Filter Actual-Time Error Rows From CSV To SQL Database Desk In Azure Information Manufacturing facility – Half Two

**This can be a continuation of half one, I counsel you to verify that first to get a transparent understanding**

As soon as the primary situation is accomplished let’s verify the second which I named as ValidRows as it’ll seize solely the non-error values. In comparison with the primary situation that is quite simple as we don’t need to construct any expressions, it simply collects all of the information that doesn’t fulfill level primary.

Each the circumstances are accomplished, now go to the preview choice and refresh to verify the implementation and the information break up is working correctly.

Choose the primary drop down within the knowledge preview tab the place all of the error or incomplete knowledge is amassed.

The second dropdown clearly accommodates all of the legitimate knowledge rows.

The subsequent step of our demo is to retailer each the ErrorRows and ValidRows into the respective SQL database tables. For this we should add a sink subsequent to the prevailing ErrorRows within the knowledge circulation like beneath. Please observe that this sink ought to be pointed to lure all of the error rows therefore we place it subsequent to ErrorRows sink.

Once we attempt to create a brand new dataset, we will see the accessible linked service and the tables current underneath them to pick out within the drop-down.

Filter real-time error rows from CSV to SQL Database Table in Azure Data Factory

For those who bear in mind we created an extra column named ‘FileName’ in our database desk to retailer the information supply file identify as nicely. We’ll create a derived column in between which can embrace the filename into the database desk each time the sink is run.

Filter real-time error rows from CSV to SQL Database Table in Azure Data Factory

One factor value mentioning right here is we have now created all columns together with the date in ‘Sales_table_ErrorRows’ desk as varchar as a result of once we attempt to retailer the error rows they is perhaps of any variety, so having a column with datatypes like datetime will find yourself in error.

I’ve disabled the auto-mapping from the mapping settings after which refreshed the information preview and will see the error rows getting fetched correctly together with the filename.

Filter real-time error rows from CSV to SQL Database Table in Azure Data Factory

Allow us to transfer on to the subsequent step, Legitimate rows.

Create a sink dataflow that may transfer the information into the database desk much like the earlier instance. Sadly, after I previewed the information, I couldn’t see any datas in ‘Date2’ column after which after some time I discovered the explanation and stuck it by making a derived column that may convert the Date2 column from string into Date datatype. At all times remember to ensure the information you move on ought to be as identical because the datatype it’s already current in database desk, this can keep away from half of the run failure errors.

I added Date2 column into the prevailing derived column settings and transformed from string up to now operate utilizing the visible expression builder as beneath. We might additionally see the string image abc has been transformed into calender image within the knowledge preview part as marked beneath.

Filter real-time error rows from CSV to SQL Database Table in Azure Data Factory

See all of the legitimate information are getting positioned with file identify.

Filter real-time error rows from CSV to SQL Database Table in Azure Data Factory

Now the subsequent and ultimate step is to maneuver these knowledge into the database desk that we created earlier. For this we’ll create a brand new pipeline.

After debugging the dataflow we might see the execution run efficiently. It’s also possible to publish every part and run the dataflow instantly.

The legitimate rows and the error rows has been segregated into two separate tables.


We noticed how we might use Azure Information Manufacturing facility to filter out error rows and retailer them right into a SQL database desk. This is likely one of the many actual time situations which will probably be helpful if you find yourself coping with thousands and thousands of rows that must be remodeled to current it to the administration. Planning to publish on the right way to take care of such actual time issues by means of ADF and Synapse, keep tuned. Cheers!

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