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Category: Cloud

Automatically Stopping Data Explorer Clusters

Gabi Lehner has good news for us:

Azure Data explorer team is constantly focused on reducing COGS and making sure users are paying only for value they are getting.

As part of this initiative, we’re now adding a new automatic capability to stop unused clusters.

In case, you created a cluster and did not ingest any data to it or even if you ingested data and later, you’re not running any queries or ingesting new data for days, we will automatically stop that cluster.

I have two thoughts on this. First, good. Frankly, every cloud service should have automatic pausing unless it makes sense not to—that is, pausing should be the default, not a feature you add later. This is especially true for expensive data processing services.

Second, based on the description, I think I’d like a little more control over this, in terms of how long we go before auto-stop kicks off. It’s ten days, which is a reasonable + large number, but other numbers could make just as much sense for a given user. I like the idea that we see in Databricks and in Azure Synapse Analytics Spark pools: give me a reasonable default, but let me change it in case the reasonable default can’t cut it for some reason.

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Reference to Database Not Supported in this Version of SQL Server

Diego Nieto-Arroyo troubleshoots an issue:

In this article I will show how to resolve and overcome errors while attempting to query a system tables or any table for that matter, via Linked Servers. The error seen below is the result of the issue we encountered.

Msg 40515, Level 16, State 2, Line 1

Reference to database and/or server name in ‘xxxx.sys.sp_tables_info_90_rowset_64’ is not supported in this version of SQL Server.

Read on to see what causes this issue and how you can resolve it.

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Building Data Mesh Edges in Azure

Paul Andrew continues a series on data mesh in Azure:

Anyway, moving on. In part 2 of this blog series, keeping the same focus from part 1, with the first data mesh principal. Let’s take our nodes and start thinking about the edges. The data mesh – data product interfaces… Enter my Azure Resource Group with arms/antenna type things, seen on the right 

Caveat: as you may have already gathered, I’m going to use the terms edge and interface a lot in this post. The meaning in the context of the data mesh is the same. Nodes with edges, nodes with interfaces.

Click through for more detail.

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Spark in Azure Databricks

Tomaz Kastrun starts winding down a series on Apache Spark. Part 22 covers Spark in Azure Databricks:

Azure Databricks is a platform build on top of Spark based analytical engine, that unifies data, data manipulation, analytics and machine learning.

Databricks uses notebooks to tackle all the tasks and is therefore made easy to collaborate. Let’s dig in and start using a Python API on top of Spark API.

Read on for that primer.

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Azure App Service Source Code Breach

Catalin Cimpanu reports on a security problem:

Microsoft has notified earlier this month a select group of Azure customers impacted by a recently discovered bug that exposed the source code of their Azure web apps since at least September 2017.

The vulnerability was discovered by cloud security firm Wiz and reported to Microsoft in September. The issue was fixed in November, and Microsoft has spent the last few weeks investigating how many customers were impacted.

Click through for the full report, with the upshot that if you run Azure App Services on Linux, you were probably affected.

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Deploying SQL Server to Azure Container Instance via ARM

Rajendra Gupta builds an ARM template:

The Azure Resource Manager (ARM) template is a JavaScript Object Notation (JSON) file for deploying Azure resources automatically. You can use a declarative syntax to specify the resources, their configurations. Usually, if you need to deploy Azure resources, it might be a tiring experience of navigating through different services, their configurations. With the ARM templates, you no longer need to click and navigate around the portal. For example, you can use configure the template for Azure VM or Azure SQL Database deployment.

Click through for a step-by-step walkthrough. I will say, though, that I tend heavily to revise ARM templates the Azure Portal creates. They tend to make everything parameters, to the point where you get inundated with context-free decisions.

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Cosmos DB Continuous Backup

Gauri Mahajan explains how continuous backup works in Cosmos DB:

To follow the exercise in this article, we would need an Azure Account with administrative privileges to operate the Azure Cosmos DB service. It is assumed that such an Azure Account and setup is already available and ready for use. The Continuous backup option is not available for all the editions of Cosmos DB and works only under certain configurations. The focus of this exercise is to understand the use-cases and options under which continuous backup would work in Cosmos DB.

There are four restrictions (as of the draft of this article), which should be kept in view while considering using continuous backup.

Read on for those restrictions and then how you can configure continuous backup to run.

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Log Replay for Azure SQL Managed Instance

Joey D’Antoni has some quick notes on the Log Replay Service:

Recently, I’ve started on a project where we are migrating a customer to Azure SQL Managed Instance, which now supports a few different migration paths. You can simply backup and restore from a database backup, but you can’t apply a log or differential backup to that database. You can also use the Database Migration Service, but that requires a lot of infrastructure and Azure configuration. The log replay service, or LRS, is the functional equivalent of log shipping to your new managed instance database. While log shipping is a well known methodology for both database migrations or disaster recovery. However, the implementation is a little different–let’s talk about how it works.

Click through to see how it differs.

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