SOA and microservices follow similar concepts. In the Chapter 1, Demystifying Microservices, we saw that microservices are evolved from SOA and many service characteristics are common in both approaches.

However, are they the same or are they different?

Since microservices are evolved from SOA, many characteristics of microservices are similar to SOA. Let's first examine the definition of SOA.

Cloud computing is one of the most rapidly evolving technologies. It promises many benefits, such as cost advantage, speed, agility, flexibility, and elasticity. There are many cloud providers offering different services. They are lowering the cost models to make it more attractive to the enterprises. Different cloud providers, such as AWS, Microsoft, Rackspace, IBM, Google, and so on, use different tools, technologies, and services. On the other hand, enterprises are aware of this evolving battlefield and, therefore, they are looking for options for de-risking from lockdown to a single vendor.

Many organizations do a lift and shift of their applications to cloud. In such cases, the applications may not realize all benefits promised by the cloud platforms. Some applications need to undergo an overhaul, whereas some may need minor tweaking before moving to the...

Serverless computing architecture or Functions as a Service (FaaS) has gained quite a bit of popularity these days. In serverless computing, developers need not worry about application servers, virtual machines, Containers, infrastructure, scalability, and other quality of services. Instead, developers write functions and drop those functions into an already running computing infrastructure. Serverless computing improves faster software deliveries as it eliminates the provisioning and management part of the infrastructure required by microservices. Sometimes, this is even referred to as NoOps.

FaaS platforms support multiple language runtimes, such as Java, Python, Go, and so on. There are many serverless computing platforms and frameworks available. Moreover, this space is still evolving. AWS Lambda, IBM OpenWhisk, Azure Functions, Google Cloud Functions...

There are new styles of microservices use cases in the context of big data, cognitive computing, bots, and IoT:

The preceding diagram shows a simplified Lambda architecture commonly used in the context of big data, cognitive, and IoTs. As you can see in the diagram, microservices play a critical role in the architecture. The batch layer process data, and store typically in a Hadoop Distributed File System (HDFS) file system. Microservices are written on top of this batch layer process data and build serving layer. Since microservices are independent, when they encounter new demands, it is easy to add those implementations as microservices.

Speed-layer microservices are primarily reactive microservices for stream processing. These microservices accept a stream of data, apply logic, and then respond with another set of events. Similarly, microservices...

The trios Cloud (more specifically, Containers), Microservices and DevOps, are targeting a set of common objectives--speed of delivery, business value, and cost benefits. All three can stay evolved independently, but they complement each other to achieve the desired common goals. Organizations embarking on any of these naturally tend to consider the others as they are closely linked together:

Many organizations start their journey with DevOps as an organizational practice to achieve high velocity release cycles, but eventually move to microservices architecture and cloud.  However, it is not mandatory to have microservices and Cloud to support DevOps. However, automating release cycles of large monolithic applications does not make much sense, and, in many cases, it would be impossible to achieve. In such scenarios, microservices architecture...

The reactive programming paradigm is an effective way to build scalable, fault-tolerant applications. The reactive manifesto defines basic philosophy of reactive programming.

By combining the reactive programming principles together with the microservices architecture, developers can build low latency high throughput scalable applications.

Microservices are typically designed around business capabilities. Ideally, well-designed microservices will exhibit minimal dependencies between microservices. However, in reality, when microservices are expected to deliver same capabilities delivered by monolithic applications, many microservices have to work in collaboration. Dividing services based on business capabilities will not solve all issues. Isolation and communication...