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Category: Cloud

Delta Lake Support in Azure Stream Analytics

Emma An makes an announcement:

Delta Lake has gained popularity in recent times due to its unique features and advantages over traditional data warehouse and other storage formats. For those already using traditional data storage format or moving to a lakehouse architecture, Delta Lake can offer several compelling benefits that can further enhance the performance and capabilities of their data pipelines. Many Azure services are integrated with Delta Lake, and now you can use Azure Stream Analytics to write in Delta format.

In this blog, we will explain the native support of Delta Lake in Azure Stream Analytics, that can help users take their workload to the next level, providing a seamless and scalable solution for large-scale data processing and storage. It is easy to start, taking only a few clicks to create an end-to-end pipeline, and write to either a new or existing Delta table stored in Azure Data Lake Storage Gen2.

This is a nice addition to Stream Analytics and Emma shows two ways you can write out results in Delta Lake format.

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First Thoughts on Azure Hyperscale Serverless

Reitse Eskens shares some thoughts:

As some of you know, I’ve written a series of blog posts on Azure SQL Databases and there’s an accompanying session that I had the honour of presenting a number of times.
Now Azure keeps developing new offers and one of these went in public preview February 15th. An offer I hadn’t seen coming. You can read the introductory post here.

It’s the Azure Hyperscale Serverless option.

Read on for Reitse’s impressions from the preview. This wasn’t a torture test but did provide an overview of how to create and load data into the database. Reitse also calculates the cutoff point when you should switch from Serverless to traditional Hyperscale, so check that out as well.

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Building an Internal Load Balancer in Azure

Vaibhav Kumar balances the scales:

The Internal load balancer manages load for a private network with any inbound access from the public platform. As in the diagram below, the primary load balancer managing load from the internet is a public-type load balancer. But, the VMs communication to storage or database is managed through a type-internal load balancer.

Click through for a walkthrough of the process.

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Working with Postgres Extensions in Azure Cosmos DB

Sarah Dutkiewicz runs into an issue:

Problem: I installed PostGIS on my single-node cluster without issues. However, I scaled my cluster to 2 nodes afterwards. When I ran the query that uses ST_X and ST_Y from PostGIS, I got the following error:

ERROR:  type "public.geometry" does not exist
CONTEXT:  while executing command on private-w0.azure-cosmos-db-global-ug-demo.postgres.database.azure.com:5432

When I read the CONTEXT message, I realized by the w# reference that the worker nodes didn’t have PostGIS installed. When you scale the nodes – at least in this case, it doesn’t enable the extensions over there.

Read on to see how Sarah was able to resolve this issue.

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Azure Defender for SQL Overview

Deepthi Goguri looks at an Azure security offering:

Azure Defender for SQL, once you enable it will alert you for any SQL injection attacks, brute force attacks or any breached identities trying to access the data of your database. It also provides the vulnerability assessments. Vulnerability assessments give you alerts about the configurations of your database. If your database configuration is not following the standards of Azure, you will receive the alerts in the vulnerability assessment report.

You can enable the Azure Defender at the subscription level or at the Server level or at the resource level as well. Under the recommendations in the security center in the Azure portal, check for the Remediate security configuration. This will show if the Azure defender is configured properly.

I like Azure Defender for SQL, especially the advanced threat protection element. It’s based on IP address location and has caught me in different locations as I’ve traveled.

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Azure ML Overview

Sanil Mhatre gets us started with Azure Machine Learning:

The five-part series is designed to jump-start any IT professional’s journey in the fascinating world of Data Science with Azure Machine Learning (Azure ML). Readers don’t need prior knowledge of Data Science, Machine Learning, Statistics, or Azure to begin this adventure.

All you will need is an Azure subscription and I will show you how to get a free one that you can use to explore some of Azure’s features before I show you how to set up the Azure ML environment.

Part 1 is available now, with the other parts coming up soon. Even so, Part 1 is a big article on its own.

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Distributing Azure Costs by Cost Centers, Divisions, and Projects

Pranab Paul divvies up the costs:

While working on various customer and partner facing roles, I felt the necessity of a simple and flexible solution to align Azure Cost to the customer’s organizational structure. “Project Bose” is a fully operational prototype derived from the same thought process. This is a side project I am working on during my leisure time. I found various customers derived similar solutions in-house, and there are ISV solutions as well. But there are a few fundamental differences between “Project Bose” and all the other solutions I found. “Project Bose” has a flexible backend and hence any changes in organizational structure can easily be implemented on it without disruption. It is also independent of using Resource Tags, which gives it the opportunity to remain non-vulnerable to erroneous values injected intentionally or non-intentionally by IT-Ops.

Pranab didn’t include a link to the GitHub repo, so here it is.

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Data Protection in Azure

Deepthi Goguri enumerates the ways:

Data needs to be protected no matter where it lives, On-prem or in Azure. Data can be protected by using the encryption that Azure provides. What are the types of encryption we have in Azure?

In addition to specific encryption options, Deepthi also provides an overview of Dynamic Data masking and Ledger tables.

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Migrating from Elasticsearch to Azure Data Explorer

Bhaskar Kakaraparthy does a logging switcharoo:

This article is an extension to an existing article to migrate data from Elastic Search to Azure Data Explorer (ADX) using Logstash pipeline as a step-step-step guide.  In this article, we will explore the process involved in migrating data from one source (ELK) to another (ADX) and discuss some of the best practices and tools available to make the process as smooth as possible.

Using Logstash for data migration from Elasticsearch to Azure Data Explorer (ADX) was a smooth and efficient process. With the help of ADX output plugin & Logstash, I was able to migrate approximately 30TBs of data in a timely manner. The configuration was straightforward, and the data transfer with ADX output plugin was quick and reliable. Overall, the experience of using ADX output plugin with Logstash for data migration was positive and I would definitely use it again for similar projects in the future.

Read on to see how.

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