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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. 


NDC London 2020 – My highlights

by Ed Freeman

A couple of weeks back, along with a rabble of other endjineers, I was fortunate enough to attend NDC London. This wasn’t my first time at an NDC conference – in fact, my previous outing was to Oslo to experience the “original” flavour of NDC back in 2018. That was extremely fun and packed with […]


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.


In this blog from the Azure Advent Calendar 2019 we discuss building a secure data solution using Azure Data Lake. Data Lake has many features which enable fine grained security and data separation. It is also built on Azure Storage which enables us to take advantage of all of those features and means that ADLS is still a cost effective storage option!

This post runs through some of the great features of ADLS and runs through an example of how we build our solutions using this technology!


Very excited to be speaking at NDC in London in January! The talk is focused on “Combatting illegal fishing with Machine Learning and Azure” and will focus on the recent work we did with OceanMind. OceanMind are a not-for-profit who are working on cleaning up the world’s oceans with the help of Microsoft’s cloud technologies. […]


Import and export notebooks in Databricks

by Ed Freeman

Sometimes we need to import and export notebooks from a Databricks workspace. This might be because you have a bunch of generic notebooks that can be useful across numerous workspaces, or it could be that you’re having to delete your current workspace for some reason and therefore need to transfer content over to a new […]


Machine learning often seems like a black box. This post walks through what’s actually happening under the covers, in an attempt to de-mystify the process!

Neural networks are built up of neurons. In a shallow neural network we have an input layer, a “hidden” layer of neurons, and an output layer. For deep learning, there is simply more hidden layers which allows for combining neuron’s inputs and outputs to build up a more detailed picture.

If you have an interest in Machine Learning and what is really happening, definitely give this a read (WARNING: Some algebra ahead…)!


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.


Here at endjin we’ve done a lot of work around data analysis and ETL. As part of this we have done some work with Databricks Notebooks on Microsoft Azure. Notebooks can be used for complex and powerful data analysis using Spark. Spark is a “unified analytics engine for big data and machine learning”. It allows you to run data analysis workloads, and can be accessed via many APIs. This means that you can build up data processes and models using a language you feel comfortable with. They can also be run as an activity in a ADF pipeline, and combined with Mapping Data Flows to build up a complex ETL process which can be run via ADF.


Endjin is a Snowflake Partner

by Howard van Rooijen

I’ve very pleased to announce that endjin has become a Snowflake partner. This fantastic “designed for the cloud” data platform redefines what a data warehouse can be in the age of cloud. With features such as data sharing, usage based billing, and availability on Microsoft Azure, it has won our hearts. Over the last three years, we’ve […]


Mapping Data Flows are a relatively new feature of ADF. They allow you to visually build up complex data transformation sequences. This can aid in the streamlining of data manipulation and ETL processes, without the need to write any code! This post gives a brief introduction to the technology, and what this could enable!


If, like me, you are a fan of Azure Data Factory and love Snowflake then you are probably disappointed that there isn’t a native Data Factory connector for Snowflake. While we wait for an official connector from Microsoft we have no alternative but to roll our own. In this blog post I will walk you through […]


Announcing Power BI Weekly!

by Howard van Rooijen

We launched Azure Weekly, a free weekly newsletter, back in 2014. 200+ issues and many thousands of global subscribers later, it’s still going strong. Last month Ed Freeman pointed out that analytics section of Azure Weekly was mainly full of Power BI articles and that it was by far the largest category in the newsletter. […]


When he joined endjin, Technical Fellow Ian sat down with founder Howard for a Q&A session. This was originally published on LinkedIn in 5 parts, but is republished here, in full. Ian talks about his path into computing, some highlights of his career, the evolution of the .NET ecosystem, AI, and the software engineering life.


I’m very excited that Ian Griffiths has joined endjin as a “Technical Fellow”. This is a new career pathway branch we created especially for Ian, as he didn’t really fit into any of our existing roles; his skills and expertise exemplify a pathway that many software engineers desire, but few have the opportunity to achieve […]


I’m back! Hi everyone. It’s been a long while since my last blog. *Bows head in shame* Blogging is one of those things I know I should do more often, but I’m still not in the routine in which one needs to be in order to do it regularly. As a consequence, what usually happens […]


Recently, Jess and I were looking at a Power BI report we produced for one of our clients. On the ‘overview’ page, we have numerous high-level visuals: Total Sales, Previous Year Sales, % Change YoY etc. Something struck me as odd, though. As I’m sure many of you who have spent days on a report […]


Using Python inside SQL Server

by Ed Freeman

Hello everyone. Before Christmas I played around with SQL Server 2017’s inline Python integration capability. This capability was announced early last year, with the corresponding integration with R already being possible for a number of months. The main benefits from this are the abilities to: Eliminate data movement (having to transfer data samples from a database to […]