. Afterwards every loader has run once with the same load timestamp.

How the load control plans and runs loads is explained in [Understand Load Control](https://knowledge.bigenius-x.com/understand-load-control?hsLang=en). The procedure applies to all Fabric Lakehouse generator configurations with Target Platform `Fabric`; for `Azure Synapse`, see [Azure Synapse - Load data - Native Load Control](https://knowledge.bigenius-x.com/azure-synapse-load-data-native-load-control-2.0?hsLang=en).

### Prerequisites

- [LoadControl Options](https://knowledge.bigenius-x.com/loadcontrol-options-project?hsLang=en) contains `Multi-Threading Notebook`, and [Deploy LoadControl](https://knowledge.bigenius-x.com/deploy-loadcontrol-project?hsLang=en) is `true`. Otherwise the artifacts contain no load notebook.
- The artifacts are [deployed](https://knowledge.bigenius-x.com/fabric-lakehouse-deployment-2.0?hsLang=en) without errors, including the execution log. `deploy.ps1` has set the default lakehouse of the notebooks.

### Load the data

1. In Fabric, select **Workspaces** and open the workspace of the deployment.  
   ![Workspace list with the workspace bgDocumentation](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Mar-25-2026-09-30-19-4799-AM.png?width=266&height=176&name=image-png-Mar-25-2026-09-30-19-4799-AM.png)
2. Open the notebook `<project>_MultithreadingLoadExecution` in the folder of the deployment.
3. Run all cells of the notebook.
4. Wait until the last cell ends. It prints one line per step, for example `<time> - <loader>: finished task`, and ends with `tasks completed successfully`.

The notebook takes the load timestamp when it starts and passes it to every loader notebook. The variable `threads_amount = 4` in its second cell sets how many loaders run in parallel. Each loader notebook may run for `run_notebook_timeout` seconds, 3600 by default.

Give each project its own load control lakehouse or schema (placeholders `loadcontrol#loadcontrol#database_name` and `schema_name`). With the default Deployment Options, a project that deploys the load control into the same location replaces the logs of the other projects.

### Check the result

The load succeeded when the notebook ends with `tasks completed successfully`. To count the rows, run the notebook `Helpers\<project>_SelectResults`. It lists each result view with its row count and newest load timestamp:

![Output of SelectResults with the columns table\_name, row\_count and bg\_loadtimestamp for three Stage result views](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Mar-25-2026-10-23-18-6707-AM.png?width=670&height=126&name=image-png-Mar-25-2026-10-23-18-6707-AM.png)

To see the steps of the last load, run this query in a notebook, with the lakehouse and schema of the load control:

```
SELECT log_timestamp, log_level, task_name, message
FROM <lakehouse>.<schema>.execution_log
WHERE load_timestamp =
      (SELECT MAX(load_timestamp)
       FROM <lakehouse>.<schema>.execution_log)
  AND (log_level = 'ERROR' OR task_name = 'main'
       OR message IN ('finished task', 'task failed'))
ORDER BY log_timestamp;
```

### Example

A DataVault and Mart project loads the Stage `STG_ST_ITEM`, the Hub `RDV_HUB_ITEM` and the Satellite `RDV_SAT_ITEM` from a source table with three rows. The first load ran in under five minutes and wrote 20 rows with the log level `INFO`. The query of [Check the result](https://knowledge.bigenius-x.com/fabric-lakehouse-load-data-native-load-control-2.0#check-the-result) returned:

```
task_name            message
STG_ST_ITEM_Loader   finished task
RDV_HUB_ITEM_Loader  finished task
RDV_SAT_ITEM_Loader  finished task
main                 tasks completed successfully
```

Afterwards the tables held these rows. The Hub and the Satellite each contain one more row than the source: the unknown-member row, which the deployment inserts.

```
table                 rows
stg_st_item           3
rdv_hub_item          4
rdv_sat_item          4
rdv_sat_item_result   3
```

### Load the data partially

You can generate, deploy and load the model objects of one [Dataflow Modeling View](https://knowledge.bigenius-x.com/manage-modeling-views?hsLang=en):

1. Generate the artifacts for the view instead of the whole project, see [Generate artifacts](https://knowledge.bigenius-x.com/generate-artifacts?hsLang=en).
2. Deploy the artifacts of the view, see [Fabric Lakehouse - Deployment](https://knowledge.bigenius-x.com/fabric-lakehouse-deployment-2.0?hsLang=en).
3. Run the load notebook of the view as described in [Load the data](https://knowledge.bigenius-x.com/fabric-lakehouse-load-data-native-load-control-2.0#load-the-data).

### Load all the data from partial deployments

When the model objects of a project are organized in several Dataflow Modeling Views, a Data Factory pipeline can run the load notebooks of all views. Organize the views as follows:

- A model object belongs to only one view.
- A model object that must be loaded after another one is in the same view or in a view that is loaded later.

Example: the views CreditCard, Customer and Order. Order contains model objects that depend on model objects of the other two views:

![CreditCard and Customer both flow into Order](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-01-2025-09-30-06-0649-AM.png?width=290&height=153&name=image-png-Dec-01-2025-09-30-06-0649-AM.png)

Generate and deploy each view as described in [Load the data partially](https://knowledge.bigenius-x.com/fabric-lakehouse-load-data-native-load-control-2.0#load-the-data-partially). Then create the pipeline:

1. In the workspace, select **New item** and search for **Pipeline**.  
   ![New item dialog with the item type Pipeline](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-09-49-04-4951-AM.png?width=520&height=189&name=image-png-Dec-02-2025-09-49-04-4951-AM.png)
2. Enter a name, for example `pipeline_load_all`, and select **Create**. Then start with a blank canvas.  
   ![Build a pipeline page with Start with a blank canvas](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-09-50-32-9041-AM.png?width=539&height=215&name=image-png-Dec-02-2025-09-50-32-9041-AM.png)
3. On the **Activities** tab, select **Notebook**. On its **General** tab, enter a name such as `Load_View_CreditCard`. On its **Settings** tab, select the load notebook of the view.  
   ![Activities tab with the Notebook activity](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-09-55-26-7717-AM.png?width=640&height=83&name=image-png-Dec-02-2025-09-55-26-7717-AM.png)
4. Add one notebook activity per view in the same way.
5. Drag the **On success** handle of each activity to the activity that depends on it.  
   ![Pipeline in which Load\_View\_CreditCard and Load\_View\_Customer both lead to Load\_View\_Order](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-10-34-44-6995-AM.png?width=504&height=239&name=image-png-Dec-02-2025-10-34-44-6995-AM.png)
6. Select **Run**. The activities start in the order of their connections.  
   ![Pipeline run with both first activities succeeded and Load\_View\_Order in progress](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-10-30-51-4812-AM.png?width=640&height=260&name=image-png-Dec-02-2025-10-30-51-4812-AM.png)

### Failure symptoms and recovery

#### Some loaders failed

Message: `<number> tasks failed during execution. Please check the logs for more details.`  
Cause: A loader notebook failed. It wrote an `ERROR` row with the error to the execution log, and the notebook skipped all loaders that depend on it; the other loaders ran.  
Fix: Read the `ERROR` rows with the query of [Check the result](https://knowledge.bigenius-x.com/fabric-lakehouse-load-data-native-load-control-2.0#check-the-result). Correct the cause and run the notebook again; it runs all loaders with a new load timestamp.

#### The notebook cannot write the execution log

Message: `[REQUIRES_SINGLE_PART_NAMESPACE] spark_catalog requires a single-part namespace, but got <lakehouse>.<schema>`  
Cause: The execution log is in a lakehouse with schemas, but the default lakehouse of the notebook has no schemas.  
Fix: Make the lakehouse with schemas the default lakehouse of the load notebooks, see [Fabric Lakehouse - Deployment](https://knowledge.bigenius-x.com/fabric-lakehouse-deployment-2.0?hsLang=en#failure-symptoms-and-recovery).

For failures in other steps, see [Troubleshoot generation, deployment and load failures](https://knowledge.bigenius-x.com/troubleshoot-generation-deployment-and-load-failures?hsLang=en).

### Release differences

#### Legacy sequential notebook

Generator 1.8 also offered the LoadControl Options value `Sequential Notebook`, which generated `LoadControl\<project>_SimpleLoadExecution.ipynb`. Since Generator 1.9, `Multi-Threading Notebook` is the native load notebook. Up to Generator 2.0, add the lakehouse in the notebook before you run it, see [Fabric Lakehouse - Deployment](https://knowledge.bigenius-x.com/fabric-lakehouse-deployment-2.0?hsLang=en#release-differences).

### Related articles

- Previous: [Fabric Lakehouse - Deployment](https://knowledge.bigenius-x.com/fabric-lakehouse-deployment-2.0?hsLang=en)
- Reference: [LoadControl Options (Project)](https://knowledge.bigenius-x.com/loadcontrol-options-project?hsLang=en), [Deploy LoadControl (Project)](https://knowledge.bigenius-x.com/deploy-loadcontrol-project?hsLang=en), [Fabric Lakehouse - Placeholder values](https://knowledge.bigenius-x.com/fabric-lakehouse-placeholder-values-2.0?hsLang=en)
- See also: [Understand Load Control](https://knowledge.bigenius-x.com/understand-load-control?hsLang=en), [Fabric Lakehouse - Load data - Apache Airflow Load Control](https://knowledge.bigenius-x.com/fabric-lakehouse-load-data-apache-airflow-load-control?hsLang=en), [Fabric Lakehouse - Artifacts](https://knowledge.bigenius-x.com/fabric-lakehouse-artifacts-2.0?hsLang=en), [Manage Dataflow Modeling Views](https://knowledge.bigenius-x.com/manage-modeling-views?hsLang=en)

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