Use pyspark to read data from azure fileshare

Hey guys does anyone have an idea how I can read qvd data from azure fileshare

I want to read these qvd convert to parquet then load the data using ADF to the container

But I am having trouble using synapse to read data from specified fileshare this is how the path is defined

base_path = f'abfss://fileshare@storagename.file.core.windows.net'
adf_path= f'{base_path}/WMOS/WMOS'

i get

Server failed to authenticate the request. Make sure the value of Authorization header is formed correctly including the signature

however same code but for blob in the same container works

First, make sure you have python between these version [3.6 - 3.9]

While creating apache spark pool select spark version in such a way that python version should be between as mentioned above.

Next, create a notebook in your synapse workspace add following code. To get data from Azure file Share , you need to download it locally and read into pandas then to spark dataframe.

Add below code blocks into you notebook.

pip install azure-storage-file-share==12.1.0 qvd

installs required package.

from qvd import qvd_reader
localpath="tmp.qvd"
connection_string = "Your_conn_string_to_storage_account"
share_name = "Your_file_share_name"
directory_name = "dir_name_in_fileshare"
file_name = "Airlines.qvd"

def  download_from_file_storage():
    share_client = ShareClient.from_connection_string(connection_string, share_name)
    file_client = share_client.get_file_client(directory_name + '/' + file_name)
    with  open(localpath, "wb") as  file:
        download_stream = file_client.download_file()
        file.write(download_stream.readall())
        
download_from_file_storage()

Function which downloads file into local file system.

from pyspark.sql.functions import col
df = qvd_reader.read(localpath)
s_df = spark.createDataFrame(df)
s_df = s_df.withColumn("AirlineId",col("%Airline ID")).drop(col("%Airline ID"))
display(s_df)

Here, reading qvd file from local and converting it to spark dataframe.

Next, using linked service writing that data to adls2 storage as parquet.

linkedServiceName_var = "adls2link"
spark.conf.set("fs.azure.account.auth.type", "SAS")
spark.conf.set("fs.azure.sas.token.provider.type", "com.microsoft.azure.synapse.tokenlibrary.LinkedServiceBasedSASProvider")
spark.conf.set("spark.storage.synapse.linkedServiceName", linkedServiceName_var)

raw_container_name = "data"
raw_storageaccount_name = "jgsadls2"
relative_path = "qvd_parquet"
path = f"abfss://{raw_container_name}@{raw_storageaccount_name}.dfs.core.windows.net/qvd_parquet"
s_df.write.parquet(path)

Before, executing this you need to create linked service to your adls storage.

Output:

and

If you want to use this in pipeline, add this notebook to pipeline with exit value as path and run it. Then take the path in output of the pipeline and use it further.