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Azure API Management gives you a framework for publishing your APIs in a consistent manner with built-in benefits like developer engagement, business insights, analytics, security, and protection. However, the most powerful capability it offers is the ability to customize behavior of the API itself. Think of the customization as a short program that gets executed just before or after your API is invoked. The short program is simply a collection of statements (called policies in Azure API Management). Examples of policies that come out of the box include format conversion from XML to JSON, applying rate and quota limits and enforcing IP filtering. In addition, you have control flow policies such as choose that is similar to if-then-else, or a switch construct and set-variable that allows you declare a context variable. Finally, you have the ability to write C# (6.0) expressions. Each expression has access to the context variable, as well as, allowed to leverage a subset of .NET Framework types. As you can see, Azure API Management policies offer constructs equivalent to a programming language.

This begs the question, how do you author Azure API Management policies?

Well, today you have two options. Read More…

What is MTConnect?

MTConnect is the communication standard of choice for manufacturing. It allows organized retrieval of data from shop floor equipment in a structured XML.

The adjacent diagram depicts the key components of MTConnect. Let us start with the shop floor equipment shown at the bottom of the diagram – a CNC lathe.  Above that, we have an optional adapter component that converts machine-specific data into a MTConnect defined format. The adapter component is optional as most manufacturers are building this capability directly into their machines.  On the top is the agent component responsible for converting MTConnect data into XML. Additionally the agent also exposes a RESTful service that can be used to retrieve data. Read More…

Transient exception handling and retry logic are considered an important defensive programming practice, especially in the public cloud. But how good is your exception handling? Unfortunately, it’s not always easy to simulate transient exceptions.

Consider the Azure Redis Service for example. It does not have a way to simulate failures. So we decided to create our own Chaos Redis library. Fortunately, Microsoft has developed a Windows port of Redis Cache.

We decided to modify the code so we can inject chaos. Read More…

At a recent holiday dinner, a conversation with a friend eventually progressed to the topics of self-driving cars and facial-recognition software – and the overall roles and capabilities of artificial intelligence (AI). My friend’s assertion was that “AI is ultimately about pattern matching.” In essence, you equip the AI with a library of “patterns” and their corresponding associated actions. Based on the input it receives from the real world, the AI software program will then make an attempt to match the input to a stored pattern and execute the corresponding associated action.

Of course any program, regardless of whether it is designed to steer a car or detect a face in an image, relies on pattern-matching at the lowest level. That said, as we will see shortly, a deep learning-based approach is a fundamentally different way to solve the problem. And it’s an approach that is poised to reinvent computing. Read More…

Companies are adopting Docker containers at a remarkable pace and for a good reason – Docker containers are turning out to be key enablers for a micro-services based architecture.

As a quick recap, Docker containers are:

  • Encapsulated, deployable components that can run as isolated instances
  • Small in size with a fast boot-up time
  • Include tools that enable containerized application images to be easily moved across the public cloud and on-premises
  • Capable of applying limits on physical resources consumed by any given application

Given the popularity of Docker containers, it should come as no surprise that the Azure platform already provides first-class support for a container hosting solution, in the form of Azure Container Service (ACS). ACS makes it simple to create a cluster of Virtual Machines that can run containerized applications. ACS relies on popular open-source tools – with Docker as the container format, and a choice of Marathon, DC/OS, Docker Swarm and Kubernetes for orchestration and scheduling, etc. All this makes it possible to easily run containerized workloads on Azure in a portable manner.

But the Docker containerization story on Azure does not stop here.

It is also being weaved more and more into existing PaaS offerings, including Azure Batch, Azure App Service and Azure Service Fabric. Let’s briefly review the latest developments to see how Docker integrates with Azure PaaS: Read More…

22106868_sYou’re an enterprise. You’ve done your research. You’ve read the whitepapers. You’ve heard all the success stories (along with a few cautionary tales). Perhaps you’ve already taken your first steps into the cloud, but want to embark on a larger-scale public cloud adoption strategy.

But what does that look like for your enterprise? The journey is different for you – for everyone, really. And you certainly don’t want to make it up as you go along.

Here are five important things you need to map out before you start your public cloud journey. We’re confident in this roadmap because we’ve been along for the ride before. We’ve helped many large enterprises and agencies successfully adopt and implement their own unique cloud strategies. Read More…

 By now, DevOps is well-established within web companies, unicorns, and product companies—and especially among companies targeting the cloud. To spare you the lengthy introduction, DevOps brings “development” and “operations” together as a moniker for company-wide collaboration that will improve business agility. The key DevOps traits are:

  • Involving Ops teams in early stages of development
  • Focus on automating all aspects of the IT life cycle
  • Continuous improvement
  • Maturity of self-service model

Enterprise DevOps Challenges

Despite its success within smaller companies, implementing DevOps in large enterprises has proven to be more difficult. Rachael Shannon-Solomon writes in The Wall Street Journal that DevOps is perhaps better suited for startups at the current time than for enterprise IT. Regardless of whether you agree with her article, it does raise some important points related to siloed structures, organizational change and affecting cultural change on a large scale.

The issue is not that enterprises aren’t adopting DevOps (just look at the latest State of DevOps report for evidence to the contrary); it is the unique set of challenges that large enterprises face that make it harder for DevOps to succeed. Let’s take a closer look: Read More…

AIS is proud to announce the release of our Chief Technology Officer Vishwas Lele’s Pluralsight course on Cloud Oriented Programming. This course demonstrates coding techniques to optimize your applications that are targeted to run in the public cloud.

The public cloud is tomorrow’s IT backbone. As cloud vendors introduce new capabilities, the application-building process is undergoing a profound transformation. The cloud is based on key tenets such as global scale, commodity hardware, usage-based billing, scale-out, and automation. But how does the cloud impact what we do as programmers every day? What do we need to do at a program level that aligns us with the aforementioned tenets? This course discusses 9 techniques / tips (organized in three modules) designed to help developers make more effective use of the cloud.

Click here to get started!

Read More…

Welcome to part seven of our blog series based on my latest PluralSight course: Applied Azure. Previously, we’ve discussed Azure Web Sites, Azure Worker Roles, Identity and Access with Azure Active Directory, Azure Service Bus and MongoDB, HIPPA Compliant Apps in Azure and Offloading SharePoint Customizations to Azure.

Motivation

No lengthy commentary is needed to communicate the growing importance of big data technologies. Look no further than the rounds of funding [1][2][3] that companies like Cloudera, Hortonworks and MapR have attracted in recent months. It is widely expected that the market for Hadoop will likely grow to $20 Billion by 2018.

The key motivations for the growth of big data technologies includes:

  • The growing need to process ever increasing volumes of data. This growth in data is not limited to web scale companies alone. Businesses of all sizes are seeing this growth.
  • Not all data conforms to a well-defined structure/schema, so there is a need to supplement (if not replace) the traditional data processing and analysis tools such as EDWs.
  • Ability to take advantage of deep compute analytics using massively parallel, commodity based clusters. We will see examples of deep compute analysis a little bit later but this is a growing area of deriving knowledge from the data.
  • Overall simplicity (from the standpoint of the analyst/ developer authoring the query) that hides the non-trivial complexity of the underlying infrastructure.
  • Price-performance benefit accorded due to the commodity based clusters and fault tolerance.
  • The ability to tap into fast paced innovation taking place within the “Hadoop” ecosystem. Consider that Map Reduce, which has been the underpinning of Hadoop ecosystem for years, is being replaced by projects such as Yarn in recent months. Read More…

Welcome to part six of our blog series based on my latest PluralSight course: Applied Azure. Previously, we’ve discussed, HIPAA Compliant Apps with Windows Azure Trust Center,  Azure Web Sites, Azure Worker RolesIdentity and Access with Azure Active Directory and Azure Service Bus and MongoDB.

Motivation

Question: “How does an admin protect their SharePoint farm from poorly written custom code?” Answer: “Force custom code to run in the SharePoint sandbox mode.” Not quite! Turns out that running in a sandbox mode (as the name suggests, it is a restricted execution mode within SharePoint) is not very productive because of the performance penalty and very limited capabilities available to code running in it. A better approach is to move the code “outside” of SharePoint and into a “private” execution environment (so that the errant developers can shoot themselves in the foot, but not everyone else). Read More…