Microsoft Fabric Data Ingestion Methods and Tools

By:   |   Updated: 2023-12-08   |   Comments   |   Related: > Microsoft Fabric


I've been reading the tips about Microsoft Fabric on I have wondered if most sample data was uploaded manually to Fabric, but I assume this is not ideal for a production environment. Which methods or tools can be used to automate the ingestion of data into Microsoft Fabric?


Microsoft Fabric is the new centralized end-to-end data analytics platform in the cloud. It offers several compute services, such as the lakehouse, data warehouse, and real-time analytics. Manually uploading data daily is not the goal; instead, it is having it ingested automatically. In this tip, we'll investigate some options to get data into Fabric.

We can divide the ingestion of data in Fabric into two categories:

  1. Code-based pipelines: Notebooks and Spark jobs.
  2. UI-based pipelines: Data pipelines and data flows.

Keep in mind that this tip was written while Microsoft Fabric was in public preview. This means features can change, disappear, or be added, and even the general outlook may change. Additionally, to limit the scope, real-time ingestion for the KQL databases or Eventstreams is left out.

Code-based Pipelines

A previous tip, Create a Notebook and Access Your Data in a Microsoft Fabric Lakehouse, shows how to create a notebook that will read some data using PySpark.

data loaded to a dataframe in a notebook

You can go to its settings in a notebook by clicking on the gear icon.

go to notebook settings

There, you will find a Schedule section to define a simple schedule that will execute your notebook.

define notebook schedule

Alternatively, you can go to the notebook's Run section and click Schedule for the same settings pane.

schedule notebook through run pane

There's time zone support, but oddly, you must specify an end date for the schedule before clicking Apply. Another option is to add your notebook to a pipeline and then schedule that pipeline. You can find more information on pipelines in the UI-based pipelines section of this tip.

add notebook to pipeline

Using the Data Engineering persona, you can create a Spark Job Definition instead of directly scheduling a notebook.

Spark Job Definition

When a new Spark Job is created, you must specify a name first:

specify name for spark job

In the Spark Job, you can switch between the available languages by using the dropdown at the top:

change language of the spark job

For the job configuration, you need to specify a main definition file (which contains the actual code that needs to be run) by either uploading a file or referencing one stored in Azure Data Lake Storage. You can also specify optional reference files (for example, modules used in your Python code) and/or command line arguments.

spark job configuration

At the bottom, you need to reference one or more lakehouses, just as you would in a notebook. Once the configuration is done, you can schedule the job in Settings, like in the notebook.

schedule spark job

UI-based Pipelines

When in the data factory persona, two possible objects can be created: data pipelines and dataflow Gen2.

ui-based pipelines in Fabric

A data pipeline is essentially the same as a pipeline in Azure Data Factory (ADF). However, it's not an exact copy of ADF for the moment. Some features are still missing and being added while Fabric is still in preview mode.

To create a new pipeline, you have three options: add a pipeline activity, copy data, or choose a task to start.

start building your data pipeline, 3 options

Adding a pipeline activity to a blank canvas is typically chosen if you already have experience with ADF and immediately want to start working on your pipeline.

If you choose Copy data, this will create a pipeline with one Copy Data activity, configured by the wizard, similar to ADF, as seen in the image below.

copy data wizard in Fabric

The third option, Choose a task to start, provides templates to begin the creation of your pipeline quickly, as seen below.

templates for pipelines in ADF

A significant difference between pipelines in Fabric and ADF is that there are no datasets and linked services in Fabric. Instead, everything is defined in-line:

source connections in copy data activity

If you want to edit an existing connection, click Edit next to the dropdown. This will take you to the Data screen, where you can edit the connection and view all other created connections.

all connections in Fabric

You can reach the same screen by clicking the gear icon in the top right corner and choosing Manage connections and gateways.

manage connections in the settings menu

Another feature not present in Fabric is the mapping data flows of ADF. The Power Query data flow of ADF is similar to the Dataflow Gen2 pipeline in Fabric, as both are implementations of the Power Query engine. Dataflows Gen2 are also an improvement upon the Power BI dataflows, as you can choose a destination in the Gen2 pipeline.

A Dataflow Gen2 pipeline allows you to transform data by applying transformations. You can use the ribbon to create a new transformation or the visual editor to add a transformation.

power query editor in dataflow gen2

By clicking the icons in the visual editor or selecting a step in the sidebar, you can go back and forth between the different transformations and preview their effect on the data. As mentioned before, a destination can be configured. You can hover over the icon in the visual editor to see the current configuration.

destination config in visual editor

You can edit the destination by clicking on it.

edit destination

The same can be done in the query settings pane:

destination in query settings

You can choose a destination in the ribbon if you don't have one. If a destination is already configured, this option will be greyed out:

add data destination in ribbon

In the destination settings, you can choose to either overwrite the target or to append data to the target:

append or overwrite


In Microsoft Fabric, there are several methods in two broad categories to ingest data: code-based pipelines and UI-based pipelines. The first category, Code-based pipelines, mainly uses Spark for data ingestion and transformation. You can use notebooks and Spark jobs to automate ingestion. The second category, UI-based pipelines, is either data pipelines (like Azure Data Factory) or dataflows (Power Query online in the browser, with the option to choose a destination).

These are not your only options, though. Because the storage format is open (both the lakehouse and the warehouse store data as delta tables), you are free to use any tool you like to get data into Fabric. In the warehouse, it's also possible to use SQL statements such as COPY INTO or CREATE TABLE AS SELECT to get data into tables.

Next Steps
  • If you want to familiarize yourself with the concepts of Azure Data Factory (which can be translated to Microsoft Fabric pipelines), check out the free tutorial.
  • The tip, What is Power Query?, introduces the Power Query editor. Power BI Desktop is used in this tip, but the concepts are roughly the same in a Microsoft Fabric Dataflow Gen2.
  • You can find more Fabric tips in this overview.

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About the author
MSSQLTips author Koen Verbeeck Koen Verbeeck is a seasoned business intelligence consultant at AE. He has over a decade of experience with the Microsoft Data Platform in numerous industries. He holds several certifications and is a prolific writer contributing content about SSIS, ADF, SSAS, SSRS, MDS, Power BI, Snowflake and Azure services. He has spoken at PASS, SQLBits, dataMinds Connect and delivers webinars on Koen has been awarded the Microsoft MVP data platform award for many years.

This author pledges the content of this article is based on professional experience and not AI generated.

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Article Last Updated: 2023-12-08

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