Example
sameer/student/land/compressed files
sameer/student/pro/uncompressed files
sameer/employee/land/compressed files
sameer/employee/pro/uncompressed files
In the above example I need to read files from all LAND folders present in different sub directories and need to process them and place them in PRO folders with in same sub folders.
For this I have taken two GCS nodes one from source and another from sink.
in the GCS source i have provided path gs://sameer/ , it is reading files from all sub folders and merging them into one file placing it in sink path.
Excepted output all files should be placed in sub directories where i have fetched from.
It can achieve the excepted output by running pipeline separately for each folder
I am expecting is this can be possible by a single pipeline run
It seems like your use case is simply moving files. In that case, I would suggest using the Action plugin GCS Move or GCS Copy.
It seems like the task you are trying to carry out is not possible to do in one single Data Fusion pipeline, at least at the time of writing this.
In a pipeline, all the sources and sinks have to be connected. Otherwise you will get the following error:
'Invalid DAG. There is an island made up of stages ...'
This means it is not possible to parallelise several uncompression tasks, one for each folder of files, inside the same pipeline.
At the same time, if you were to use something like the following schema, the outputs would be aggregated and replicated over all of the sinks:
Finally, I would say that the only case in which you can parallelise a task between several sources and several links is when using multiple database tables. By means of the following plug-ins (2) and (3) you can process data from multiple table inputs and export the output to multiple tables. If you would like to see all available plugins for Data fusion, please check the following link (4).
Related
In Azure Data Factory I have a pipeline, created from the built-in copy data task, that copies data from 12 entities (campaign, lead, contact etc.) from Dynamics CRM (using a linked service) and outputs the contents as parquet files in account storage. This is run every day, into a folder structure based on the date. The output structure in the container looks something like this:
Raw/CRM/2022/05/28/campaign.parquet
Raw/CRM/2022/05/28/lead.parquet
Raw/CRM/2022/05/29/campaign.parquet
Raw/CRM/2022/05/29/lead.parquet
That's just an example, but there is a folder structure for every year/month/day that the pipeline runs, and a parquet file for each of the 12 entities I'm retrieving.
This involved creating a pipeline, dataset for the source and dataset for the target. I modified the pipeline to add the pipeline's run date/time as a column in the parquet files, called RowStartDate (which I'll need in the next stage of processing)
My next step is to process the data into a staging area, which I'd like to output to a different folder in my container. My plan was to create 12 scripts (one for campaigns, one for leads, one for contact etc.) that essentially does the following:
accesses all of the correct files, using a wildcard path along the lines of: Raw/CRM/ * / * / * /campaign.parquet
selects the columns that I need
Rename column headings
in some cases, just take the most recent data (using the RowStartDate)
in some cases, create a slowly changing dimension, ensuring every row has a RowEndDate
I made some progress figuring out how to do this in SQL, by running a query using OPENROWSET with wildcards in the path as per above - but I don't think I can use my SQL script in ADF to move/process the data into a separate folder in my container.
My question is, how can I do this (preferably in ADF pipelines):
for each of my 12 entities, access each occurrence in the container with some sort of Raw/CRM///*/campaign.parquet statement
Process it as per the logic I've described above - a script of some sort
Output the contents back to a different folder in my container (each script would produce 1 output)
I've tried:
Using Azure Data Factory, but when I tell it which dataset to use, I point it to the dataset I created in my original pipeline - but this dataset has all 12 entities in the dataset and the data flow activity produces the error: "No value provided for Parameter 'cw_fileName" - but I don't see any place when configuring the data flow to specify a parameter (its not under source settings, source options, projection, optimize or inspect)
using Azure Data Factory, tried to add a script - but in trying to connect to my SQL script in Synapse - I don't know my Service Principal Key for the synapse workspace
using a notebook Databricks, I tried to mount my container but got an error along the lines that "adding secret to Databricks scope doesn't work in Standard Tier" so couldn't proceed
using Synapse, but as expected, it wants things in SQL whereas I'm trying to keep things in a container for now.
Could anybody point me in the right direction. What's the best approach that I should take? And if its one that I've described above, how do I go about getting past the issue I've described?
Pass the data flow dataset parameter values from the pipeline data flow activity settings.
I have 4000 files each averaging 30Kb in size landing in a folder on our on premise file system each day. I want to apply conditional logic (several and/or conditions) against details in their file names to only move files matching the conditions into another folder. I have tried linking a meta data activity which gets all files in the source folder with a filter activity which applies the conditional logic with a for each activity with an embedded copy activity. This works but it is taking hours to process the files. When running the pipeline in debug the output window appears to list each file copied as a line item. I’ve increased the batch count setting in the for each to 50 but it hasn’t improved things. Is there a way to link the filter activity directly to the copy activity without using for each activity? Ie pass the collection from the filter straight into copy’s source. Alternatively, some of our other pipelines just use the copy activity pointing to a source folder and we configure its filefilter setting with a simple regex using a combination of * and ?, which is extremely fast. However, in this particular scenario, my conditional logic is more complex and I need to compare attributes in each file’s name with values to decide if the file should be moved. The filefilter setting allows dynamic content so I could remove the filter activity completely, point the copy to the source folder and put the conditional logic in the filefilter’s dynamic content area but how would I get a reference to the file name to do the conditional checks?
Here is one solution:
Write array output as text to a .json in Blob Storage (or wherever). Here are the steps to make that work:
Copy Data Source:
Copy Data Sink:
Write the json (array output) to a text file that has the name of the files you want to copy.
Copy Activity Source (to get it from JSON to .txt):
Sink will be .txt file in your Blob.
Use that text file in your main copy activity and use the following setting:
This should copy over all the files that you identified in your Filter Activity.
I realize this is a work around, but really is the only solution for what you are asking. Otherwise there is no way to link a filter activity straight to a copy activity.
I want to perform some file level, field level validation checks on the dataset I receive.
Given below some checks which I want to perform and capture any issues into audit tables.
File Level Checks: File present, size of the file, Count of records matches to count present in control file
Field Level checks: Content in right format, Duplicate key checks, range in important fields.
I want to make this as a template so that all the project can adopt it, Is it good to perform these checks in ADF or in Databricks. If it is ADF any reference to example dataflow/pipeline would be very helpful.
Thanks,
Kumar
You can accomplish these tasks by using various Activities in Azure data factory pipeline.
To check the file existence, you can use Validation Activity.
In the validation activity, you specify several things. The dataset you want to validate the existence of, sleep how long you want to wait between retries, and timeout how long it should try before giving up and timing out. The minimum size is optional.
Be sure to set the timeout value properly. The default is 7 days, much too long for most jobs.
If the file is found, the activity reports success.
If the file is not found, or is smaller than minimum size, then it can timeout, which is treated like a failure by dependencies.
To count of matching records and assuming that you are using CSV, you could create a generic dataset (one column) and run a copy activity over whatever folders you want to count to a temp folder. Get the rowcount of the copy activity and save it.
At the end, delete everything in your temp folder.
Something like this:
Lookup Activity (Get's your list of base folders - Just for easy rerunning)
For Each (Base Folder)
Copy Recursively to temp folder
Stored procedure activity which stores the Copy Activity.output.rowsCopied
Delete temp files recursively.
To use the same pipeline repeatedly for multiple datasets, you can make your pipeline dynamic. Refer: https://sqlitybi.com/how-to-build-dynamic-azure-data-factory-pipelines/
Is there a way to find the oldest file in a directory using Azure Data Lake?
I had assumed I could use the meta data activity to get all the file names and dates (which I can). I then thought I could use the forEach to set two variables in the pipeline (Name & Date) with the values from the list if they were older than the current value of the variables. This does not work as all the files are processed in parallel. This really should not be this hard.
Yes, ForEach activity in Azure Data Factory works in parallel by default , but you change to work sequentially through checking Sequential option.
More details, you can refer to this documentation.
In our DataLake storage, we received unspecified amount of folders every day. Each of these folders contain at least one file.
Example of folders:
FolderA
|_/2020
|_/03
|_/12
|_fileA.json
|_/04
|_/13
|_fileB.json
FolderB
|_/2020
|_/03
|_/12
|_fileC.json
Folder C/...
Folder D/...
So on..
Now:
1. How do I iterate every folders and get the file(s) inside it?
I would also like to do 'Copy Data' from each of these files and make a single .csv file from it. What would be the best approach to achieve it?
This can be done with a single copy activity using wildcard filtering in the source dataset, as seen here: https://azure.microsoft.com/en-us/updates/data-factory-supports-wildcard-file-filter-for-copy-activity/
Then in the sink tab of the copy activity, select Merge Files in the Copy behavior as seen here:
If you have extra requirements, another way to do this is by using Mapping Dataflows. Mark Kromer explains a similar scenario here: https://kromerbigdata.com/2019/07/05/adf-mapping-data-flows-iterate-multiple-files-with-source-transformation/
Hope this helped!