A Fine Slice Of SQL Server
Dimitry Furman has a new announcement:
Reliable, in-depth, and at-scale monitoring of database performance has been a long-standing top priority for SQL customers. Today, we are pleased to announce the public preview of database watcher for Azure SQL, a managed database monitoring solution to help our customers use Azure SQL reliably and efficiently.
Click through to see what it offers and what’s on the roadmap for this product.
Comments closedAravind Mahadevan shares information on a new bit of functionality:
We’re excited to announce that Extensible Key Management (EKM) using Azure Key Vault in SQL Server on Linux is now generally available from SQL Server 2022 CU12 onwards, which allows you to manage encryption keys outside of SQL Server using Azure Key Vaults.
In this blog post, we’ll explore how to leverage Azure Key Vault as an EKM provider for SQL Server on Linux.
Read on to see how to set this up.
Comments closedTanayankar Chakraborty asks, what’s in a name?:
An issue was reported recently where the customer complained that in their cost analysis report of their Azure SQL DBs, the db name appears appended with a comma(,) and a number. While they agreed with the DB name in the report, they didn’t understand the number after the comma and its significance. This is how the cost analysis report looks like:
Click through for a redacted version of the report, showing an example of the database in question, as well as an explanation of what this number means.
Comments closedSiliang 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.
Comments closedPaul 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.
Comments closedRahul 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.
Comments closedMatt 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.
- Use Auditing to analyze audit logs and reports – Azure SQL Database & Azure Synapse Analytics | Micr…
- sys.fn_get_audit_file (Transact-SQL) – SQL Server | Microsoft Learn
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.
Comments closedKendra 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.
Comments closedArun 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.
Comments closedDustin 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.
Comments closed