DULY | Distance-based Unsupervised Learning in pYthon | Machine Learning library

The issue was due to concurrency: two threads were calling my code above, and therefore sometimes one thread would not have time to send the zero-length packet right after its packet.

So this actually seems to work:

You would have to create a MASTER KEY first after creating a SQL POOL from Azure Portal. You can do this by connecting through SSMS and running a T-SQL command. If you now try to read from a table in this pool, you would see no error in databricks.

Going through these docs, Required Azure Synapse permissions for PolyBase

As a prerequisite for the first command, the connector expects that a database master key already exists for the specified Azure Synapse instance. If not, you can create a key using the CREATE MASTER KEY command.

Next..

Is there any way by which a database master key would restrict read operations, but not write? If not, then why could the above issue be occuring?

If you notice, while writing to SQL, you have configured temp directory in the storage account. Azure Synapse connector automatically discovers the account access key set and forwards it to the connected Azure Synapse instance by creating a temporary Azure database scoped credential

Creates a database credential. A database credential is not mapped to a server login or database user. The credential is used by the database to access to the external location anytime the database is performing an operation that requires access.

And from here Open the Database Master Key of the current database

If the database master key was encrypted with the service master key, it will be automatically opened when it is needed for decryption or encryption. In this case, it is not necessary to use the OPEN MASTER KEY statement.

When a database is first attached or restored to a new instance of SQL Server, a copy of the database master key (encrypted by the service master key) is not yet stored in the server. You must use the OPEN MASTER KEY statement to decrypt the database master key (DMK). Once the DMK has been decrypted, you have the option of enabling automatic decryption in the future by using the ALTER MASTER KEY REGENERATE statement to provision the server with a copy of the DMK, encrypted with the service master key (SMK).

But...from

For SQL Database and Azure Synapse Analytics, the password protection is not considered to be a safety mechanism to prevent a data loss scenario in situations where the database may be moved from one server to another, as the Service Master Key protection on the Master Key is managed by Microsoft Azure platform. Therefore, the Master Key password is optional in SQL Database and Azure Synapse Analytics.

As you can read from above, I tried to repro and yes, after you first create a SQL POOL from Synapse Portal, you can write to a table from databricks directly but when you try to read the same you get the exception.

Spark is writing the data to the common blob storage as parquet file and later synapse uses COPY statement to load these to given table. And when reading data from synapse dedicated SQL pool table, Synapse is writing the data from dedicated sql pool to common blob storage as parquet file with snappy compression and then this is read by Spark and displayed to you.

We are just setting the blob storage account key and secret in the config for the session. And using forwardSparkAzureStorageCredentials = true Synapse connector is forwarding storage access key to Azure synapse dedicated pool by creating Azure database scoped credential.

Note: You can .load() into data frame without exception but when you try and use display(dataframe) the exception pops.

Now considering if MASTER KEY exists, connecting to your sql pool db, you can try the below,

Examples: Azure Synapse Analytics

With @url http://www.amazon.com/s?field-keywords=selfish+gene I get also error 503.

Probably it is very old example - it uses http but modern pages use https - and amazone could rebuild page and now it has better system to detect spamers/hackers/bots and block them.

If I use @url http://toscrape.com/ then I don't get error 503 but I still get other error FAILED because it needs some code in parse()

@scrapes Title Author Year Price means it has to return item with keys Title Author Year Price

You'll want to use a conditional type. Instead of noting val as string | number, mark it as a generic and then use a conditional type to note that the return type depends on the type of val.

We have multi-image that works in a way similar to androids DPI level. They don't use the iOS convention since we support more device resolutions than iOS. This works both in the CSS and designer. See the developer guide on multi-image for more details.

The solution proposed by @MinhazKazi in the comments is correct, but he forgot some apostrophes.

I was going to delete this question but decided to leave it here with an answer for the benefit of anyone else trying to do something similar.

The key point to note is that the call to Accumulator.addStream does just that - it furnishes a stream to be listened to, no actual data to be read. What you do next is this

The directory name synapse/workspace01/parquet/dim_mydim has an _ in dim_mydim, can you try replacing the underscore, or maybe you can use dimmydim to test whether it works.

To assign a Built-In policy, I would suggest referencing the desired Policy Definition as a data source. This way, you do not need to declare/create a new Policy Definition in your Terraform code. (Although alternatively, you could just place the Definition ID for the Built-In Policy as the value for policy_definition_id in the azurerm_policy_assignment resource block).

Here is an example of referencing a Built-In Policy Definition as a Data source in Terraform.

Below is an example of what your Terraform would look like to take a Built-In Policy Definition from the Portal and assign to a management group.