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

[Skip to content](https://knowledge.bigenius-x.com/azure-synapse-load-data-native-load-control-2.0#main-content)

[Contact us](https://knowledge.bigenius-x.com/kb-tickets/new?hsLang=en) [Go to Customer Portal](https://knowledge.bigenius-x.com/customer-portal?hsLang=en)

[![TRI\_Logo\_biGENIUS\_2020\_RGB\_s](https://knowledge.bigenius-x.com/hs-fs/hubfs/Marketing/TRI_Logo_biGENIUS_2020_RGB_s.png?width=150&height=42&name=TRI_Logo_biGENIUS_2020_RGB_s.png)](https://www.bigenius-x.com/)

Open main navigation

Close main navigation

- [Contact us](https://knowledge.bigenius-x.com/kb-tickets/new)
- [Go to Customer Portal](https://knowledge.bigenius-x.com/customer-portal)

 How can we help you?

- There are no suggestions because the search field is empty.

1. [biGENIUS-X Knowledge Base](https://knowledge.bigenius-x.com/?hsLang=en)
2. [Generators](https://knowledge.bigenius-x.com/generators?hsLang=en)
3. [Load data with a native load control](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-a-native-load-control)

# Azure Synapse - Load data - Native Load Control

**Applies to**  
Platform: Fabric Lakehouse

Load a deployed Fabric Lakehouse solution in Azure Synapse 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 Target Platform `Azure Synapse`; for `Fabric`, see [Fabric Lakehouse - Load data - Native Load Control](https://knowledge.bigenius-x.com/fabric-lakehouse-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`.
- The artifacts are [deployed](https://knowledge.bigenius-x.com/azure-synapse-deployment-2.0?hsLang=en) without errors, including the load control.

### Load the data

1. Open Synapse Studio with the **Workspace web URL** of the Synapse workspace and select **Develop**.  
   ![Develop in the menu of Synapse Studio](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-18-2023-09-09-41-1227-AM.png?width=156&height=56&name=image-png-Oct-18-2023-09-09-41-1227-AM.png)
2. Open the notebook `<project>_MultithreadingLoadExecution`.
3. Attach the notebook to `<Spark pool>`.  
   ![Notebook toolbar with the Attach to list for the Spark pool](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-18-2023-02-05-34-4798-PM.png?width=640&height=62&name=image-png-Oct-18-2023-02-05-34-4798-PM.png)
4. Select **Run all** and wait until the last cell ends. It prints one line per step 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.

Give each project its own load control database (placeholder `loadcontrol#loadcontrol#database_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 `<project>_SelectResults`. It lists each result view with its row count and newest load timestamp:

![Output of SelectResults with the row count and load timestamp of each result view](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Sep-05-2024-04-21-03-2479-PM.png?width=670&height=277&name=image-png-Sep-05-2024-04-21-03-2479-PM.png)

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

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

### Example

To read a result view of the Raw Vault, add a cell to a notebook with the database `rawvault` and a Hub named `creditcard_hub`:

```
mydf = spark.sql(
  "select * from `rawvault`.`rdv_hub_creditcard_hub_result`")
mydf.show(truncate = False)
```

The result view `rdv_hub_<hub name>_result` shows the loaded rows of the Hub `rdv_hub_<hub name>`.

### 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 [Azure Synapse - Deployment](https://knowledge.bigenius-x.com/azure-synapse-deployment-2.0?hsLang=en).
3. Run the load notebook of the view as described in [Load the data](https://knowledge.bigenius-x.com/azure-synapse-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 Synapse 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/azure-synapse-load-data-native-load-control-2.0#load-the-data-partially). Then create the pipeline in your working branch:

1. In Synapse Studio, select **Integrate**, then the plus icon and **Pipeline**.  
   ![Integrate with the plus menu and the entry Pipeline](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-02-02-36-4897-PM.png?width=403&height=186&name=image-png-Dec-02-2025-02-02-36-4897-PM.png)
2. Drag the activity **Notebook** of **Synapse** into the pipeline.  
   ![Pipeline canvas with a Notebook activity](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-02-03-51-9799-PM.png?width=446&height=261&name=image-png-Dec-02-2025-02-03-51-9799-PM.png)
3. On its **General** tab, enter a name such as `Load_View_CreditCard`. On its **Settings** tab, select the load notebook of the view and the Spark pool.
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 two view activities lead to the activity of the view Order](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-02-06-08-2575-PM.png?width=528&height=237&name=image-png-Dec-02-2025-02-06-08-2575-PM.png)
6. Commit, create a pull request to the collaboration branch and publish, as in [Azure Synapse - Deployment](https://knowledge.bigenius-x.com/azure-synapse-deployment-2.0?hsLang=en#upload-the-artifacts).
7. Select **Debug** or add a trigger. The activities start in the order of their connections.  
   ![Pipeline run with the first activities succeeded](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Dec-02-2025-02-07-04-1440-PM.png?width=563&height=264&name=image-png-Dec-02-2025-02-07-04-1440-PM.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/azure-synapse-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.

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.

### Related articles

- Previous: [Azure Synapse - Deployment](https://knowledge.bigenius-x.com/azure-synapse-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)

- [Getting started](https://knowledge.bigenius-x.com/getting-started?hsLang=en#main-content)

    - [Guidelines](https://knowledge.bigenius-x.com/getting-started?hsLang=en#guidelines)
    - [Start working](https://knowledge.bigenius-x.com/getting-started?hsLang=en#start-working)
    - [Account](https://knowledge.bigenius-x.com/getting-started?hsLang=en#account)
- [General overview](https://knowledge.bigenius-x.com/general-overview?hsLang=en)
- [Artificial Intelligence](https://knowledge.bigenius-x.com/artificial-intelligence?hsLang=en)
- [Application Modules](https://knowledge.bigenius-x.com/application-modules?hsLang=en#main-content)

    - [Administration](https://knowledge.bigenius-x.com/application-modules?hsLang=en#administration)
    - [Solutions](https://knowledge.bigenius-x.com/application-modules?hsLang=en#solutions)
    - [Global Features](https://knowledge.bigenius-x.com/application-modules?hsLang=en#global-features)
    - [Projects](https://knowledge.bigenius-x.com/application-modules?hsLang=en#projects)
    - [Branches](https://knowledge.bigenius-x.com/application-modules?hsLang=en#branches)
    - [Data Connections](https://knowledge.bigenius-x.com/application-modules?hsLang=en#data-connections)
    - [Dataflow Modeling - Overview](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-overview)
    - [Dataflow Modeling - Wizard Steps](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-wizard-steps)
    - [Dataflow Modeling - Terms](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-terms)
    - [Dataflow Modeling - Term Mapping](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-term-mapping)
    - [Dataflow Modeling - Relationships](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-relationships)
    - [Dataflow Modeling - Data Quality](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-data-quality)
    - [Dataflow Modeling - Indexes](https://knowledge.bigenius-x.com/application-modules?hsLang=en#dataflow-modeling-indexes)
    - [Relationship Modeling](https://knowledge.bigenius-x.com/application-modules?hsLang=en#relationship-modeling)
    - [Generate Artifacts](https://knowledge.bigenius-x.com/application-modules?hsLang=en#generate-artifacts)
    - [Project Settings](https://knowledge.bigenius-x.com/application-modules?hsLang=en#project-settings)
    - [Data Marketplace](https://knowledge.bigenius-x.com/application-modules?hsLang=en#data-marketplace)
- [Generators](https://knowledge.bigenius-x.com/generators?hsLang=en#main-content)

    - [Fabric Warehouse](https://knowledge.bigenius-x.com/generators?hsLang=en#fabric-warehouse)
    - [Fabric Lakehouse](https://knowledge.bigenius-x.com/generators?hsLang=en#fabric-lakehouse)
    - [Databricks](https://knowledge.bigenius-x.com/generators?hsLang=en#databricks)
    - [Snowflake](https://knowledge.bigenius-x.com/generators?hsLang=en#snowflake)
    - [Microsoft SQL Server](https://knowledge.bigenius-x.com/generators?hsLang=en#microsoft-sql-server)
    - [Oracle](https://knowledge.bigenius-x.com/generators?hsLang=en#oracle)
    - [Artifacts](https://knowledge.bigenius-x.com/generators?hsLang=en#artifacts)
    - [Replace Placeholders](https://knowledge.bigenius-x.com/generators?hsLang=en#replace-placeholders)
    - [Target solution environment](https://knowledge.bigenius-x.com/generators?hsLang=en#target-solution-environment)
    - [Deployment](https://knowledge.bigenius-x.com/generators?hsLang=en#deployment)
    - [Deployment with an Azuze DevOps pipeline](https://knowledge.bigenius-x.com/generators?hsLang=en#deployment-with-an-azuze-devops-pipeline)
    - [Delta Deployment](https://knowledge.bigenius-x.com/generators?hsLang=en#delta-deployment)
    - [Load control environment](https://knowledge.bigenius-x.com/generators?hsLang=en#load-control-environment)
    - [Load data with a native load control](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-a-native-load-control)
    - [Load data with Apache Airflow](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-apache-airflow)
    - [Model Object Type](https://knowledge.bigenius-x.com/generators?hsLang=en#model-object-type)
    - [Properties](https://knowledge.bigenius-x.com/generators?hsLang=en#properties)
    - [Default Terms](https://knowledge.bigenius-x.com/generators?hsLang=en#default-terms)
- [Discovery application](https://knowledge.bigenius-x.com/discovery-application?hsLang=en#main-content)

    - [Discovery configurations](https://knowledge.bigenius-x.com/discovery-application?hsLang=en#discovery-configurations)
- [Best practices](https://knowledge.bigenius-x.com/best-practices?hsLang=en#main-content)

    - [Modeling Approaches](https://knowledge.bigenius-x.com/best-practices?hsLang=en#modeling-approaches)
    - [Use Cases](https://knowledge.bigenius-x.com/best-practices?hsLang=en#use-cases)
    - [Business Rules](https://knowledge.bigenius-x.com/best-practices?hsLang=en#business-rules)
    - [Data Quality Rules](https://knowledge.bigenius-x.com/best-practices?hsLang=en#data-quality-rules)
- [FAQs](https://knowledge.bigenius-x.com/faqs?hsLang=en)
- [Product Release Notes](https://knowledge.bigenius-x.com/product-release-notes?hsLang=en#main-content)

    - [SaaS Application](https://knowledge.bigenius-x.com/product-release-notes?hsLang=en#saas-application)
    - [Discovery application](https://knowledge.bigenius-x.com/product-release-notes?hsLang=en#discovery-application)
    - [Generators](https://knowledge.bigenius-x.com/product-release-notes?hsLang=en#generators)
- [Legal Documents](https://knowledge.bigenius-x.com/legal-documents?hsLang=en#main-content)

    - [Current legal docs](https://knowledge.bigenius-x.com/legal-documents?hsLang=en#current-legal-docs)
    - [Software Product and Limits](https://knowledge.bigenius-x.com/legal-documents?hsLang=en#software-product-and-limits)

- [Contact us](https://knowledge.bigenius-x.com/kb-tickets/new?hsLang=en)
- [Customer Portal](https://knowledge.bigenius-x.com/customer-portal)
- [Sign in](https://knowledge.bigenius-x.com/_hcms/mem/login)

[![biGENIUS logo](https://knowledge.bigenius-x.com/hubfs/TRI_Logo_biGENIUS_2020_RGB-1.svg "biGENIUS logo")](https://www.bigenius-x.com)

<https://www.linkedin.com/company/bigenius> <https://www.youtube.com/@biGENIUS_DWA>

All rights reserved © 2025, biGENIUS AG