I have a pipe delimited files with header and body. Header data has fewer columns than the body. In data flow, I am splitting header and body data and doing transformations on them and union transformation is applied to transformed header and body data. While joining both the data, I am getting additional pipes at the end of the header.
Source data will be like
Header1|id1
Header2|id2
1|Debashish|1500|30
2|Susmitha|1900|20
After doing transformations,
Header1|id1||
Header2|id2||
I need to remove the extra pipelines at the end of header, It should be same as source. How can I do this.
Before copying the data from union transformation to the sink, you can concatenate all columns into a single column separated by pipe symbol and then trim the extra pipes available in the end of the data using derived column activity. I tried to repro this in my environment. Below are the detailed steps.
A sample source file is taken as in below image.
Source transformation is added with source dataset in dataflow activity.
Then, a new column called concat_column is created using derivedcolumn transformation. Value for the column is given as
concat(coalesce({_col0_},''),'|',coalesce({_col1_},''),'|',coalesce({_col2_},''),'|',coalesce({_col3_},''))
Then again, a derived column transformation is added to remove the pipes from the data. Column2 is created and value is given as rtrim(concat_column,'||').
Results of concat_column and column2 :
Select transformation is added only to select the column2
Sink transformation is added and for sink dataset, different delimiter is given (other than pipe symbol. Here no delimiter is used).
After executing the pipeline, Output file has no pipe symbol at the end.
Related
I have created a new dataflow ,at the source i have given Wildcard file name as
input/*.csv
Does this do the union of all csv files under input folder and out put it ?
Because When I do an aggregate on bothe source1 and source 2 ,source 1 has higher row count than source 2.
Does that meant ,we can union like this without union transformation?
By Passing Wildcard filename to get all the files unioned in Azure dataflow dataset?
Yes, it provides output similar to union transformation irrespective of schema of files are similar or not. if any column is not present any of the file it will return null values for that column in other files similar to union transformation.
Does that mean, we can union like this without union transformation?
As Union transformation can union only 2 datasets as per blew image you can use wildcards to union all files with file name you are passing to match.
I can't map drift my columns in ADF data flow. I'm able to manually, but this isn't possible as I have 1020 columns. File is .csv
I see a message: 'This drifted column is not in the source schema and therefore can only be referenced with pattern matching expressions'
I was hoping to have a map drifted data flow from my source data.
With > 1k columns, you should consider NOT mapping those columns. Just use column patterns inside your transformation expressions to access columns. Otherwise, ADF will have to materialize the entire 1k+ columns as a physical projection.
I have a delimited file separated by hashes that looks somewhat like this,
value#value#value#value#value#value##value
value#value#value#value##value#####value#####value
value#value#value#value###value#value####value##value
As you can see, when separated by hashes, there are more columns in the 2nd and 3rd rows than there is in the first. I want to be able to ingest this into a database using a ADF Data Flow after some transformations. However, whenever I try to do any kind of mapping, I always only see 7 columns (the number of columns in the first row).
Is there any way to get all of the values? As many columns as there are in the row with most number of items? I do not mind the nulls.
Note: I do not have a header row for this.
Azure Data Factory directly will not be able to Import schema -row with the maximum number of column. Hence, it is important to make sure you have same number of columns in your file.
You can use Azure functions to validate your file and update it to get equal number of columns in all rows.
You could give it a try to have a local file with row with the maximum number of column and import the schema from the file, else you have to go for Azure Functions where you have to convert the file and then trigger the pipeline.
We ingest data from multiple customers and have no control over the format of the data. The data pertains to the same subject matter but the file names, column names, headers, and row headings are all variable.
Schema drift capabilities in data flow mapping looks like it will handle the variable file and column names but i'm not sure how best to handle the fact that the column headings could be on row 1 or 2 or 10, etc.
Previously we used some Python code to figure this out, is there any capability within Data Factory to accommodate this level of variability?
You will need a rule to determine which row has the headers. Then, you can use a Filter transform to filter out the header row from the data rows.
But if you need the names of the headers in your flow, then you'll need to first run that file through a separate data flow that rewrites the file with the header row as the first row.
You can do this by adding 2 sources to a data flow, both pointing to the same file. Then, filter OUT the header row from one source and filter IN just the header row in the 2nd source.
Union those 2 streams back together and write to a new file in the Sink.
I have a source .csv with 21 columns and a destination table with 25 columns.
Not ALL columns within the source have a home in the destination table and not all columns in the destination table come from the source.
I cannot get my CopyData task to let me pick and choose how I want the mapping to be. The only way I can get it to work so far is to load the source data to a "holding" table that has a 1:1 mapping and then execute a stored procedure to insert data from that table into the final destination.
I've tried altering the schemas on both the source and destination to match but it still errors out because the ACTUAL source has more columns than the destination or vice versa.
This can't possibly be the most efficient way to accomplish this but I'm at a loss as to how to make it work.
Yes I have tried the user interface, yes I have tried the column schemas, no I can't modify the source file and shouldn't need to.
The error code that is returned is some variation on:
"errorCode": "2200",
"message": "ErrorCode=UserErrorInvalidColumnMappingColumnCountMismatch,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=Invalid column mapping provided to copy activity: '{LONG LIST OF COLUMN MAPPING HERE}', Detailed message: Different column count between target structure and column mapping. Target column count:25, Column mapping count:16. Check column mapping in table definition.,Source=Microsoft.DataTransfer.Common,'",
"failureType": "UserError",
"target": "LoadPrimaryOwner"
Tim F. Please view the statements in this Schema mapping in copy activity:
Column mapping supports mapping all or subset of columns in the source
dataset "structure" to all columns in the sink dataset "structure".
The following are error conditions that result in an exception:
1.Source data store query result does not have a column name that is specified in the input dataset "structure" section.
2.Sink data store (if with pre-defined schema) does not have a column name that is specified in the output dataset "structure" section.
3.Either fewer columns or more columns in the "structure" of sink dataset than specified in the mapping.
4.Duplicate mapping.
So,you could know that all the columns in the sink dataset need to be mapped. Since you can't change the destination,maybe you don't have to struggle in an unsupported feature.
Of course ,you could use stored procedure mentioned in your description.That's a perfect workaround and not very troublesome. About the using details, you could refer to my previous cases:
1.Azure Data Factory activity copy: Evaluate column in sink table with #pipeline().TriggerTime
2.Azure Data factory copy activity failed mapping strings (from csv) to Azure SQL table sink uniqueidentifier field
In addition, if you really don't want avoid above solution,you could submit feedback to ADF team about your desired feature.