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This post explains how to update Azure Analysis Services model schemas from inside custom .NET applications. Whilst not a common scenario for most, it shows that this is easy to do using the AMO SDK. So, there’s nothing stopping you from developing complex and rich end-user functionality over the top of your data analysis solutions – providing run-time, user-driven schema changes like “what if” analysis.


Integrating Azure Analysis Services into custom applications doesn’t just mean read-only data querying. But if your application changes the underlying model, it will need to be re-processed before the changes take effect. This post describes how to use the REST API for Azure Analysis Services inside a custom .NET application to perform asynchronous model refreshes, meaning your applications can reliably and efficiently deal with model updates.


Being able to construct DAX queries dynamically in C# means the possibilities are endless in terms of integrating Azure Analysis Services queries into your custom applications, and with the code samples in this post, you have everything you need to get started.


The theme of this year’s British Science Week (6 – 15 March 2020) is “Our Diverse Planet”. We’ll be getting involved by speaking to school children about the work we’ve been doing with Oxfordshire-based OceanMind (part of the Microsoft AI for Good programme) to help them combat illegal fishing, hopefully inspiring some of the next generation of data scientists!


Integrating Azure Analysis Services into custom applications means more than just querying the data. By surfacing the metadata in your models, you can build dynamic and customisable UIs and APIs, tailored to the needs of the client application. This post explains how easy it is to query model metadata from .NET, so you can create deeper integrations between your data insights and your custom applications.


One of the first steps in integrating Azure Analysis Services into your applications is creating and opening a connection to the server – just like any other database technology. This post explains the ins and outs of creating Azure Analysis Services connections, including code samples for each of the key scenarios. 


With a variety of integration support through client SDKs, PowerShell cmdlets and REST APIs, it can be hard to know where to start with integrating Azure Analysis Services into your custom applications. This posts walks through the options, and lays out a simple guide to choosing the right framework.


We’ve done a lot of work at endjin with Azure Analysis Services over the last couple of years – but none of it has been what you’d call “traditional BI”. We’ve pulled, twisted and bent it in all sorts of directions, using it’s raw analytical processing power to underpin bespoke analysis products and processes. This post explains some of the common (and not-so-common) reasons why you might want to do similar things, and how Azure Analysis Services might be the key to unlocking your data insights.


Whilst some of the Azure Active Directory PowerShell for Graph module (AzureAD) functionality has been rolled into the new Azure PowerShell Az module, it’s not currently (and might never be) a replacement for the full power of what you can achieve with AzureAD. So, there’s every chance you’ll find yourself needing to use both side-by-side. This post explains how to do that using the new cross-platform PowerShell Core.


A Power BI based solution typically consists of a variety of technologies – for example Azure data platform services containing source data. As such, automation of Power BI resources needs to be considered as part of a wider DevOps strategy. This post describes the specific steps needed in order to fully automate the creation and security of Power BI workspaces using Powershell and Azure DevOps pipelines.


Azure DevOps Work Items offer a lot of power and features out of the box, but sometimes you need insights that Azure DevOps doesn’t natively provide. In this blog post Director of Engineering, James Broome, shows how you can use the Azure DevOps Restful API to generate insights and even use Power BI to visualise them in this step-by-step guide.


In the world of DevOps, cloud and platform services, how does a developer’s “definition of done” need to change? This post argues that as the silos of development and operations are broken down, the responsibility of understanding the whole solution increases meaning, to truly take advantage of the cloud, the need for quality and professionalism is critical for success.


Machine Learning – the process is the science

by James Broome

What do machine learning and data science actually mean? This post digs into the detail behind the endjin approach to structured experimentation, arguing that the “science” is really all about following the process, allowing you to iterate to insights quickly when there are no guarantees of success.


This post looks at what machine learning really is (and isn’t), dispelling some of the myths and hype that have emerged as the interest in data science, predictive analytics and machine learning has grown. Without any hard guarantees of success, it argues that machine learning as a discipline is simply trial and error at scale – proving or disproving statistical scenarios through structured experimentation.


Azure Batch – Time is Money in Big Compute

by James Broome

Earlier in the year, endjin worked with the Azure Batch Product Team to run a series of experiments against the Azure Batch service using a framework we developed for performing scale, soak and performance tests. We’ve had conversations with a number of organisations over the last 5 years who have scaled their compute intensive workloads (SAS, […]