- #Microsoft azure storage explorer tutorial how to#
- #Microsoft azure storage explorer tutorial zip file#
- #Microsoft azure storage explorer tutorial manual#
This module is connected to the Script Bundle port of the **Execute Python script**.
#Microsoft azure storage explorer tutorial zip file#
The **Saved Datatasets** module at upper right is the zip file containing non-standard Python packages used for connection to Azure Blob Storage. Mwahlamlgallery,st=2016.&c.vHI%3D,compressedfileexample, This data is written in csv format.Īccount_name,sas_token,container_name,file_name At upper left, an **Input Data Manually** module contains the account name, SAS token, container name, and file name (blob name) to be used in this example.
#Microsoft azure storage explorer tutorial manual#
# Manual input of account access information # Using the Shared Access Signature token within Azure Machine Learning The latter part of this URL (everything after the quotation mark) is called the SAS token. Click the Create button to generate the signature.Ī window containing the new SAS URL will then open. Read permissions will be sufficient to access the contents of the blob storage container from within Azure ML. In the dialog box that appears, select an appropriate expiration date for the signature. # Generating an SAS with Microsoft Azure Storage ExplorerĪfter installing and launching (), navigate to the desired storage account and container using the directory tree at left, then right-click the container of interest and select the "Get Shared Access Signature" option. (For more information on SAS and generation options, please see Tamra Myers's () articles.) Several options for generating an SAS are available for this experiment, we used (), a Windows/Mac/Linux utility that is also handy for uploading files to Blob Storage. A Shared Access Signature (SAS) can be used in lieu of an account key to provide limited access to specific containers or blobs. Your Azure Blob Storage account name and key can be used to access files from within Python, but you may not wish to store this sensitive information within a shared or published Azure Machine Learning workspace. This file was uploaded as a dataset in Azure Machine Learning Studio. for more information), installing the `azure-storage` package via `pip`, and compressing the contents of the virtual environment's `site-packages` directory. It was generated by creating a new Python virtual environment in Cygwin (see () by David L. The experiment also includes a zip file containing the `azure-storage` Python package and its dependencies. (Please see Robin Shahan's () for more information on creating storage accounts.) The compressed file was uploaded as a `BlockBlob` to Azure Blob Storage using (), into a previously-created Azure storage account and container.
Sir Galahad of Camelot,I seek the Grail,Blue - no wait yellow The contents of the file in uncompressed form are: Our example input file, ``, is a comma-separated value (csv) file that has been compressed using `gzip`.
#Microsoft azure storage explorer tutorial how to#
This experiment demonstrates how to generate an SAS using Microsoft Azure Storage Explorer and employ the SAS to access and decompress a compressed csv file.
The **Execute Python Script** module can be used to access files in other formats, including compressed files and images, using a Shared Access Signature (SAS). The **Reader** module can be used to import selected file types from Azure Blob Storage into Azure Machine Learning Studio.
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