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Category: Cloud

Mounding ADF Instances in Microsoft Fabric

Published 2024-12-06 by Kevin Feasel

Koen Verbeeck has an existing Azure Data Factory:

We recently started using Microsoft Fabric for our cloud data platform. However, we already have quite an estate of Azure data services running in our company, including a huge number of Azure Data Factory (ADF) pipelines. It seems cumbersome to migrate all those pipelines to Microsoft Fabric, especially because some features are not supported yet and ADF is the mature choice at the moment. We like the concept of Microsoft Fabric’s centralization, where everything is managed in one platform. Is there an option to manage ADF in Fabric?

Read on for the answer, but make sure to check out its limitations as well.

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Creating a Project in Azure AI Foundry

Published 2024-12-04 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI:

Azure AI models inference service provides access to the most powerful models available in the Azure AI model catalog. Coming from the key model providers in the industry including OpenAI, Microsoft, Meta, Mistral, Cohere, G42, and AI21 Labs; these models can be integrated with software solutions to deliver a wide range of tasks including content generation, summarization, image understanding, semantic search, and code generation.

The Azure AI model inference service provides a way to consume models as APIs without hosting them on your infrastructure. Models are hosted in a Microsoft-managed infrastructure, which enables API-based access to the model provider’s model. API-based access can dramatically reduce the cost of accessing a model and simplify the provisioning experience.

Read on to learn more about what you get when you create a project.

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Features in Azure AI Foundry

Published 2024-12-03 by Kevin Feasel

Tomaz Kastrun continues a series:

Azure AI Foundry is all purpose tool that provides all of the capital ingredients that data scientists would need in order to create, develop and deploy the generative AI applications. The platfrom supports and gets you the following services and abilitiies:

Click through for those features and how you can access the Azure AI Foundry.

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Optimizing AWS Costs

Published 2024-12-03 by Kevin Feasel

Albert McQuiston speaks my language (that is, saving money):

Every organization looks to save on its cloud expenses to align with business objectives. With the following tips, you can optimize your Amazon Web Services (AWS) cloud expenditure and review the key aspects where you can save more effectively.

Read on for some high-level tips. It doesn’t cover things like spot instances, but does a pretty decent job of laying out the problem and showing some of the cost and budgeting tools available to figure out where your company’s money is going.

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A Review of the Azure AI Foundry

Published 2024-12-02 by Kevin Feasel

Tomaz Kastrun starts a new series:

Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Azure AI Foundry brings these services together in a single unified experience for AI development on the Azure cloud platform.

Until now, developers needed to work with multiple tools and web portals in a single project. With Azure AI Foundry, these tasks are now simplified and offers same environment for better collaboration.

Read on to see more about the Azure AI Foundry.

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Table Cloning in Snowflake

Published 2024-11-27 by Kevin Feasel

Kevin Wilkie creates a clone:

In this coding scenario, I am copying everything from TableA and pushing it into a new table called TableB in the same database and schema.

If you check the size of the database before and after you clone a table, it will be the same size – no matter the size of TableA. This is because, at this point in time, TableB exists only as a “pointer” to the data that constitutes TableA. It is not until something changes in one of the tables – say adding a row to TableA, that it stops being a “pointer” and is artificially constituted.

Read on to learn more about how this works.

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Comparing Azure Kubernetes Service and Container Apps

Published 2024-11-27 by Kevin Feasel

Gaurav Shukla makes a comparison:

Hello Readers!! Welcome to the new blog!! AKS vs ACA, which is best in cloud migration? When migrating an application to the cloud, choosing the right platform is crucial to ensure scalability, cost-effectiveness, and ease of management. Two of the prominent services offered by Azure for running containerized applications are Azure Kubernetes Service (AKS) and Azure Container Apps (ACA). Both are excellent choices, but their use cases, complexity, and operational overhead differ significantly. This blog will provide a detailed comparison of AKS and ACA, helping you decide which is the best approach for your cloud migration.

Read on for an overview of each service and a nice table outlining the differences.

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Power BI Writeback via Fabric SQL Database

Published 2024-11-26 by Kevin Feasel

Jon Voge gives us a use case for Fabric SQL Databases:

Until recently, Fabric has allowed us to choose between Lakehouses and Warehouses as a backend. For write-back use cases, neither are ideal.

  • The SQL Endpoint of Lakehouses are Read-Only, making writes from Power Apps impossible.
  • While the SQL Endpoint of Warehouses are write-enabled, they do not support enforced Primary Keys, which are a hard requirement for Power Apps to be able to write directly to a data source.

Jon briefly describes two mechanisms people used and then how you can do this more effectively with a Fabric SQL Database. Based on the article, it seems that you could probably still do the same with an Azure SQL Database, though I suppose handling the managed identity could be an issue.

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AWS DMS and a LOB Bug

Published 2024-11-25 by Kevin Feasel

Richard O’Riordan fixes an issue:

The table over in our Postgres cluster is similar except for the data type “text” being used instead of “varchar”. All kind of boring so far, but what we noticed that on some very rare occasions the “largevalue” column was empty over in the PostgreSQL database even though for that row it was populated in SQL Server.

This seemed odd to me, like you would expect if there was some error inserting the row on the PostgreSQL side then since it is all done within a transaction that it would either all succeed or all fail, how would the row be partially inserted, i.e. missing this text value.

Read on for the story and several tests of how things work.

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Debugging in Databricks

Published 2024-11-19 by Kevin Feasel

Chen Hirsh enables a debugger:

Do you know that feeling, when you write beautiful code and everything just works perfectly on the first try?

I don’t.

Every time I write code It doesn’t work in the beginning, and I have to debug it, make changes, test it…

Databricks introduced a debugger you can use on a code cell, and I’ve wanted to try it for quite some time now. Well, I guess the time is now 

I’m having trouble in finding the utility for a debugger here. Notebooks are already set up for debugging: you can easily add or remove cells and the underlying session maintains state between cells.

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