I'm attempting to assign quartiles to a numeric source data range as it transits a data flow.
I gather that this can be accomplished by using the ntile expression within a window transform.
I'm failing in my attempt to use the documentation provided here to get any success.
This is just a basic attempt to understand the implementation before using it for real application. I have a numeric value in my source dataset, and I want the values within the range to be spread across 4 buckets and defined as such.
Thanks in advance for any assistance with this.
In Window transformation of Data Flow, we can configure the settings keeping the source data numeric column in “Sort” tab as shown below:
Next in Window columns tab, create a new column and write expression as “nTile(4)” in order to create 4 buckets:
In the Data Preview we can see that the data is spread across 4 Buckets:
Related
As someone with a background in Alteryx, it has been a slow process to get up to speed with the expressions and syntax within Azure Data Factory data flows. I am trying to filter out rows containing the following string in a similar manner to this Alteryx filter code below:
!Contains([Subtype], "News")
After scrolling through all the string expressions in Azure Data Factory, I am struggling to find anything similar to the logic above. Thanks in advance for any help you can provide me on this front!
You can use Filter transformation in ADF Data flow and give the condition
for any column like below:
My Sample Data:
Here I am filtering out the rows the which contains a string of "Rakesh" in the Name column with the Data flow expression instr(Name,"Rakesh")==0.
instr() returns number of common letters. Our condition satisfies if its result is 0.
Filter Transformation:
.
Output in Data preview of filter:
You can see the remaining rows only in the result.
I need to calculate billing percentage with respect to the total in Azure Data Factory WorkFlow. I used the expression:
Total_amount/sum(Total_amount)
But it doesn´t work. How could I calculate percentages using aggregate transformation inside a data flow?
Create a data flow with 2 sources. They can both be the same source, in your example. The first stream will have Source->aggregate [sum(total amount)]->Sink (cached). The second stream will have Source->derived column (total amount/lookup from the cached sink above). My example screenshot below does this exact same thing with loan data. This is what my formula in the derived column looks like: loan_amnt / sink1#output().total_amount
You can try using windows transformation in data flow.
Source:
Add a dummy column using derived column transformation and assign a constant value.
Using windows transformation, get the percentage value of the total amount.
Expression in window column:
multiply(divide(toInteger(Total_amount),sum(toInteger(Total_amount))),100)
Preview of windows transformation:
I'm having issues using the Exists Transformation within a Data Flow with a generic dataset.
I have two sources (one from staging table "sourceStg", one from DWH table "sourceDwh") and want to compare if the UniqueIdentifier-Column in the staging table is existing in the UniqueIdentifier-Column in the DWH table. For that I have a generic data set which I query with a SQL statement containing parameters.
When I open the "Exists settings" I cannot choose any Column from the source in the conditions since the source is generic and has no Projection until I run the data flow. However, I have a parameter which I get from the parent pipeline which provides me the name of the Column containing the UniqueIdentifier (both column names in staging / DWH are the same).
I tried to add following statement "byName($UniqueIdentifier)" in the left and right column field but the engine resolves them both as the sourceStg-Column since the prefix of the source-transformations is missing and it defaults to the first one. What I basically now try to achieve is having some statement as followed defining the correct source-transformation and the column containing the unique identifier with a parameter.
exists(sourceStg#$UniqueIdentifier == sourceDwh#$UniqueIdentifier)
But either the expression cannot be parsed or the result does not retrieve the actual UniqueIdentifier value from the column but writes the statement (e.g. sourceStg#$UniqueIdentifier) as column value.
The only workaround I found so far is having two derived columns which adds a suffix to the UniqueIdentifier-Column in one source and a new parameter $UniqueIdentiferDwh which is populate with the parameter $UniqueIdentifier and the same suffix as used in the derived column.
Any Azure Data Factory experts out there to help?
Thanks in advance!
The excel consist of 62 columns and 7 columns are fixed and rest of them have weeks as in year(week1 to week 52)
I have used a data flow task to unpivot the 53 columns into rows with 2 extra columns year and value.
The problem is that I have the 52 week column names keep changing on every week data load and how to I handle this change in column names in data flow. For a single run it gives the exact output
What you'll want to do here is to implement late-binding of your schema, or what ADF refers to as "schema drift". Instead of setting a hardened "early binding" schema in your Source projection, leave the dataset schema and projection empty.
Next, add a Derived Column after your source and call it "Projection". This is where you'll build your projection using rules to account for your evolving schema.
Build out your canonical model with the column names for your entire year using byName('columnname'). That will tell ADF to look for the existence of the column in single quotes from your source data while also providing a schema that you can use to build out your pivot table.
If you need to cast the values, wrap byName() inside of a casting function, i.e. toString(), toDate(), etc.
I try create a custom parallel extractor, but i have no idea how do it correctly. I have a big files (more than 250 MB), where data for each row are stored in 4 lines. One file row store data for one column. Is this possible to create working parallely extractor for large files? I am afraid that data for one row, will be in different extents after file splitting.
Example:
...
Data for first row
Data for first row
Data for first row
Data for first row
Data for second row
Data for second row
Data for second row
Data for second row
...
Sorry for my English.
I think, you can process this data using U-SQL sequentially not in parallel. You have to write a custom applier to take a single/multiple rows and return single/multiple rows. And then, you can invoke it with CROSS APPLY. You can take help from this applier.
U-SQL Extractors by default are scaled out to work in parallel over smaller parts of the input files, called extents. These extents are about 250MB in size each.
Today, you have to upload your files as row-structured files to make sure that the rows are aligned with the extent boundaries (although we are going to provide support for rows spanning extent boundaries in the near future). In either way though, the extractor UDO model would not know if your 4 rows are all inside the same extent or across them.
So you have two options:
Mark the extractor as operating on the whole file with adding the following line before the extractor class:
[SqlUserDefinedExtractor(AtomicFileProcessing = true)]
Now the extractor will see the full file. But you lose the scale out of the file processing.
You extract one row per line and use a U-SQL statement (eg. using Window Functions or a custom REDUCER) to merge the rows into a single row.
I have discovered that I cant use static method to get an instance of IExtractor implementation in USING statement if I want use AtomicFileProcessing set on true.