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Category: Cloud

Building a Docker Image with Docker Build Cloud

Published 2024-04-26 by Kevin Feasel

Andrew Pruski shows off Docker Build Cloud:

In a previous blog post we went through how to build a Docker container image from a remote (Github) repository.

Here we’re going to expand on that by actually building the image itself remotely, using Docker Build Cloud.

What we can do with Docker Build Cloud is instead of building the image locally and then having to push to a remote container registry (for example the Docker Hub), we can build remotely and then immediately push that image to the registry so that it is available for immediate use by say, our team members or deployment/testing pipelines.

Read on to see how it works.

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Copying Azure SQL Managed Instance Databases

Published 2024-04-25 by Kevin Feasel

Scott Klein performs a migration:

So, back to our customer. They essentially lifted and shifted their on-premises databases to Azure SQL Managed Instance and have been using it successfully for nearly two years. Again, this is awesome.

Last week they came to us and asked about reporting with Managed Instance. They were looking at data marts and data warehouses, but we needed more information. It turns out they have some people that just want the ability to query the databases, and potentially hook up Excel to these databases for data analysis.

The caveat is that the people I was talking to didn’t want to give the other group direct access to the production environment. Toootally get that. Yeah, like 100% get it. So, what are the options?

Read on for the solution Scott came up with.

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Three Layers of Azure Data Factory Framework Components

Published 2024-04-24 by Kevin Feasel

Martin Schoombee continues a series on orchestration in Azure Data Factory:

Before we dive into the details of the Data Factory pipelines, it is worth explaining the conceptual structure of my framework and its components. How it all fits together is important, and after reading the post on the metadata as well the pieces of the puzzle will hopefully start falling into place.

When I started thinking about what I’d like the framework to do, three conceptual layers started to emerge and we’ll review them from the bottom up:

Click through for the description of each layer.

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Cannot Open Backup Device with SQL Managed Instance and SAS Token

Published 2024-04-23 by Kevin Feasel

Sam Garth troubleshoots an issue:

On a recent case, a customer was trying to restore a database from a storage account using a SAS token when they received the below error.

An exception occurred while executing a Transact-SQL statement or batch.
(Microsoft.SqlServer.ConnectionInfo)

Additional information:
Cannot open backup device
‘https://storage.blob.core.windows.net/container/dbbackup_2024_03_21_121901.bak‘
Operating system error 86(The specified network password is not correct.).
RESTORE HEADERONLY is terminating abnormally. (Microsoft SQL Server, Error: 3201)

Read on for the troubleshooting steps Sam followed to solve the problem.

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Azure Regions and Pricing

Published 2024-04-18 by Kevin Feasel

Koen Verbeeck has a public service announcement:

Today I was having a nice discussion with some colleagues about Fabric and pricing/licensing came up. I mentioned an F2 is only around €250 a month, but a colleague said “no no, it’s over €300”.

There can be significant differences in prices for services based on region, not just for Microsoft Fabric, but also for a variety of services. This will depend on how new the hardware is, how much demand there is in the region, and a few other factors. Cloud Price does a good job of keeping track of VM pricing by region, and even tells you the cheapest region for each class of VM. For other services, you may have to trawl through Azure APIs and pricing pages to get the best deal.

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Metadata Tables and Azure Data Factory

Published 2024-04-17 by Kevin Feasel

Martin Schoombee brings back metadata tables:

The metadata that drives the execution within a framework is probably the most critical part. Going back to our analogy of building a house, the metadata would be the foundation. It is here where you are going to make some architectural decisions outside of which the framework cannot operate.

One such decision is how configurable or flexible you’d like the framework to be. In other words, how many attributes would you like to be dynamic and/or have the option to change during execution. It seems like an easy choice, and most engineers would lean towards “everything” or “as much as possible” as an answer. In reality however, the trade-off is complexity and the more dynamic you make the framework the more complicated it becomes. And you pay for the complexity later when you need to maintain or add new functionality to it.

Read on to see how it all fits together.

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Enhanced Patching for SQL Server on Azure VMs

Published 2024-04-17 by Kevin Feasel

Taryn Pratt has an update:

We are pleased to announce the GA release of enhanced patching capabilities for SQL Server on Azure VMs using Azure Update Manager. When you register your SQL Server on Azure VM with the SQL IaaS Agent extension, you unlock a number of feature benefits, including patch management at scale with Azure Update Manager.

Read on to see what this does, how you can set it up, and how you can migrate from the SQL Server IaaS agent extension’s automated patching service.

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Elastic Jobs for Azure SQL DB

Published 2024-04-17 by Kevin Feasel

Josephine Bush digs into Elastic Jobs:

I know if you are a SQL Server DBA using Azure SQL DB, you’ve been sorely missing the agent. Enter Elastic Jobs to help you schedule jobs more easily against Azure SQL DB. I will cover setting up and scheduling Elastic Jobs to execute Ola index maintenance. If you’ve used Elastic Jobs in the past, there are some very nice improvements with the recent GA release, so don’t feel discouraged if you didn’t like it in the past—it’s way better now!

Read on for a deep dive into Elastic Jobs.

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Documenting Table Columns with the Python SDK for Purview

Published 2024-04-16 by Kevin Feasel

Danaraj Ram Kumar breaks out the Python IDE:

There are several approaches to work with Microsoft Purview entities programmatically, especially when needing to perform bulk operations such as documenting a large number of tables and columns dynamically. 

This article shows how to use the Python SDK for Purview to programmatically document Purview table columns in bulk – assuming there are many tables and columns that needed to be automatically documented based off a reference tables – as in this example, the data dictionary maintained in Excel.

On the other hand, Purview REST APIs can be used to natively work with the REST APIs whereas the Python SDK for Purview is a wrapper that makes it easier to programmatically interacts with the Purview Atlas REST APIs in the backend.

Click through for sample code and explanations.

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