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Category: Cloud

What Comes after the Well-Architected Framework Review

Published 2022-02-18 by Kevin Feasel

Ben Brauer takes us through the next step:

Congratulations! You’ve finished your Well-Architected Review of a workload, giving you a better understanding of where it could be fortified along the five pillars: Security, Reliability, Operational Excellence, Performance Efficiency and Cost Optimization. You have received Microsoft’s best practices as recommendations based on your answers to questions specific to each pillar.

The report (example below) shows a Well-Architected score for each pillar, as well as prioritized recommendations that allows for you to focus on biggest areas of impact. A great example is virtual machine right sizing. You can significantly lower your costs if you know which VM is best suited for your workload type.

By the way, if you have Azure resources, I highly recommend checking out the Well-Architected Framework assessment link there. It can take a very long time to go through because of just how many questions there are; that said, the results are also pretty specific and can be immediately helpful.

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Amazon RDS: Backups and Patching

Published 2022-02-18 by Kevin Feasel

Joey D’Antoni is not impressed:

While some services include other really useful features (for example the query data collected by the Azure SQL Database and Managed Instance platforms), I wanted to focus on the common value adds to PaaS systems across providers. I made the last two of these bold, because I feel like they are are the most important, especially in scenarios where the vendor doesn’t own the source to the applications. Like Amazon RDS for SQL Server.

Click through for Joey’s thoughts on the topic.

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Creating an Azure Integration Runtime

Published 2022-02-17 by Kevin Feasel

Andy Leonard builds out an Azure Integration Runtime:

Many Azure Data Factory developers recommend creating an Azure Integration Runtime for use with Mapping Data Flows. Why? One reason is you cannot configure all the options in the default AutoResolveIntegrationRuntime supplied when an Azure Data Factory instance is provisioned.

At the time of this writing, it’s not obvious how one creates an Azure Integration Runtime. You would think creating an integration runtime would begin with:

It turns out to be a little trickier than you might first expect.

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Replication in Azure DB for MySQL

Published 2022-02-16 by Kevin Feasel

Arun Sirpal explains how you can set up replication with Azure DB for MySQL:

No doubt there will be a need for you to split off your analytical queries from the main database for performance reasons.

If you have been following me in the past with Azure SQL DB you would use failover group read endpoints. With MySQL we would need to build a replica (read only) to another server. This uses MySQL’s native feature binlog replication which is great to hear. This form is asynchronous.

Read on to see how.

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Finding Public IP Addresses in Azure

Published 2022-02-15 by Kevin Feasel

Werner Rall is looking for public IPs:

Creating Resources in Azure is so simple for IT teams these days but finding all the public endpoints that could be visible to the internet can be challenging. Why do I need to understand which IP’s are exposed to the internet? Without a proper understanding of which Public IPs are available to the internet we cannot fully secure or protect our resources. In this article we will look at using the Azure Native Graph Explorer solution to query not only Virtual Machine Public IP Addresses but other resources containing IP addresses in our Azure Tenant. 

Read on to see how.

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The Azure SQL DB Serverless Compute Tier

Published 2022-02-10 by Kevin Feasel

Paul Randal explains why there is yet another tier of Azure SQL Database:

Over the past several years, I’ve helped numerous customers migrate SQL Server workloads to Azure SQL, including Azure SQL Database, Azure SQL Managed Instance, and Azure SQL Virtual Machines. 

In this article, I’ll explain some of the challenges of optimizing the compute cost for an Azure SQL Database deployment and review how the serverless compute tier can greatly simplify it.

Click through to see where the serverless tier fits and how you can make it work best in your environment.

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Getting Started with Azure Bicep

Published 2022-02-09 by Kevin Feasel

Jonathan D’Aloia looks at Azure Bicep:

This is going to be the first a few blogs in a series related to Azure BICEP. I will start the journey from the very beginning by showing you how to configure a local environment all the way to automating bicep deployments through multi-stage YAML Pipelines, covering how you can scale your infrastructure quickly and effectively.

In this blog, I will give a brief introduction to Azure BICEP and will also cover the easiest way to configure an environment locally ready to build and deploy your bicep templates.

Read on for the setup portion of the series.

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Run Spark within Azure ML Compute

Published 2022-02-04 by Kevin Feasel

James Nguyen makes an announcement:

Following the blog post on Turning AML compute into Ray and Dask , we’ve added a new exciting capability to run Spark within AML compute where Spark shares the same context with your ML code. The Spark version is 3.2.1 with support for Delta Lake and Synapse SQL read/write. This enables users of AML to perform powerful data transformation and even Spark ML within AML interactive notebook or in a job run. 

Traditionally, Azure ML integrates with Spark Synapse or external compute services via a pipeline step or better via magic command like %synapse, but the computing context is separate from your AML logic so you still need to run Spark in a separate step and persist the output to some storage and load it in your AML script.

With this approach, Spark is available right within your AML code whether it’s AML notebook, python script or pipeline step. It shares the common computing context and most of the cases you can just directly convert the Spark Dataframe to Pandas and Dask Dataframe without persisting first to an intermediary storage.

I’ll have to try this out to see if it makes up for their getting rid of the Spark-based curated environments last year.

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