A Fine Slice Of SQL Server
Vicent-Philippe Lauzon makes an announcement:
We recently made our new Kusto data connection available in public preview: Cosmos DB to Azure Data Explorer Synapse Link.
This does look like a marketing-heavy announcement but the short version is that you can ingest data from Cosmos DB into Data Explorer pools via Synapse Link rather than creating your own ETL process. The previous Cosmos DB connector for Synapse Link tied to a dedicated SQL pool.
Comments closedHugo Calzada Martin recovers from an oopsie:
If we have a Customer-managed TDE (Transparent Data Encryption) and we delete by mistake the entire “Azure Key Vault” or just the “Key” object, the database will be inaccessible:
Read on for Hugo’s response as well as how to recover from this scenario. There are examples of how to do this in the Portal, in Powershell, and the CLI.
Comments closedFor this instance, we will setup an alert and action to determine and send out a notification when a Virtual machine has been stopped and also could be restarted whenever such conditions has met.
1. Firstly, Go to search pane on the Azure portal search monitor, click on alert inside monitor and create an alert rule. Further, specify a scope for what you want to setup alert. On this occasion, I am setting it for virtual machine.
Read on to learn more about what Azure Monitor does, as well as the steps to set up an alert and an action.
Comments closedAaron Bertrand gets into the whats and wherefores:
Like many companies, Stack Overflow is trying to get out of the business of running our architecture in our own data centers; instead, we want to offload some of the more mundane parts of system administration to a cloud service offering like Azure.
I’m going to cut to the chase for the purpose of this article and concede we’ve already decided on Azure for the majority of our infrastructure and, most importantly to me, our databases.
Click through to learn what their plan is and why Aaron & co went that particular route.
Comments closedTomaz Kastrun shares additional resources:
If you want to immerse in further reading and additional knowledge, here are some links. Here are just couple.
That’ll do it. With 24 posts in 24 days, Tomaz wraps up an excellent advent series. There’s a lot more to Azure ML but this was a great overview of AML if you’re new to the product.
Comments closedMeagan Longoria links in a script:
In early 2022, Microsoft released a new activity in Azure Data Factory (ADF) called the Script activity. The Script activity allows you to execute one or more SQL statements and receive zero, one, or multiple result sets as the output. This is an advantage over the stored procedure activity that was already available in ADF, as the stored procedure activity doesn’t support using the result set returned from a query in a downstream activity.
However, when I went to find examples of how to reference those result sets, the documentation was lacking.
Click through as Meagan corrects a gap in documentation.
Comments closedTomaz Kastrun is winding down an advent of Azure ML. Day 22 covers batch scoring:
Batch endpoints are a great and simple way to run inference over large volumes of data. They simplify the process of hosting your models for batch scoring.
Click through to see how it all works.
Comments closedTomaz Kastrun nears the end of the Azure ML advent. Day 20 covers package requirements and other niceties:
When creating notebooks, it is always a good way to have the dependencies included. Whether it is a particular version of a package, a separate script file or an installation requirement.
Selecting an environment or kernel can be an issue if it is not correctly initiated with the code. And you can also check the kernels with a simple python code:
Day 21 looks at the Azure CLI and running code from within a compute instance terminal:
Comments closedUsing Azure CLI can help you progress faster, make repetitve tasks automated and even use the GIT integration, for faster and better collaboration.
So we have created a YAML file on Day20 and we can use it also with Azure CLI to create an environment.
Xu Jiang announces a public preview:
The Stream Analytics no-code editor is a drag and drop design tool that helps customers to develop the Stream Analytics jobs without writing a single line of code. The experience provides a canvas that allows you to connect to input sources to quickly see your streaming data. Then you can transform and preview it before writing to your destination of choice in Azure. To learn more, see No-code stream processing through Azure Stream Analytics | Microsoft Learn.
Read on to see how you can capture and process data into Delta Lake format via their designer.
Comments closedTomaz Kastrun continues an advent of Azure ML. Day 18 takes us through feature exploration:
Azure Machine Learning is also a great tool to do ordinary statistical analysis, graph plotting and everything that goes along.
Let’s get an open dataset, that is available on UCI Machine Learning repository and import it in the pandas dataframe.
Day 19 picks up with feature engineering:
Comments closedYesterday we have shown, that statistical analysis and all bolts and whistles can be done super simple in Azure machine learning. Today we will continue with feature engineering and modelling.
So, what is feature engineering? Is a general process and can involve both feature construction: adding new features from the existing data, and feature selection: choosing only the most important features for improving model performance, reducing data dimensionality, doing log-transformation, removing outliers, to do scaling (normalisation, standardisation), imputations, general transformation (and others, as polynomial), variable creation, variable extraction and so on.