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Category: Cloud

SQL Server on Azure Container Instances

Published 2021-07-09 by Kevin Feasel

Arun Sirpal has a series for us. Part 1 involves spinning up SQL Server on ACI:

This is Microsoft’s serverless technology which allows us to deploy containers without having to worry about managing the underlying hardware. It’s a way to get access to SQL fast (faster than traditional methods like installing a virtual machine) to do things like test code fixes etc.

There a couple of ways of doing this, you can use the portal, PowerShell or Azure CLI, I actually like Azure CLI.

Part 2 gives you an idea of what you get:

In the last post we built an image of SQL server 2019 Linux hosted in Azure Container Instance for fast access to SQL server. So, your next question is probably, lets see some database action?

When you connect to SSMS its not different, the feel and look, is, SQL server. Lets have a tour.

The normal warning with Azure Container Instances is that they’re great for development and testing efforts (in part because of how inexpensive it is compared to alternatives on Azure) but won’t have the same uptime or high availability guarantees that a service like Azure Kubernetes Service will have.

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Optimizing BERT Models on Google Colab

Published 2021-07-08 by Kevin Feasel

Kevin Jacobs fine-tunes some NLP processes:

BERT is a language model and can thus be used for predicting the next word in a sentence. Furthermore, BERT can be used for automatic summarization, text classification and many more downstream tasks. Google Colab provides you with a cloud-based environment on which you can train your machine learning models on a GPU. The downside is that your data is uploaded to the Google cloud. Google Colab gives you the opportunity to finetune BERT.

Click through to see how.

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Migrating Historical Data from Azure Analysis Services to Power BI Premium Per User

Published 2021-07-07 by Kevin Feasel

Gilbert Quevauvilliers continues a series on moving to Power BI Premium Per User:

In this blog post I am looking at how to load or reload historical data in AAS and PPU and compare the differences.

It should already be noted that I am only going to compare tables where I have partitions created and enabled. The reason being for dimension tables it is typically quick and easy to reload the data by re-processing the data for the table.

Read on for the details.

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Using Filter Based Feature Selection in Text Analytics

Published 2021-07-02 by Kevin Feasel

Dinesh Asanka takes us through a text analytics technique in Azure Machine Learning:

There are two parameters to be defined in the Feature Hashing control. Hashing bitsize will define the maximum number of vectors. 10 hashing bitsize means 1,024 vectors (2^10). 1,024 vectors are more than enough even for the large volume text files. Next, we need to choose N-grams which is 2 as 2 is the optimal number for N-grams for most situations. A detailed description of N-Grams is given in the link given in the reference section.

After the vectors are generated, we do not need other text columns. Apart from the vectors, we need only the dependent attribute or the category column in this example. Therefore, we can remove the unnecessary attributes by Select Columns in dataset control. However, this control will show 1,024 vectors even though it is not available in the previous step, Feature Hashing. Therefore, you need to choose only the available attributes in the Feature Hashing control at the Select Columns in dataset control. In the above example, only 93 vectors were generated.

Click through to learn more.

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Monitoring Power Virtual Agent Chatbots

Published 2021-07-01 by Kevin Feasel

Devin Knight has a video for us:

Power Virtual Agents empowers subject matter experts to build intelligent conversational bots, using a guided, no-code graphical interface. In this video you will learn how to monitor how successful your chatbots are at answering your users questions. Using the monitoring capability you will uncover areas of your chatbot that can be improved.

If I were familiar enough with Latin, I’d try a play on “Quis custodiet ipsos custodes?” with this.

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Executing Azure Data Factory Pipelines with Power App

Published 2021-06-30 by Kevin Feasel

Rayis Imayev has a plan:

One of my university professors liked to tell us a quote, “The Sleep of Reason Produces Monsters”, in a way to help us, his students, to stay active in our thinking process. I’m not sure if Francisco Goya, had a similar aspiration when he was creating his artwork with the same name.

So, let me explain my reasons to create a solution to trigger Azure Data Factory (ADF) pipelines from a Power App and why it shouldn’t be considered as a monster 🙂

If that’s not an introduction enticing enough to get you to read the whole thing, I don’t know what is.

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Using the Cosmos DB Integrated Cache

Published 2021-06-29 by Kevin Feasel

Hasan Savran makes use of a cache:

We are ready to write some code now. Integrated Cache works only in Eventual Consistency for now. So, we need to send requests in Eventual consistency to test the Integrated Cache. To do that, we need to use requestOptions parameter in SDK. You can change your database consistency level to Eventual too for testing if you like. Don’t forget to change it back later!

Hopefully that limitation changes later, but in the meantime, click through to see how to use the integrated cache in Cosmos DB.

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Moving Artifacts between Folders in Synapse Studio

Published 2021-06-25 by Kevin Feasel

Wolfgang Strasser looks at a recent update:

Another small but very powerful usability extension in Azure Synapse Studio was added at the beginning of June: Move artifacts across folders in Synapse Studio (without extra clicks but with drag&drop)

Once again, the release notes list contained the short sentence that made me curious… hmm… that sound nice… In one of my previous post, I described the “old” way of moving artifacts around in Synapse Studio.

Click through for a demonstration.

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Connecting to Cosmos DB via Dedicated Gateway

Published 2021-06-24 by Kevin Feasel

Hasan Savran introduces us to the Cosmos DB Dedicated Gateway:

Cosmos DB team announced a new way named Dedicated Gateway to connect to Azure Cosmos DB. As you might know there is already a standard gateway to connect to Cosmos DB. Dedicated or Standard gateway means that there is a computer stays between Cosmos DB replica set and your application. Your application request goes to gateway server then goes to Cosmos DB database. The biggest difference between Standard Gateway and Dedicated Gateway is, you do not share the dedicated gateway server with other Cosmos DB customers.

     Dedicated Gateway is totally yours and you are responsible for its costs. Depending on your application size, you can select different size of gateway servers.

Read on to learn how expensive it is and the benefits it brings.

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Using Ola’s Maintenance Solution on RDS

Published 2021-06-24 by Kevin Feasel

Jack Vamvas takes us through a couple of nuances around using Ola Hallengren’s SQL Server Maintenance Solution on Amazon RDS:

I’ve used the Ola Hallengren Maintenance Solution across various SQL Server environments . I was recently asked by a colleague about how adaptable they are to the AWS RDS SQL Server environment. 

I checked the Ola Hallengren FAQ and there is a comment :

Read on to learn the details.

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