As database experts and Business Intelligence specialists we are used to selecting business value from static data sets. But is this always the optimal scenario? In today’s world of rapidly changing businesses, with the emergence of Big Data, The Internet of Things and on-line social interactions on a massive scale, the challenges of selecting business value from static data are increasing in complexity In this demo intensive session Alan will unleash the power of Azure Stream Analytics, showing how data stream processing scenarios can be implemented quickly and efficiently. The scenarios demonstrated will range from issuing speeding tickets in a driving game to analyzing dynamic data streams from trending topics in Twitter.

This demo intensive session will show how physical devices can be integrated with cloud-based services in Microsoft Azure. The principles, patterns and best practices of integrating devices to the cloud will be covered in detail. Receiving and processing telemetry data, notification messages, and sending control messages securely will be explored.

The brokered messaging services in the Microsoft Azure Service Bus provide advanced enterprise class point-to-point and brokered messaging capabilities in the cloud. For developers experienced with message based technologies such as MSMQ, BizTalk Server, Azure Storage Queues and IBM MQ-Series the availability of these capabilities raises a lot of questions. This demo centric session will attempt to answer these questions and more by showing how the advanced features of Azure Service Bus brokered messaging can be leveraged be developers familiar with message oriented systems.

This demo intensive session focusses on the use of Microsoft Azure services to host a back end system for real- time telemetry processing in a 3D racing game. The use of asynchronous queues, worker roles, storage services and websites are explained. Techniques for real-time data stream processing, data archiving and report generation are discussed and demonstrated. The techniques demonstrated will be applicable to many scenarios, from multi-player gaming to device telemetry and the internet of things.

This entertaining and demo intensive session lifts the lid on the black art of physics simulation in computer games. The sample scenario is the re-writing and testing of the physics engine in a sample 3D driving game. Starting with the basics of force, mass and acceleration the simulation is gradually enhanced to include lateral and vertical g-force, tire slip, down-force and collision detection. The addition of a drag reduction system (DRS) and kinetic energy recovery system (KERS) round off the demo, and add a strategic element to the game. The techniques demonstrated can easily be applied to 2D and 3D games on any technology to improve the responsiveness, feel and overall playability of the game.

Red Dog Racing is a 3D driving game that makes use of back-end services hosted in Windows Azure. Sector and lap time data and telemetry data is send from the game to the Windows Azure Service Bus. A worker role is used to de-queue and process the data and store it as entities in Windows Azure Table Storage. Telemetry data is also sent to Windows Azure Blob Storage to provide the game with a replay function that displays ghost cars for the fastest laps driven by other players. The best lap times, overall standings, and driver telemetry data can be viewed on a website.

Windows Azure Storage provide massively scalable storage services with local and geo-redundancy. Organizations can store terabytes of data off-site and in a reliable, highly available and cost efficient manner. For those of you wishing to explore the capabilities of Windows Azure Storage there are bound to be a number of questions that will arise. Can a blob storage container really handle millions of blobs effectively? How efficient is a Table Storage query when the table contain over a billion entities? How can these data volumes be uploaded reliably and effectively? How can the usage and billing of a data intensive solution be monitored? This demo-intensive session will answer these questions and show how big data scenarios can be effectively implemented using Windows Azure Storage.

Rendering 3D ray-traced animations is a task that requires massive compute power. Creating a 256 node render farm that is capable of producing high definition high quality 3D animations would cost hundreds of thousands of dollars, and take months to provision. Creating this environment in a cloud-based platform can cost around $30 per hour, and takes only a few minutes to provision. In this session the power of cloud-computing is demonstrated live on stage, with the creation of a render farm in Windows Azure using 256 virtual servers to render a 3D animation using depth data captured by a Kinect sensor. The principles used in the architecture of this solution can be applied to many other scenarios, and is covered in detail, along with tips and advice for creating your own grid computing applications.

Windows Azure provides a number of services that allow existing on-premise applications to be extended to the cloud, or migrated to cloud-based hosting. In this demo intensive application an on-premise multi-tier application will first be extended to the cloud using the Windows Azure Service Bus, before being fully migrated to Windows Azure using Windows Azure Websites, Worker Roles and Windows Azure SQL Database. The end result will be a fully cloud-hosted application.

This webcast continues in the theme of calling business rules from .NET code. It demonstrates how a business rules policy can be published as a WCF service, increasing the reach of the rules policy to applications both in and outside the enterprise.

This webcast continues from the webcast on calling business rules from .NET code. It demonstrates how tracking can easily be added to an application that calls business rules. This can be useful for debugging wow the rules are executing and also creating an audit trail for rule execution.

This webcast will show how the BizTalk Server Business Rules Engine (BRE) can be called from a .NET application using the Rules API. Calling the rules engine in this way extends the reach of the BRE, allowing it to be used in scenarios independent of BizTalk Server.

This webcast will take a detailed look at the Atomic Transaction shape in BizTalk orchestrations. The correct usage of the shape will be demonstrated along with some tips for reducing persistence and error handling.

A look at the transaction and compensation model in BizTalk Server 2009. The code is based on the Service Aggregator orchestration, which is described in a previous webcast.

A look at Custom XSLT mapping in BizTalk Server

This webcast runs through the installation of the core BizTalk components in a multi server environment using Virtual PC images.

This webcast is the third in a series looking at the service aggregator pattern in BizTalk Server 2009. The other webcasts are Service Aggregator and Orchestration Performance.

Using transactions and parallel actions to increase orchestration performance.

A basic orchestration pattern to call multiple services from a BizTalk orchestration.

Using views in Business Activity Monitoring to track Sushi production.

Using activities in Business Activity Monitoring to track Sushi consumption.

Looking at correlation, activation and instance subscriptions and issues 'Zombie' messages

A look at aggregating Sushi RFID tags with a sequential convoy orchestration.

A simple demo of correlation in BizTalk Orchestrations.