Through Microsoft's Rapid Deployment Program, Microsoft Premier and Consulting services had the opportunity to work with a Fortune 100 Company that "invents and manufactures technologies to address some of the world's toughest challenges initiated by revolutionary macrotrends in science, technology and society." A simple business opportunity expanded to see how this large, international customer can ingest millions of events per hour, exploit near-real-time analytics, and tackle true 'Big Data' problems successfully on our Azure data platform. We will provide a detailed walk through the architecture that was implemented, review some obstacles met by the various teams (client, product, consulting), and do a deep-dive into the various Azure components: DocumentDB, Stream Analytics, HDInsight, and Azure Data Factory.
In this session, we discuss how to transform the economics of hybrid clouds built with Cloud OS. We look at the process of metering granular usage and cost data for Microsoft System Center 2012 R2, Windows Server 2012 R2 Hyper-V, Windows Azure Pack, Azure public cloud, and other non-Microsoft IT resources, and then transform it into actionable financial intelligence. We discuss a variety of use cases, such as multi-tenant chargeback and billing, demand forecasting, profit management, technology trends, and what-if modeling, to get a better understanding of how enterprises and service providers are using IT Financial Management to optimize costs and drive greater business agility with the Microsoft cloud platform and Cloud Cruiser.
In the second half of this session, we'll connect to Azure SQL DW, load some risk data, improve the data model, create some insightful reports and dashboards, ask some questions then analyse in Excel. We'll use an example of financial market risk data. This is stored in data warehouses since a bank can easily generate a few hundred million rows of each day and risk managers need to analyse this data over several days and months. Such large volumes make this data a prime candidate for moving to Azure SQL DW. In addition, the elastic capabilities of SQL DW are very useful for example at UAT phases when users need an additional large dataset available for a (hopefully) short period of time. In the demo, we will use the scenario of an equity trading division with a bank. I will spend a couple of minutes introducing a small fictional dataset of the profit and loss (P&L) and VaR over a few years. (VaR, stands for Value At Risk, and is a common measure of the riskiness of the portfolio of trades.) We will load the data into Power BI desktop. We will improve the data model; build a hierarchy, and hide columns of no interest to our users, and calculate a few useful quantities using DAX. We will build a typical market risk report known as a back test chart which compares our P&L and VaR. We'll do this firstly using the standard and custom visuals then using the R Script visual to give us a more precise visualisation that meet the demands of regulators. Once done, we are ready to publish data and reports to the Power BI service. There will we pin a few visuals to a dashboard and then interrogate the data using plain English with the Q&A feature. Finally we will analyse our data in Excel. This is a very exciting new(ish) feature of Power BI and very useful to the risk managers in our scenario who traditionally do analyse their risk data in Excel pivot tables. This allows them to have their cake and eat it – to be able to visualise, explore and share their data with Power BI but also to take advantage of all the analytical power of Excel.