Whenever I use ADF copy activity with Blob as source/sink, ADF creates an empty file named after the directory of the sink Blob.
For instance, if I want to copy from input/file.csv to process/file.csv, the copy happens but I also have a blob called "process" with size 0 byte created each time.
Any idea why?
Source
Sink
Firstly, I would suggest you optimize you pipeline copy active settings.
Since you are copying one file from one container/folder to another, you can directly set the source file with parameter. Wildcard path expression *.csv is usually used for folder the same type of files.
You can test again and check if the empty file exist again.
HTH.
This happens if you have a storage ADLS gen2 but you have not enabled the Hierarchical namespace and you select the ADLS gen2 while defining your Linked Service and Dataset. A quick fix for this is use Azure Blob Storage when defining LS and DS.
Related
I need to transfer around 20 CSV files inside a folder named ActivityPointer in an azure blob storage container to Azure SQL database in a single data factory pipeline, but ActivityPointer contains 20 CSV files and another folder named snapshots inside it. So when I try to create a pipeline and give * to select all the CSV files inside ActivityPointer it includes the snapshots folder too, which should not be included. Is there any possibilities to complete this task. Also I can't create another folder to transform the snapshots folder into it. What can I do now? Anyone can please help me out.
Assuming you want to copy all CSV files within ACtivityPointer folder,
You can use wildcard expression as below :
you can provide path till Active folder and than *.csv
Copy data is also considering the inner folder while using wildcards (even if we use .csv in wildcard file path). So, we have to validate whether it is a file or folder. Please look at the following demonstration.
First use Get Metadata on the required folder with field list as Child items. The debug output will be:
Now use this to iterate through child items using For each activity.
#activity('Get Metadata1').output.childItems
Inside for each, use if condition activity to check whether the current item is a file or not. Use the following condition.
#equals(item().type,'File')
When this is true, you can use copy data to complete copying the file to target table (Ignore the false case). I have create file_name parameter in my source dataset passing its value as #item().name().
This will help you to achieve your requirement. The following is the debug output. I have 4 files and 1 folder. The folder will be ignored, and the rest will be copied into the target table.
I am trying to copy files from an FTP to Blob , the probleme is that my pipeline copies all files including the old ones. I would like to do an incremental load by only copying new files. how do U configure this. BTW in my FTP dataset the parameters ModifiedStartDate and ModifiedEndDate are not showing. I would also like to configure theses dates dynamically
Thank you!
There's some work to be done in Azure Data Factory to get this to work. What you're trying to do, if I understand correctly, is to Incrementally Load New Files in Azure Data Factory. You can do so by looking up the latest modified date in the destination folder.
In short (see the above linked article for more information):
Use Get Metadata activity to make a list of all files in the Destination folder
Use For Each activity to iterate this list and compare the modified date with the value stored in a variable
If the value is greater than that of the variable, update the variable with that new value
Use the variable in the Copy Activity’s Filter by Last Modified field to filter out all files that have already been copied
I have 4 csv files in Azure blob storage, with same metadata, that i want to process. How can i add them to the datacatalog with a single name in Kedro.
I checked this question
https://stackoverflow.com/questions/61645397/how-do-i-add-many-csv-files-to-the-catalog-in-kedro
But this seems to load all the files in the given folder.
But my requirement is to read only given 4 from many files in the azure container.
Example:
I have many files in azure container in which are 4 transaction csv files with names sales_<date_from>_<date_to>.csv, i want to load these 4 transaction csv files into kedro datacatalog under one dataset.
For starters, PartitionedDataSet is lazy, meaning that files are not actually loaded until you explicitly call that function. Even if you have 100 CSV files that get picked up by the PartitionedDataSet, you can select the partitions that you actually load/work with.
Second, what distinguishes these 4 files from the others? If they have a unique suffix, you can use the filename_suffix option to just select them. For example, if you have:
file_i_dont_care_about.csv
first_file_i_care_about.csv
second_file_i_care_about.csv
third_file_i_care_about.csv
fourth_file_i_care_about.csv
you can specify filepath_suffix: _file_i_care_about.csv.
Don’t think there’s a direct way to do this , you can add another subdirectory inside the blob storage with the 4 files and then use
my_partitioned_dataset:
type: "PartitionedDataSet"
path: "data/01_raw/subdirectory/"
dataset: "pandas.CSVDataSet"
Or in case the requirement of using only 4 files is not going to change anytime soon ,you might as well pass 4 files in the catalog.yml separately to avoid over engineering it.
I have been manually partitioning files with pandas (creating an index or multi-index and then writing a separate parquet file for each index in a loop) to Azure Blob.
However, when reading the docs for pyarrow, I see that it is possible to create a 'dataset' which includes a folder structure for partitioned data. https://arrow.apache.org/docs/python/parquet.html
The example for the Monthly / daily folder is exactly what I am trying to achieve.
dataset_name/
year=2007/
month=01/
0.parq
1.parq
...
month=02/
0.parq
1.parq
...
month=03/
...
year=2008/
month=01/
...
fs = pa.hdfs.connect(host, port, user=user, kerb_ticket=ticket_cache_path)
pq.write_to_dataset(table, root_path='dataset_name',
partition_cols=['one', 'two'], filesystem=fs)
Can I do this with Azure Blob (or Minio which uses S3 and wraps over my Azure Blob storage)? My ultimate goal is to only read files which make sense for my 'query'.
Just per my experience and based on your current environment Linux on Azure VM, I think there are two solutions can read partition parquet files from Azure Storage.
Follow the section Reading a Parquet File from Azure Blob storage of the document Reading and Writing the Apache Parquet Format of pyarrow, manually to list the blob names with the prefix like dataset_name using the API list_blob_names(container_name, prefix=None, num_results=None, include=None, delimiter=None, marker=None, timeout=None) of Azure Storgae SDK for Python as the figure below, then to read these blobs one by one like the sample code to dataframes, finally to concat these dataframes to a single one.
Try to use Azure/azure-storage-fuse to mount a container of Azure Blob Storage to your Linux filesystem, then you just need to follow the document section Reading from Partitioned Datasets to read the Partitioned Dataset locally from Azure Blob Storage.
I have an Oracle instance running on a stand alone EC2 VM, I want to do two things.
1) Copy the data from one of my Oracle tables into a cloud directory that can be read by DynamoDB. This will only be done once.
2) Then daily I want to append any changes to that source table into the DynamoDB table as another row that will share an id so I can visualize how that row is changing over time.
Ideally I'd like a solution that would be as easy as pipeing the results of a SQL query into a program that dumps that data into a cloud files system (S3, HDFS?), then I will want to convert that data into a format that can be read with DynamoDB.
So I need these things:
1) A transport device, I want to be able to type something like this on the command line:
sqlplus ... "SQL Query" | transport --output_path --output_type etc etc
2) For the path I need a cloud file system, S3 looks like the obvious choice since I want a turn key solution here.
3) This last part is a nice to have because I can always use a temp directory to hold my raw text and convert it in another step.
I assume the "cloud directory" or "cloud file system" you are referring to is S3? I don't see how it could be anything else in this context, but you are using very vague terms.
Triggering the DynamoDB insert to happen whenever you copy a new file to S3 is pretty simple, just have S3 trigger a Lambda function to process the data and insert into DynamoDB. I'm not clear on how you are going to get the data into S3 though. If you are just running a cron job to periodically query Oracle and dump some data to a file, which you then copy to S3, then that should work.
You need to know that you can't append to a file on S3, you would need to write the entire file each time you push new data to S3. If you are wanting to stream the data somehow then using Kenesis instead of S3 might be a better option.