synapse | A transparent service discovery framework | Microservice library

Jason, I'm sorry, hashbytes() is not supported against external tables.

Azure Synapse brings together data integration, enterprise data warehousing, and big data analytics and provides a unified experience in a single workspace. Dedicated SQL Pools are part of this workspace. However, Dedicated SQL Pool (Formerly SQL DW) will still be a stand-alone service in Azure for those who do not want all the other features of Synapse analytics.

Azure Synapse Analytics Spark pool supports - Only following magic commands are supported in Synapse pipeline : %%pyspark, %%spark, %%csharp, %%sql.

Python packages can be installed from repositories like PyPI and Conda-Forge by providing an environment specification file.

Steps to install python package in Synapse Spark pool.

Step1: Get the packages details like name & version from pypi.org

Note: (great_expectations) and (0.13.19)

Step2: Create a requirements.txt file using the above name and version.

Step3: Upload the package to the Synapse Spark Pool.

Step4: Save and wait for applying packages settings in Synapse Spark pools.

Step5: Verify installed libraries

To verify if the correct versions of the correct libraries are installed from PyPI, run the following code:

Using convolutions as FCs can be done (for example) with filters of spatial size (1,1) and with depth of the same size as the FC input size.

The resulting feature map would be of the same size as the input feature map, but each pixel would be the output of a "FC" layer whose weights are the weights of the shared 1x1 conv filter.

This kind of thing is used mainly for semantic segmentation, meaning classification per pixel. U-net is a good example if memory serves.

Also see this.
Also note that 1x1 convolutions have other uses as well.
paperswithcode probably some of the nets there use this trick.

You can use the Dynamic Management View (DMV) sys.pdw_table_distribution_properties in a dedicated SQL pool to determine if a table is distributed via round robin, hash or replicated, eg

The data flow requires a compute context, which is Spark. When you use a query in the transformation, that query will get executed from that Spark cluster, which essentially gets pushed down into the database engine for resolution.

Yes. PyODBC works with Synapse. It's not perfect but I use it.

https://docs.microsoft.com/en-us/azure/azure-sql/database/connect-query-python

Note that installing it can be a bit tricky. You need the Python package, but also the ODBC driver and the apt package unixodbc-dev.

Here is the part of my dockerfile that does it on Ubuntu 18.04

If you are writing to a dedicated SQL pool within the same Synapse workspace as your notebook, then it's as simple as calling the synapsesql method. A simple parameterised example in Scala, using the parameter cell feature of Synapse notebooks.

That type of query would work in the serverless SQL pool, as per the documentation on OPENROWSET. It would not work in a dedicated SQL pool.

If you are in Synapse Studio, try changing the Connect to option to Built-in, which is the serverless engine. Optionally create a database to store objects like external data sources, external tables and views in:

Another easy way to generate a working OPENROWSET statement would be via Synapse Studio > Data hub (the little cylinder on the right), Linked > double-click your datalake to navigate to the parquet file you want to query > right-click it > SELECT TOP 100 ...