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Category: Cloud

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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From Cosmos DB to Dedicated SQL Pools via Synapse Link

Published 2022-02-04 by Kevin Feasel

Jovan Popovic shows off Azure Synapse Link:

At the time of writing this article, the dedicated SQL pool doesn’t have the ability to read data from CosmosDB/Dataverse using the Synapse link. There are scenarios where you would need to use CosmosDB data in your dedicated SQL pool, so you would need to find a way how to load data. In theory, you could create an ADF pipeline that reads data from CosmosDB or Dataverse and store data in the dedicated SQL pool as a target. This might be a problem if your Pipeline is reading data directly from CosmosDB account because it might impact both operational workload performance and cost. The analytical storage is the recommended location that you should use to fetch all data from CosmosDB/Dataverse.

In this post, I will describe how to use a two-step approach where you export your data using the serverless SQL pool via Synapse link into Azure Data Lake storage, and then load data into the dedicated SQL pool table. This process is shown in the following figure:

A couple of weeks back, I wrote about another method of doing this through the Spark pool. Now you have two options.

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Kibana Dashboards on Azure Data Explorer

Published 2022-02-03 by Kevin Feasel

Guy Reginiano has an announcement for us:

Elasticsearch and Kibana users can now easily migrate to Azure Data Explorer (ADX) while keeping Kibana as their visualization tool, alongside the other Azure Data Explorer experiences and the powerful KQL language.
A new version of K2Bridge (Kibana-Kusto free and open connector) now supports dashboards and visualizations, in addition to the Discover tab which was previously supported.

Click through to see how it works. I’m not the world’s biggest fan of Kibana by any stretch of the imagination but it’s nice to have this ability.

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Working with Notebooks in Azure ML

Published 2022-02-03 by Kevin Feasel

I have started a new series:

In the prior series, Low-Code Machine Learning with Azure ML, we saw how to get started with Azure Machine Learning in a fairly pain-free way, especially for developers getting started with machine learning. In this series, I will assume that you already know all of those details and instead, we’re going to go full-code.

There are a few different ways in which we can go full-code with Azure ML. Today, we’re going to look at the easiest of those methods: using Jupyter notebooks within Azure ML Studio.

Read on for the first post in the series.

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