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Category: Cloud

Managing Spatial Data in Azure

Published 2021-07-19 by Kevin Feasel

Rolf Tesmer takes us through the different Azure services which offer some ability to work with spatial data:

Every now and then you come across a use-case where you need to do something with spatial data, and you need to do it in the cloud (Azure, of course)! Up until that very point you maybe didn’t know, or perhaps even care, much about the intricacies of spatial data assets, let alone how the heck you were going to store it, process it, and query it, without making a mess of your current data stack.

Well, if you’re that person, then I say welcome to this blog post!

Click through for a fairly lengthy list, including Rolf’s comments on each. Also note the one big omission from the list as far as data platform products.

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Parameterizing ADF Pipelines

Published 2021-07-19 by Kevin Feasel

Reitse Eskens continues a series on learning Azure Data Factory:

In my previous blog I created the integration runtimes, and the linked services. However, we need to create new datasets. If you remember, and I don’t blame you if you don’t, the dataset I created contained a reference to a table. That’s nice, but this time we don’t want just one table, we want a number of tables.

Click thorugh to check it out.

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Renaming a YAML Pipeline in Azure DevOps

Published 2021-07-15 by Kevin Feasel

Hamish Watson figures out what’s in a name:

I had created a pipeline using YAML – which was called InfrastructureAsCode as the YAMP file was in the root directory.

However I wanted to move it into a folder .\InfrastructureAsCode\pipelines\… and run the YAML file from there – as I would have a non-prod and PROD version of them (as the schedule was different for each).

Click through to see how Hamish was able to resolve this.

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Case-Insensitive Collations in Redshift

Published 2021-07-09 by Kevin Feasel

Mengchu Cai, et al, show us how to change collation with Redshift:

Amazon Redshift is a fast, fully managed, cloud-native data warehouse. Tens of thousands of customers have successfully migrated their workloads to Amazon Redshift. We hear from customers that they need case-insensitive collation for strings in Amazon Redshift in order to maintain the same functionality and meet their performance goals when they migrate their existing workloads from legacy, on-premises data warehouses like Teradata, Oracle, or IBM. With that goal in mind, AWS provides an option to create case-insensitive and case-sensitive collation.

In this post, we discuss how to use case-insensitive collation and how to override the default collation. Also, we specifically explain the process to migrate your existing Teradata database using the native Amazon Redshift collation capability.

Specifically, it appears that they have two collations exposed: one which is case-sensitive and the other which is case-insensitive.

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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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