The build in this article, from source file to report.

Create a pipeline, run the Copy data assistant to load your file into a lakehouse table, and set the column types before you save. Then add a daily schedule. If your report reads the table through a Direct Lake semantic model, it shows the new data after every run with no refresh step.

Tested on a live Fabric trial capacity, SKU FTL64, loading a public CSV file over HTTP into a lakehouse and reading it through a Direct Lake semantic model.

What you end up with

The finished build is four Fabric items plus the source file, and each does one job.

You can follow along with the same data. Two sample files are public: https://casewhen.co/samples/sales-orders.csv holds 1,000 orders, and https://casewhen.co/samples/sales-orders-update.csv holds the same orders plus one more week, 1,085 in total. Everything below runs on a Fabric trial. Pipelines, lakehouses and semantic models all work on a trial. Only the AI features are blocked.

The Copy data assistant now creates a Copy job

Open your workspace and select New item, then Pipeline. The start screen offers four ways in. The Copy data assistant is the one built for loading a file.

Fabric > Workspace > New item > Pipeline. The start screen, with the Copy data assistant outlined.

The assistant does not add a Copy activity to your pipeline. It creates a separate Copy job item and adds a Copy job activity that runs it. Microsoft's own first-pipeline quickstart still describes the older result, a Copy activity on the canvas, so what you see will not match it.

That matters because the Copy job holds the source, the table name and the column types. Those are edited in the Copy job, not in the pipeline.

Connect to the file

In the assistant, search for HTTP and select the Http connector. It asks for two things: a URL and an authentication kind. For a public file, the authentication kind is Anonymous.

Copy data assistant > Choose data source > Http. The URL is the folder, and authentication is Anonymous.

Put the folder in the URL field, ending with a slash, and the file name on the next screen, in Relative URL. The connection then serves every file in that folder. Fabric's own error message confirms the slash matters: when a request fails, it tells you to make sure a base URL that includes a path ends with /.

Set the column types before you save

The assistant reads the file and shows a preview. The Schema tab shows the problem: every column arrives as String, including the numbers and the date.

Copy data assistant > Choose data > Preview data > Schema. All nine columns are read as String.

If you save with those types, amount is text in the table, and a report cannot sum text. Fix it on the Map to destination step. Select Edit column mapping and change the destination types:

The decimal type defaults to precision 38 and scale 18. For money, 18 and 2 is the usual choice.

Copy data assistant > Map to destination > Edit column mapping. Destination types changed from string.

On the same step, rename the table. The assistant names it dbo.HttpServerFile after the connector, which tells nobody what it holds. This walkthrough uses dbo.sales_orders.

Every run replaces the whole table

The last step of the assistant summarizes the job. It reads "Full copy" and "Overwrite destination". Incremental copy is grayed out for an HTTP source, so full copy is the only option.

Copy data assistant > Review + save. Full copy with Overwrite destination.

Overwrite is correct when the source is a full extract, a file that holds every order each day. Each run then replaces the table with the latest complete picture. If your source only sends the new rows each day, this setup deletes the previous rows on every run. That case needs a different design.

Two defaults under Advanced settings on the same screen are worth knowing:

• The timeout is 12 hours.

• Retry is 0, so a single failed request fails the whole copy. This is the Copy job's own retry setting. The pipeline activity has a separate one, covered under failures below.

Give the pipeline its connection

When you save, the assistant closes and the pipeline canvas shows one Copy job activity. Its name, Copy job_c06 here, is the activity's name. The Copy job item it calls is named separately, CopyJob_1 here. You can rename the activity on its General tab.

The activity is not ready to run yet. It needs a connection of its own, which the assistant never asks for.

Pipeline editor > Copy job activity > Settings. The Connection field is empty and required after the assistant finishes.

To fix it, open the Connection list, select Browse all, choose Copy job, and sign in with your organizational account. This is a one-time step. The connection is saved and reused.

Check the load

Run the pipeline. The Copy job has its own results panel, and it is the quickest place to confirm a load, because it counts rows on both sides.

CopyJob_1 > Results. 1,000 rows read, 1,000 rows written, one file.

The first run read 1,000 rows and wrote 1,000 rows in 25 seconds. The pipeline around it took just over a minute. The lakehouse table view showed the same count, with the integer, date and decimal types from the mapping.

One thing can mislead you here. The lakehouse's SQL analytics endpoint syncs on a delay. Right after the second run, a query against the endpoint still returned 1,000 rows, while the table itself already held 1,085. If a SQL query disagrees with the Copy job results straight after a run, wait and query again before you assume the load failed.

Build a Direct Lake model on the table

In the lakehouse, select New semantic model, give it a name and tick the table. The dialog is titled "Direct Lake semantic model name": a model created this way is Direct Lake by default. It reads the Delta table in OneLake directly instead of importing a copy. If Direct Lake is new to you, it sits between the two modes compared in DirectQuery vs Import.

To check the numbers, open the model's DAX query view and run a query that returns the row count, the total and the latest date:

EVALUATE
ROW (
    "Rows", COUNTROWS ( sales_orders ),
    "Total amount", SUM ( sales_orders[amount] ),
    "Last order date", MAX ( sales_orders[order_date] )
)

After the first load it returned 1,000 rows, a total of 299,345.10 and a last order date of 29 August 2026. Those match the source file exactly.

One small fix before you build visuals. The model marks order_id as a summable number, so a visual would add up order IDs. Set its Summarize by property to None.

The report updates without a refresh step

The second run loaded sales-orders-update.csv into the same table. Nothing refreshed the semantic model. The same DAX query then returned 1,085 rows, a total of 325,311.20 and a last order date of 5 September 2026.

Semantic model > DAX query view. The model shows the second load's 1,085 rows with no refresh.

The model's settings show why. Under Refresh, "Keep your Direct Lake data up to date" is on for a new model. Fabric describes it as detecting changes to the data in OneLake and updating the Direct Lake tables automatically. The model's last refresh was still the moment it was created.

Workspace settings > Semantic models > Sales Orders > Refresh. Keep your Direct Lake data up to date is On.

You need a refresh step in two cases: when the model uses Import mode, or when this setting is off. Pipelines have a Semantic model refresh activity for that. Place it after the Copy job activity so it runs only when the copy succeeds. That activity is described here from Microsoft's documentation. The Direct Lake behavior above is what was tested.

Changing the source file resets your mapping

Sooner or later you point the job at a different file. In the Copy job, the file name lives under Choose data source, in Relative URL. Change it there and step through, and the Map to destination screen comes back with the table name reset to dbo.HttpServerFile.

CopyJob_1 > Choose data source > Map to destination. The table name is back to the default.

The column types reset to string as well. Import schemas does not bring them back, because it imports the source's schema, which is all strings. If you apply this screen as it is, the next run writes to a new table your report does not use.

The fix takes a minute:

1. In the destination dropdown, switch from Enter manually to From selection.

2. Pick the existing dbo.sales_orders table.

3. Open Edit column mapping and set the five types again.

The simpler rule is to avoid the situation. Have the source publish each day's file to the same URL, and the Copy job never needs editing.

Schedule it

In the pipeline, select Schedule, then Add schedule. The default is "By the minute", every 15 minutes, with an end date 24 hours after the start. Saved as it is, it runs 96 times and then stops.

Change Repeat to Daily. The end date then moves to 31 December 9999 on its own. Set the time of day and save.

Pipeline > Schedule > Add schedule. Daily repeats until 31 December 9999.

Find out when it fails

A failed run shows up in the Monitor hub, in the left navigation, with the pipeline and the Copy job as separate rows.

Fabric > Monitor > Activities. The pipeline and its Copy job both show Failed.

Select the details icon on the pipeline row. The Details panel cuts the message off at "error message received from ...". Select the Copy error button in that panel to copy the full message. For a missing file, it reads "Http request failed with client error, status code 404 NotFound".

The pipeline's Copy job activity has its own retry setting, separate from the Copy job's. On the activity's General tab, Enable retries sits under the timeout. Turning it on means a brief outage at the source does not fail the whole morning's load.

What happens when the trial ends

A trial lasts 60 days. The pipeline, the Copy job and the lakehouse are all non-Power BI items, and when the trial ends they become unusable and may be deleted. You get seven days after expiry to keep them by moving the workspace to a paid F or P capacity. The semantic model is a Power BI item, but it reads from the lakehouse, so it has nothing to show once the lakehouse is gone.

If this build is the start of something real, plan the capacity before the trial runs out, not after. For the choice between a pipeline and the other ways to move data in Fabric, see Dataflows vs Notebooks in Fabric and Data Factory in Fabric vs Azure.

Sources

• Create your first pipeline to copy data - Microsoft Learn

• Microsoft Fabric trial capacity - Microsoft Learn

• Direct Lake overview - Microsoft Learn

• Semantic model refresh activity in Data Factory - Microsoft Learn