I'm trying to match the xls file input with the records in the database, if the records of the xls file matches with the records in the database then the records of the xls file will not be inserted to the database (preventing duplication), and if the records of the xls file does not match with the records in the database then the records of the xls file will be input since it means the records does not exist yet. This is my connection and the details.
The problem is, no matter how i set the Lookup Failure options, the lookup will feed the records from the reference db that matches with the csv_rec to the target db. Which makes duplication, not preventing it. How am i supposed to do to only insert the main input to the target if it doesn't match with the reference db and doesn't insert the main input if there's match with the reference db? I'm new to this so i'm very confused..
Direct the stream output of the Lookup stage into a Copy stage with no output.
Add a Reject link from the Lookup stage to the ODBC stage. This link will carry those records that weren't found on the target table. Note that you'll need to set the Lookup Failed property of the Lookup stage appropriately.
Use Lookup Reject link.
Main output link -> peek
Reject -> target DB
You would need a reject link so that you can process the lookup failures further down stream
Related
I am trying to create a pipeline which performs following task.
read and parse the csv file
apply schema on top of that
records which are mapping schema is written to a valid bigquery table
records which doesn't match schema (i.e. if column expect int but in file it's string) it goes to reject bucket.
I have write following pipeline. However, the problem is, I don't see any records going to either rejected or bigquery.
if schema is not matching, shouldn't it go to reject?
I have an Azure Data factory trigger that is fired off when a file is placed in blob storage, this trigger will start pipeline execution and pass the file name to the data flow activity. I would like to make sure that all the column names from the header row in the file are in the sink table. There is an identity column in the sink table that should not be in the comparison. Not sure how to tackle this task, I've read about the 'derived column' activity, is that the route I should take?
You can select or filter which columns reside in sink dataset or table by using "Field mapping". You can optionally use "derived columns" transformation, however in the "sink transformation" you will have this by default and is set to "Auto mapping". Here you can add or remove which columns are written to sink.
In the below example the column "id" can be assumed as similar to "Identity" column in your table. Assuming all the files have same columns:
Once you have modified as per your need, you can confirm the same from the "inspect" tab before run.
Strategy:
Use two ADF pipelines, one to get a list of all files and another one to process each file copying its content to a specific SQL table.
Setup:
I’ve created 4 CSV files, following the pattern you need: “[CustomerID][TableName][FileID].csv” and 4 SQL tables, one for each type of file.
A_inventory_0001.csv: inventory records for customer A, to be
inserted into the SQL table “A_Inventory”.
A_sales_0003.csv: sales
records for customer A, to be inserted into the SQL table “A_Sales”.
B_inventory_0002.csv: inventory records for customer B, to be
inserted into the SQL table “B_Inventory”.
B_sales_0004.csv: sales
records for customer B, to be inserted into the SQL table “B_Sales”
Linked Services
In Azure Data Factory, the following linked services were create using Key Vault (Key Vault is optional).
Datasets
The following datasets were created. Note we have created some parameters to allow the pipeline to specify the source file and the destination SQL table.
The dataset “AzureSQLTable” has a parameter to specify the name of the destination SQL table.
The dataset “DelimitedTextFile” has a parameter to specify the name of the source CSV file.
The dataset “DelimitedTextFiles” has no parameter because it will be used to list all files from source folder.
Pipelines
The first pipeline “Get Files” will get the list of CSV files from source folder (Get Metadata activity), and then, for each file, call the second pipeline passing the CSV file name as a parameter.
Inside the foreach loop, there is a call to the second pipeline “Process File” passing the file name as a parameter.
The second pipeline has a parameter “pFileName” to receive the name of the file to be processed and a variable to calculate the name of the destination table based on the file name.
The first activity is to use a split in the file name to extract the parts we need to compose the destination table name.
In the expression bellow we are splitting the file name using the “__” separator and then using the first and second parts to compose the destination table name.
#concat(string(split(pipeline().parameters.pFileName, '_')[0]),'_',string(split(pipeline().parameters.pFileName, '_')[10]))
The second activity will then copy the file from the source “pFileName” to the desnation table “vTableName” using dynamic mapping, ie not adding specific column names as this will be dynamic.
The files I used in this example and the ADF code are available here:
https://github.com/diegoeick/stack-overflow/tree/main/69340699
I hope this will resolve your issue.
In case you still need to save the CustomerID and FileID in the database tables, you can use the dynamic mapping and use the available parameters (filename) and create a json with the dynamic mapping in the mapping tab of your copy activity. You can find more details here: https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-schema-and-type-mapping#parameterize-mapping
I'm doing a large-scale project with multiple pipelines, millions of records per pipeline. I'm trying to develop a generic skipped row capture process.
What I need to do is: for every source row skipped due to any error encountered on the attempted load, I want to capture a key column value from the row and write it to a distinct log file (or separate DB table row). This can't be summary data: for each individual row that fails, I need to capture the row key from that row so we can review/re-load later (I will add in system variable values to identify pipeline, component, time stamp, etc). Pipeline must complete with all successful rows loaded, all unsuccessful rows logged.
This is no-brainer functionality in most ETL tools; I have to be overlooking something in ADF, because I can't find a way to do this. Appreciate any/all suggestions.
You can enable Fault tolerance and choose Skip incompatible rows option. It will skip the incompatible rows between source and target store during copy data. e.g. type and field mismatch or PK violation.
Then you can enable session log and choose Warning log level in copy activity to log skipped rows. Finally, you can save your log file in Azure Storage or Azure Data Lake Storage Gen2.
Reference:
https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-fault-tolerance
https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-log
With your first copy activity, check the fault tolerance option in 'settings' to log skipped fault rows.
Make sure to place your rows key column, as the first in the mapping definition.
Get the copy activity logFilePath from the activity output into a variable
Add another copy activity to load skipped rows into relational table
it source path will be the variable holds logFilePath
Set the file path type to: 'Wildcard file path'
Keep the 'Wildcard file path' empty
Will be the value in Wildcard file name
Make sure that the delimited file dataset escape character is set to quotations.
The OperationItem field of the lg file holds your record fields seperated by ,; because we placed the rowID first on mapping, it will appear first in OperationalItem as well.
Goodluck
I need to modify columns based on business rules using RCP. For example, all source columns that end with '_ID' must be changed to '_KEY' to meet the target.
An example: Test_ID in source becomes Test_KEY in target
I have multiple tables, some with 2 "ID" columns, and some with 20. Is there a way to configure the modify stage to bulk rename columns based on wildcard?
If not, is there another way?
Thanks.
I doubt that there is an option using modify stage with wildcards for this.
One alternative could be a schema file which can be used with any of following stages:
Sequential File, File Set, External Source, External Target, Column Import, Column Export
This schema file could also be generated or modified to adjust the column names as needed.
Another way could be to generate the appropriate SQL statement if the data resides in a database or is written to one.
I am looking for the easiest way to manually dump a subset of records in an OpenEdge database table in the Progress ".d" file format.
The best way I can imagine is creating an extra test database with the identical schema as the source database, and then copying the subset of records over to the test database using FOR EACH and BUFFER-COPY statements. Then just export the data from the test database using the Dump Data and Definitions Table Contens (.d file )... menu option.
That seems like a lot of trouble. If you can identify the subset of records in order to do the BUFFER-COPY than you should also be able to:
OUTPUT TO VALUE( "table.d" ).
FOR EACH table NO-LOCK WHERE someCondition:
EXPORT table.
END.
OUTPUT CLOSE.
Which is, essentially, what the dictionary "dump data" .d file is less a few lines of administrivia at the bottom which can be safely omitted for most purposes.