Import from Azure Table
Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.
Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.
- See information on moving machine learning projects from ML Studio (classic) to Azure Machine Learning.
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ML Studio (classic) documentation is being retired and may not be updated in the future.
This article describes how to use the Import Data module in Machine Learning Studio (classic), to import structured or semi-structured data from Azure tables into a machine learning experiment.
Applies to: Machine Learning Studio (classic) only
Similar drag-and-drop modules are available in Azure Machine Learning designer.
The Azure table service is a data management service in Azure that can store large amounts of structured, non-relational data. It is a NoSQL data store that accepts authenticated calls from inside and outside Azure.
Importing from Azure table storage requires that you choose one of two account types: a storage account that can be accessed by using a SAS URL, or a private storage account that requires login credentials.
How to import data from Azure tables
Use the Data Import Wizard
The module features a new wizard to help you choose a storage option, select from among existing subscriptions and accounts, and quickly configure all options.
Add the Import Data module to your experiment. You can find the module under Data Input and Output.
Click Launch Import Data Wizard and follow the prompts.
When configuration is complete, to actually copy the data into your experiment, right-click the module, and select Run Selected.
If you need to edit an existing data connection, the wizard loads all previous configuration details so that you don't have to start again from scratch
Manually set properties in the Import Data module
The following steps describe how to manually configure the import source.
For Data source, select Azure Table.
For Authentication type, choose Public (SAS URL) if you know that the information has been provided as a public data source. A SAS URL is a time bound access URL that you can generate by using an Azure storage utility.
Otherwise choose Account.
If your data is in a public blob that can be accessed by using a SAS URL, you do not need additional credentials because the URL string contains all the information that is needed for download and authentication.
In the Table SAS URI field, type or paste the full URI that defines the account and the public blob.
In a page accessible via SAS URL, data can be stored using only these formats: CSV, TSV, and ARFF.
If your data is in a private account, you must supply credentials including the account name and the key.
For Table account name, type or paste the name of the account that contains the blob you want to access.
For example, if the full URL of the storage account is
https://myshared.table.core.windows.net, you would type
For Table account key, paste the access key that is associated with the storage account.\
If you don’t know the access key, see the section, “View, copy and regenerate storage access keys” in this article: About Azure Storage Accounts.
For Table name, type the name of the specific table that you want to read.
TopN: Scan just the specified number of rows, starting from the top of the dataset.
By default, 10 rows are scanned, but you can increase or decrease that value by using the Rows count for TopN option.
If the data is homogenous and predictable, select TopN and enter a number for N. For large tables, this can result in quicker reading times.
ScanAll: Scan all rows in the table.
If the data is structured with sets of properties that vary based on the depth and position of the table, choose the ScanAll option to scan all rows. This ensures the integrity of your resulting property and metadata conversion.
Indicate whether you want the data to be refreshed each time the experiment is run. If you select the Use cached results option (the default) the Import Data module will read data form the specified source the first time the experiment is run, and thereafter cache the results. If there are any changes to the parameters of the Import Data module, the data is re-loaded.
If you deselect this option, the data will be read from the source each time the experiment is run, regardless of whether the data is the same or not.
This section contains implementation details, tips, and answers to frequently asked questions.
How can I avoid re-loading the same data unnecessarily?
If your source data changes, you can refresh the dataset and add new data by re-running Import Data. However, if you don't want to re-read from the source each time you run the experiment, select the Use cached results option to TRUE. When this option is set to TRUE, the module checks whether the experiment has run previously using the same source and same input options, and if a previous run is found, the data in the cache is used, instead of re-loading the data from the source.
Can I filter data as it is being read from the source?
The Import Data module does not support filtering as data is being read. The exception is reading from data feeds, which sometimes allow you to specify a filter condition as part of the feed URL.
However, you can change or filter data after reading it into Machine Learning Studio (classic):
- Use a custom R script to change or filter data.
- Use the Split Data module with a relative expression or a regular expression to isolate the data you want, and then save it as a dataset.
If you find that you have loaded more data than you need, you can overwrite the cached dataset by reading a new dataset, and saving it with the same name as the older, larger data.
How does Import Data handle data loaded from different geographical regions?
If the blob or table storage account is in a different region from the compute node used for the machine learning experiment, data access might be slower. Further, you are charged for data ingress and egress on the subscription.
Why are some characters in my table not displayed correctly?
Machine Learning supports UTF-8 encoding. If your table uses another encoding, the characters might not be imported correctly.
Are there any prohibited characters or characters that are changed during import?
If attribute data contains quotation marks or escaped character sequences, they are handled by using the rules for such characters in Microsoft Excel. All other characters are handled by using the following specifications as a guideline: RFC 4180.
|Data source||List||Data Source Or Sink||Azure Blob Storage||Data source can be HTTP, FTP, anonymous HTTPS or FTPS, a file in Azure BLOB storage, an Azure table, an Azure SQL Database, an on-premises SQL Server database, a Hive table, or an OData endpoint.|
|tableAuthType||Account||Specify whether the data is in a public container accessible via SAS URL, or is in a private storage account that requires authentication for access.|
Public or SAS - Public storage options
|Rows to scan for property names via SAS||integer|
|Rows count for TopN via SAS|
Account - Private storage options
|Table account name|
|Table account key||any||SecureString|
|Rows to scan for property names||TopN
|Rows count for TopN||any||integer|
|Results dataset||Data Table||Dataset with downloaded data|
|Error 0027||An exception occurs when two objects have to be the same size, but they are not.|
|Error 0003||An exception occurs if one or more of inputs are null or empty.|
|Error 0029||An exception occurs when an invalid URI is passed.|
|Error 0030||an exception occurs in when it is not possible to download a file.|
|Error 0002||An exception occurs if one or more parameters could not be parsed or converted from the specified type to the type required by the target method.|
|Error 0009||An exception occurs if the Azure storage account name or the container name is specified incorrectly.|
|Error 0048||An exception occurs when it is not possible to open a file.|
|Error 0046||An exception occurs when it is not possible to create a directory on specified path.|
|Error 0049||An exception occurs when it is not possible to parse a file.|
For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.
For a list of API exceptions, see Machine Learning REST API Error Codes.
Import from Web URL via HTTP
Import from Hive Query
Import from Azure SQL Database
Import from Azure Blob Storage
Import from Data Feed Providers
Import from On-Premises SQL Server Database