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Category: Cloud

Feature Engineering with Azure ML and Microsoft Fabric

Siliang Jiao, et al, talk architecture:

Feature engineering is the process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data. The extracted features are used for training the models that can predict values for relevant business scenarios. A feature engineering system provides the tools, processes, and techniques used to perform feature engineering consistently and efficiently. 

This article elaborates on how to build a feature engineering system based on Azure Machine Learning managed feature store and Microsoft Fabric. 

Click through to see how the pieces fit together.

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Tips for Using Powershell in Azure

Paul Harrison shares a few tips with us:

When I’m working with a new object in Azure I often don’t know where the information I care about is actually found in output. PowerShell makes it easy to navigate through objects, however it isn’t easy to get an overview of all properties available if they’re nested 5 levels deep. I like to use ConvertTo-JSON to help me get a general understanding for a new object and which properties are available and how to find them.

Read on for more information about converting to JSON and four other tips.

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Saving Money on Azure Storage

Rahul Miglani claws back some cash:

In today’s digital landscape, businesses are increasingly turning to cloud storage solutions to manage their data effectively. Microsoft Azure offers a wide range of storage options tailored to meet diverse business needs while optimizing costs. In this blog post, we’ll explore how organizations can leverage Azure storage options to achieve significant cost savings without compromising performance or reliability.

Read on for ten tips. A lot of it boils down to keeping just enough data and putting it in the right tier, but there’s a bit more to the story.

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Reading Azure SQL Audit Logs from Azure Storage

Matt Changchien covers a strange scenario:

When you read an Azure SQL Database audit log from Azure Storage using sys.fn_get_audit_file, you might encounter a situation where the audit log appears non-empty, but the query still returns an empty result. This discrepancy can be puzzling, especially when the official documentation doesn’t explicitly mention any limitations or requirements for the sys.fn_get_audit_file system function.

In this post, I will shed light on these limitations and demonstrate them to provide clarity.

Read on to see when this might happen and what you can do about it.

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Extended Events Tracing on Read Scale-Out Azure SQL MI

Kendra Little goes on a journey:

It took me more than half hour to figure out how to start an XEvents trace on a read-scale out instance of Azure SQL Managed Instance. It’s hard to monitor read scale-out instances, so tracing is desirable! I started with a simple trace of sql_statement_completed. Hopefully this saves other folks some time.

Click through for that process. The process seems a bit painful, to put it kindly.

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“License-Free” Managed Instance Requirements

Arun Sirpal reads the fine print:

This is the managed instance link feature; I really like this, if you know about Distributed AGs then you may know they are tricky to setup (well I found this) but Microsoft takes care of this out of the box.

The point of this quick blog is not how to set this up but the benefit of enabling the Managed Instance as “ license free “ via the hybrid failover rights option – do not forget about this.

Read on for the list of requirements.

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Using Databricks System Tables

Dustin Vannoy has a primer on system tables in Databricks:

Monitoring is important, so I’ve covered the topic a few times in the past. I’ve talked about collecting your Spark application logs and Spark metrics. These are a good way to track what is happening and what is going wrong as your code runs. In the video related to this post I focus on a different side of monitoring. The evolving capabilities offered by Databricks System Tables. I have some sample queries and links to help you get started and begin to get value from system tables. This will need to be updated (I’ll try) as new tables go into public preview status. So let’s discuss the questions I had when I first started researching this feature:
1) What do the Databricks system tables offer me for monitoring?
2) How much does this overlap with the application logs and metrics?

Click through for a video and a walkthrough.

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Maximizing the Savings for SQL Developers

Bob Ward is speaking my language:

Whether you build applications for SQL Server on-premises or in Azure, there are several options for you to develop or test for free, or with substantial cost savings. Some of these options you may be familiar with, but this post will help explain details and answer some questions Microsoft often gets to provide you a clear story.

The part in particular I want to focus on is Azure Dev/Test pricing. I give a talk on saving money in the cloud and this is one of the bits most people know very little about. With a Dev/Test subscription in Azure (not just a regular subscription that you call “dev” or “test”!), you can avoid Windows and SQL Server licensing fees, get discounts on certain services like Azure Application Services, and do a bit more with it. It’s something most people don’t know about and the documentation is a little confusing, but Bob does a good job of clarifying things.

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Azure SQL DB Serverless for Hyperscale now GA

Morgan Oslake has an announcement:

Optimizing resource allocation to achieve performance goals while controlling costs can be a challenging balance to strike especially for database workloads with complex usage patterns.  Azure SQL Database serverless provides a solution to help address these challenges, but until now the general availability of serverless has only been available in the General Purpose tier.  However, many workloads that can benefit from serverless may require greater performance and scale along with other capabilities unique to the Hyperscale tier.

We are pleased to announce the general availability of serverless auto-scaling for Hyperscale in Azure SQL Database.  The benefits of serverless and Hyperscale now come together into a single database solution.

Read on to see what this means for you and how it can change the billing strategy around Hyperscale.

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Generating Synthetic Data for Streaming in Microsoft Fabric

Sandeep Pawar builds out some data:

If you want to learn or demo Real Time Analytics in Microsoft Fabric, you will need a streaming data source. You can use the built-in samples to get started. But there are several data generators which you can use to create custom streaming sample datasets, Azure Stream Analytics data generator being one of them. You can see them here. In this blog, I will show how to set one up to use with Fabric Eventstream.

Read on for a step-by-step guide.

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