Monday saw the UK Azure usergroup hosting the legendary ScottGu for a morning of all-things-Azure. The excited crowds outside the sell-out arena are testament to the respect for Scott’s opinion around the current cloud landscape and his thoughts on the future.
But what of the talk itself? What followed was a bit of a mixed bag – the audience was of a size that presenting anything in any depth was a lost cause and so the talk itself was pretty high level. He spent a fair while discussing the argument for adopting cloud-based platforms and infrastructure. This seemed a bit unnecessary for a talk organised for members of the UK Azure usergroup, but as the talk had been widened to other circles, so the scope of the talk broadened.
We saw demos of the new enterprise-scale VMs now available (G series apparently stands for Godzilla), with the all-important IOPS counters to prove their power. The sheer performance differential is impressive – premium storage, an abundance of cores and vast swathes of memory obviously making an incredible difference. The big argument here being that they can be turned on and off as and when the real crunching power is required, limiting their costs.
In the Platform-as-a-Service (PaaS) world, the talks and demos were understandably around the preview features announced over the past few months – the Azure API service, Event Hubs, Streaming Analytics and Machine Learning. Scott’s showpiece demo really shows the power behind some of these elements – a hand-held heat/humidity sensor linked to his PC sends realtime readings to an event hub, which is polled continuously by a streaming analytics job that pushes its aggregated results directly to a PowerBI dashboard. Not to an intermediary database, not to a linked blob storage account, but directly to the dashboard data model itself. The real-world applications of knowing when ScottGu is breathing onto a sensor are questionable, but the infrastructure costs and development turnaround time of getting a real-time operational dashboard are mind-blowingly small.
A few small niceties crept in during the management piece. Resource Groups allow for services to be managed as a singular entity, all components that make up a single application can be brought up/down at once, simplifying automation scripts no end. Also resource tags, a minor functionality addition that allows for services to be ‘tagged’. For hosting companies, they could potentially tag different customers and allow for their entire subscription to be filtered on a customer-by-customer basis. Again, a small addition that further enriches the management of Azure services.
Finally, we saw some real-world implementation presentations – two companies who have leveraged Azure to their advantage. Ad Coelum, a small startup with a SaaS product aimed at legal firms, have been able to plan a simultaneous global rollout simply through the various scale out options available. JustGiving however, have a complex chain of services that utilises some of the most powerful functionality Azure had to offer. From their newsfeed-style user timelines, to machine learning to understand the complex behaviour of ‘Givers’, they’re really pushing forward in cloud-based PaaS architecture.
Overall it was a good day, it’s certainly good to see the level of enthusiasm in the developer community for cloud architecture and it’s great to see how quickly it’s currently developing. Next time a client solution requires some blue-sky thinking, I’ll certainly be imagining clouds.
Introduction to Data Wrangler in Microsoft Fabric
What is Data Wrangler? A key selling point of Microsoft Fabric is the Data Science
Jul
Autogen Power BI Model in Tabular Editor
In the realm of business intelligence, Power BI has emerged as a powerful tool for
Jul
Microsoft Healthcare Accelerator for Fabric
Microsoft released the Healthcare Data Solutions in Microsoft Fabric in Q1 2024. It was introduced
Jul
Unlock the Power of Colour: Make Your Power BI Reports Pop
Colour is a powerful visual tool that can enhance the appeal and readability of your
Jul
Python vs. PySpark: Navigating Data Analytics in Databricks – Part 2
Part 2: Exploring Advanced Functionalities in Databricks Welcome back to our Databricks journey! In this
May
GPT-4 with Vision vs Custom Vision in Anomaly Detection
Businesses today are generating data at an unprecedented rate. Automated processing of data is essential
May
Exploring DALL·E Capabilities
What is DALL·E? DALL·E is text-to-image generation system developed by OpenAI using deep learning methodologies.
May
Using Copilot Studio to Develop a HR Policy Bot
The next addition to Microsoft’s generative AI and large language model tools is Microsoft Copilot
Apr