A Fine Slice Of SQL Server
The Log Replay Service (LRS) is a new Azure service that allows you to migrate your databases from SQL Server on-premises, SQL Server on Azure Virtual Machines, Amazon EC2, Amazon RDS for SQL Server, or Google Compute Engine to Azure SQL Managed Instance. LRS is a free cloud service that uses log shipping technology to enable custom migrations of databases from SQL Server 2008 through 2022.
Read on for some configuration options and tips on how to use the service.
Comments closedDany Hoter shares some tips on creating star schema models with Azure Data Explorer:
Relationships between DQ tables are created as M:M by default. This is not a problem and even recommended with single direction.
Read on for several tips. What’s interesting as I read this is just how radically different the advice is for ADX utilization versus Power BI utilization, such as using strings to join dimensions to facts. That would be heresy in a Kimball-style model and is a common cause for slow-down in Power BI. Yet that’s the recommendation here for working with ADX, unless I’m misunderstanding Dany’s post.
Comments closedMatt Eland doesn’t want to break the bank:
Let’s continue my recent trend in exploring pricing tips for the various parts of AI and Machine Learning on Azure with a dive into Azure Cognitive Search.
Sometimes confused with the AI offerings of Azure Cognitive Services, the entirely different Azure Cognitive Search is a rich service that allows you to index a variety of files and documents, extract meaning from those documents, and provide rich search results to users.
In this article we’ll explore the pricing structure of Azure Cognitive Search and highlight some things you should be aware of as you plan and develop your Cognitive Search resources.
Read the whole thing if you’re thinking of using Azure Cognitive Search. It’s a good service and I think the pricing model is fairly straightforward, though there are always nuances to these things.
Comments closedA tag is a user-defined label that can be attached to a Snowflake object, such as a database, table, or column. Tags can categorize objects based on any criteria that you choose, such as sensitivity, business unit, project, or owner. Once tags have been applied, you can use them to control access to the tagged objects, track usage and costs, and apply policies and rules.
Now let’s apply tagging to a specific use case: identifying sensitive customer data. For example, let’s assume that you have a table in Snowflake called “customers” that contains customer information, including their addresses. We want to categorize the “address” column as sensitive so that we can apply data protection policies and controls.
Click through for a few examples of how to create tags, apply tags to database objects, and review tagged objects.
Comments closedLaith Ayesh has a script for us:
In a few scenarios, you might need to scale multiple databases on a logical server (not part of elastic pool) at once, the azure portal only allows you to scale each database individually. This can be achieved using the following PowerShell script:
just modify the parameters like SubID, the resource group and server name and then pick the service tier you want and run the script:
Click through for the Powershell script and an important note.
Comments closedJoji Varghese gives us a hand:
In an Azure Kubernetes (AKS) cluster, Pods can access physical storage resources such as disks or volumes using Persistent Volumes (PV). To use these resources, Pods need to make a Persistent Volume Claim (PVC), which requests a specific amount of storage from a storage class. This claim can then be matched to an available Persistent Volume. Azure offers several storage solutions that can be used to provision Persistent Volumes in an AKS cluster.
This article will provide real-world guidance on securely using Container Storage Interface (CSI) drivers to provision Azure File Shares and Azure Blob storage in an AKS cluster.
If you’re looking at setting up Azure Kubernetes Service, give this a review.
Comments closedVaidy Kalpathy takes us through a bit of dimensional modeling in AWS Redshift:
Populating an SCD dimension table involves merging data from multiple source tables, which are usually normalized. SCD tables contain a pair of date columns (effective and expiry dates) that represent the record’s validity date range. Changes are inserted as new active records effective from the date of data loading, while simultaneously expiring the current active record on a previous day. During each data load, incoming change records are matched against existing active records, comparing each attribute value to determine whether existing records have changed or were deleted or are new records coming in.
Click through for the article.
Comments closedWolfgang Strasser wants to try Microsoft Purview but doesn’t want to break the bank:
And my reaction was – Nice, very nice.. I can try and create Microsoft Purview instances for free and test new features..
BUT: I wanted to be sure and check, how much metadata (sources, scan results, data assets, classifications) can fit into 1 MB of metadata.
Read on for Wolfgang’s test, as well as the full set of costs around trying out Purview.
Comments closedRyan Majidimehr has a new round-up for us:
Azure Synapse Runtime for Apache Spark 3.3 has been in Public Preview since November 2022. We are excited to announce that after notable improvements in performance and stability, Azure Synapse Runtime for Apache Spark 3.3 now becomes Generally Available and ready for production workloads.
The essential changes include features that come from upgrading Apache Spark to version 3.3.1, Delta Lake to version 2.2.0, and Python to 3.10.
This month’s set of changes isn’t quite as big as some prior months, though there are a couple items of great importance to make up for it.
Comments closedReitse Eskens ran out of money on our behalf:
In one of my last blogs, I wrote about my first encounter with the Azure Hyperscale Serverless offering. Now it’s time to dig a bit deeper and what it’s up to.
Disclaimer. Azure Hyperscale Serverless is in preview and one of the things that isn’t active yet, is the auto shutdown. This means that it will stay online 24/7. And bill you for every second it’s online. In my case, this meant that my Visual Studio credits ran out and I couldn’t use my Azure subscription anymore. Keep it in mind when testing this out, especially if your credit card is connected to said subscription.
Click through to see what Reitse was able to do in the meantime, before those Azure credits ran out for the month.
Comments closed