The backend can be considered as the brain of an application as it is responsible for the business logic and various sensitive operations under the hood. The backend of an application also influences the app’s quality, performance, security and scalability. All of this emphasizes the importance of efficient backend development. And when it comes to backend development, choosing the right tech stack is the key.

There are multiple options to choose from – Ruby, PHP, Python, Node.js, Go etc. and they are all good; which makes choosing one from the lot quite challenging. This blog is aimed at making this decision easier though we are only taking two options from that list above – Node.js and Go.

Node.js & Go

Before getting into the details, here is a brief introduction of the two.

Node.js is a Google Chrome V8 engine-based JavaScript runtime environment that first popped up back in 2008. The open source tool quickly established a thriving developer community while turning heads as a great tool to create web servers.

Go, which is also known as Golang, is a lightning fast, open source, cross-platform programming language introduced by Google a decade ago in 2009. Its creators wanted a programming language that combines everything good about existing languages in order to solve the most common problems that existing languages experience. They succeeded with Go.

Choosing between the two

To make the choice, we will take various factors into account including performance, concurrency, tools etc. Let’s start with performance.


A mobile app’s performance is measured by assessing various factors like load time, response time etc. and it directly influences mobile user satisfaction.

And when it comes to performance, Go is as good as C and C++, if not better. There are no virtual machines in Go, and it compiles to machine code which means programs are executed impressively quickly. The built-in garbage collector in Go identifies occupied memory that is no longer required, and frees it up for later use. This effective memory management also lowers the risk of security vulnerabilities considerably due to code encapsulation.

Node.js on the other hand is akin to JavaScript owing to its asynchronous, non-blocking nature. This means, smaller and simpler tasks are performed in the background without impacting the main thread. Additionally, it’s based on the V8 engine – the fastest JavaScript engine available. However, with JavaScript being an interpreted language, code execution takes longer in Node.js compared to Go.

So, performance-wise, Go ranks a bit higher than Node.js.


Concurrency in a mobile app is the app’s capability to efficiently utilize CPU to deliver great performance i.e. the app’s programs organize their execution into separate flows while facilitating communication between them utilizing only enough CPU power required for their execution. Concurrency is vital for apps that handle thousands of requests simultaneously. This attribute also translates to the app’s scalability

And concurrency is one of Go’s major benefits thanks to its lightweight Goroutines. The environment of Go allows developers to run a lot of Goroutines in parallel without using too much RAM while also hiding the complexity of the process.

The single-threaded Node.js may sometimes find CPU-bound tasks blocking the event loop which in turn slows down the program resulting in a slower app. Though this doesn’t always happen, it’s still something worth considering. Node.js already proved itself as a great choice to build super-fast, scalable apps. But the technicality that we just mentioned gives Go a slightly higher score again in terms of concurrency.


The right set of tools can cut app development costs significantly. When it comes to tools, Node.js is far ahead of Go.

Node.js features a microservices architecture which means that a single app is divided into smaller modules each with their own operational interfaces which makes adding new components to the app much easier and faster. This is complemented by Npm (Node.js package manager) that comprise over 800,000 ready-made ‘building blocks’ or tools that can be installed and used on the go.

Though Go has a smaller number of tools compared to Node.js, it features a great library brimming with features that don’t need third party support. However, the absence of a built-in GUI library drops Go’s scores here even further.

Node.js leads when it comes to the number of useful tools available.


As both Node.js and Go are open source, it’s obvious that they both have communities engaged in improving them in many ways. They have repositories on GitHub as well. However, with Node.js being a more mature tool, its community is much bigger and more vibrant. It has reached 1 billion downloads and 56,000 stars on GitHub. So finding a Node.js specialist won’t be difficult.

The Go community, though smaller compared to Node.js community, keeps growing at a rapid pace every year. With Google offering great support to push Go into mainstream, migrating to Go doesn’t seem like a bad investment at this point.

What big corporates think about Node.js & Go

Netflix, arguably the world’s biggest media streaming platform, has their app built on Node.js, and they are only praise for the open source runtime environment. LinkedIn is another Node.js supporter along with Groupon which can now process more than 425,000 active deals without hassle thanks to Node.js.

As for Go, the list is impressive and growing. Uber, that used to rely on Node.js, migrated to Go in 2016 in order to improve the performance of their geofence lookup microservice. But there were other reasons too which is a topic for another time. In addition to Uber, Google, Docker, BBC, Intel etc. all use Go highlighting Go’s simplicity.


By now, you might have realized the fact that Go has great potential. Even so, it’s not possible to definitively say that one is better than the other. Making a choice between the two depends on type and traits of the app that one wants to build.

Go is great for microservices and enterprise-grade app development but Node.js has a plethora of ready-made solutions that significantly reduce custom software development time. If you still can’t choose between the two, drop us a line and talk to the experts at AOT.

Having a Microservice architecture and reaping benefits from it are two different things which can be made possible only with an effective strategy to bypass both organizational and distributed computing challenges that will be encountered along the way. Microservices certainly offers a great many benefits to an enterprise albeit with tradeoffs. You will be getting a lot but they will take a lot from you as well.

Here’s an overview.


The most highlighted advantage of microservice architecture is that it’s a multi-dimensional autonomous unit. This autonomy can be looked at from different perspectives.


When you look at the deployment aspect, you can say that microservice, being autonomous, can be deployed independently from other services. Autonomous microservices can be cohesive i.e. all parts of the application (written in the source code) would be strongly bonded to each other.

The autonomy in deployment is also influenced by technologies which makes it vital to choose technologies wisely while developing applications. If the technology doesn’t align with the company’s competency and goals, that can lead to complications. For instance, it’s still possible to build applications with a different technology stack. However, the API should be made technology-agnostic.


The need for scaling an application arises when there is a need to serve bigger loads and when the system needs to be more resilient in case of failure. In a monolithic architecture, scaling is quite tedious. We will have to scale the entire system, all components included. Vertical scaling like this may not always be feasible. When it comes to microservices, horizontal scaling of applications is possible – by adding new nodes independently (which can also be automated).

Isolated diagnosis

The autonomy of microservice architecture allows us to isolate and investigate problems and issues within particular services while not affecting other services. The system would still be working while the investigation goes on and the solution is implemented.

Organizational culture

Autonomy can also be viewed from an organizational culture perspective. The culture should be such that a single Agile team manages and develops each microservice. They will have to take responsibility for the whole lifecycle and should be able to coordinate and collaborate their efforts into delivering the best software possible.


Businesses can reap all benefits that microservices offer provided they sacrifice something else in return. It’s no Faustian pact but those who want to implement a microservice architecture should know what they would be giving in return to leverage the potential of microservices.


As mentioned before, microservices facilitate horizontal scaling which is not just about increasing the number of service instances. The architecture should make the scaling transparent to consumers as well. Then comes the load balancing challenge without compromising transparency. For this, we can use client-side or server-side load balancing which also makes it easier for the traffic to get to new instances. Modern cloud platforms like AWS and Kubernetes can automate scaling.

Letting go of static configurations

Traditionally, applications running on physical hardware have relatively static locations. File-based configurations with predefined URLs are enough for the services to communicate in such environments. A cloud-based microservice environment is different however, as services can have multiple instances with dynamically allocated resources. Due to this, static configurations won’t cut it.

This is where service discovery plays a role. It helps services discover each other dynamically using identifiers. Enterprises can go for either client-side service discovery or server-side service discovery. Many popular cloud platforms today offer this functionality.


If the system just accommodates a single application, it’d be easy to monitor the application or locate its log files for troubleshooting. It is also easier to profile the application and identify bottlenecks should the user face responsiveness issues in the application. However, these activities tend to be harder in a distributed environment.

The system may have numerous applications running with several instances on multiple (often dynamically assigned) nodes. Finding what went wrong and where it went wrong in such a system can be challenging. Even if it’s just a responsiveness issue, the complexity of a microservice architecture can make it difficult to resolve it.

If it’s just two or three applications we are talking about, we might be able to manually go through the logs in each instance. But if there are over a dozen applications involved, the time spent looking into an issue disregards the benefits that a microservice architecture brings with it. The system would be hard to maintain. Of course, there are workarounds to this particular problem. But the point is that there are system monitoring tradeoffs in a microservice architecture.

Continuous Delivery

Making changes in applications tend to have a notable impact on the system in a monolithic architecture. Meanwhile, when there are small, independent applications in a microservice architecture, making changes is much quicker. Features can be deployed as quickly as they are developed with a Continuous Integration approach.

Safe deployment on production is vital, and requires many conditions to be checked within the build pipeline. This is the foundational principle of Continuous Delivery. With a microservice architecture, changes should be responded to quickly and should be ready for deployment once the code is pushed to the repository. Deployment won’t always be easy in practice. There should not be a line between developers, testers, and people in charge of deployment. A strategy for effective collaboration should be in place.


There are many reasons why microservice is so popular today and widely adopted. Obtaining all the value it brings to an enterprise with its autonomy that influences everything from organizational culture and data management to deployment, scalability and resilience, requires tremendous effort. The foundation for an effective microservices architecture includes service discovery, load balancing, monitoring, continuous delivery etc. which means it won’t be cheap to implement. This blog explored the effort required to leverage a microservices architecture and the tradeoffs along the way.

Hopefully, this blog helped you determine whether your infrastructure is capable of providing a complete microservice autonomy. If, in case, it doesn’t, AoT can help you out. Our analysts can help you set up a microservice architecture and help you understand what you will get and what it will take. Get in touch with us today to learn more.

Automation in IT is on the rise, particularly in infrastructures and operations. Many enterprises chose to adopt automation practices last year primarily because others started doing it, or just to get ahead of the competition may be. Automation is nevertheless one of the hottest trends in IT now, but what does it do? Why automate?

Essentially, automation helps an enterprise leverage the potential of modern technologies and methodologies to drive business. Today’s dynamic business ecosystem demands the adaptability and flexibility only automation can give. This makes it a necessity today than just a trend.

Apart from that mentioned above, companies that are fully aware of what automation can do choose it because of several other factors. The increasing adoption of automation also has to do with factors such as APIs, cloud technology, machine learning etc. The cloud’s already dominating, and machine learning prospects are impressive. The potential of a right mix of these factors fuel the adoption of automation.

Let’s look at these factors in detail.

Cloud, containers, and microservices

Cloud technologies lead the technical factors that accelerate automation adoption, especially in the operations department of an enterprise. However, the cloud is still a sophisticated technology which is challenging to use and optimize. It can be particularly challenging for a startup that’s expecting to grow at a fast pace (the infrastructure would grow in parallel subsequently requiring complex optimization of the cloud computing tech in use).

Containers and microservices helped reduce such complexities for many organizations. But this also indicates that the addition of automation can be vital. For instance, it’s possible for most enterprises to deploy a couple hundred servers manually. But if the business grows and there are thousands of servers to deploy, manual effort is no longer an option. Automation can do just that – deploy scalable workloads without increase in staff.

Kubernetes is a good example that fits this context, allowing organizations to utilize the potential of cloud, containers, and microservices effectively even while the operational needs evolve.

Increasing delivery speed of software development

The many changes in IT over the years do not pertain to technologies alone. Customer demands have changed as well. They expect faster delivery from IT now. In the case of software development, the age old monolithic lifecycle is not enough now.

IT is expected to be fast enough to drive market share, meet customer expectations, and deliver faster. Such expectations now started becoming demands. For IT to get faster, there should be a shift from manual human-driven culture to a machine-driven culture.

Modern software development culture, including the Agile-DevOps culture, can be complemented with automation practices that reduce deployment times without compromising quality. Automation brings scalability and flexibility into the mix (with the cloud enhancing it even further) i.e. people won’t have to put a lot of effort to sustain and grow the business environment. Continuous deployment platforms, containers, and other similar technologies are better leveraged with the help of automation.


If you observe keenly, you can see the involvement of APIs pretty much everywhere in the digital realm. APIs also eliminated one particular vulnerability of automation.

For automation to be effective, it needs to be capable of interacting with the diverse resources in the infrastructure the same way as humans interact with applications. A while back, automation wasn’t capable of such a feat, which became one of its biggest limitations. APIs changed all that.

Programmable APIs for an infrastructure now enables automation to do all that and more regardless of whether it’s on-premises or on the cloud.

The people factor

One of the main challenges for every enterprise is to do more with less. Imagine if an enterprise’s human talent spends too much time on manual tasks. The big picture view here would make it clear that the enterprise is not getting the best work from their staff. Essentially, they are NOT doing more with less.

Automation in an enterprise IT workspace addresses this, ensuring that the right people perform the right tasks. As a matter of fact, this is one of the major use cases of automation. While automation takes care of most of the manual tasks, people can focus their critical thinking and problem-solving capabilities more on strategic IT initiatives thus promoting a healthier work culture. The end business outcome goes positive considerably as people start working on tasks that match their experience and capabilities.

The cost factor

Last but not the least, there is the cost factor that translates from the people factor. For an enterprise, if its senior engineers are working on maintenance tasks, it’s safe to say that the enterprise is paying a lot for the maintenance. The task can be limited to junior employees. However, present industry standards still would demand the enterprise to spend a lot.

As mentioned earlier, most manual tasks can be automated now, enabling employees to focus on mission-critical tasks. So there are cost savings involved. Cost savings is one of the major reasons that drives automation projects in enterprises. That is not a healthy approach.

The approach should be looking at opportunity costs too instead. It is lucrative for an enterprise to redirect their staff from manual work to more strategic tasks which automation can do. Then there is the benefit of faster time-to-market. Without such benefits from automation, the enterprise would be spending more and doing less. Automation presents them the option to make something out of these small windows opportunity. Every little contribution reduces spend one way or the other.

Smart businesses capitalize on opportunity cost and not on how they would have to spend to implement automation.

So have you checked if your business has what it takes to drive automation? Is it cloud-driven? Do you use APIs? If you still haven’t leveraged automation for your business, you will lose your foothold very soon. If you want to see what automation combined with the cloud can do, you should seek an expert for answers. If you are reading this, you just found yourself the expert.

AoT’s expertise in all these factors that fuels automation makes us one of the best help you can get in getting your business ahead of the game. Get in touch with us to learn how automation fits your business’ ecosystem.

The Microservices – Service Oriented Architecture (SOA) comparison has been a hot topic of workplace discussion for a long time now. Microservices is relatively new though the idea isn’t, and is considered by many as the future of architectural style as it makes better use of current technologies including containers and automation. And this makes people wonder what makes one different from the other.

For starters, the main component for both is services. But they are fundamentally different, and have different characteristics.


It’s rising in popularity as the go-to architectural style to develop highly scalable applications owing to its capability in addressing many problems and issues with large, sophisticated applications. The service-based architecture comes with independently deployable services and an inter-service communication protocol (JSON, REST etc.).

However, when you consider service type classifications, the service taxonomy of microservices is notably limited. Despite this shortcoming, the architectural style provides considerably superior control throughout application development and testing cycles.

Service Oriented Architecture

What makes SOA unique is that it has been constantly evolving over time. Enterprises benefited the most as it brought order to various combinations of enterprise-level software. SOA also uses service communication protocols while representing the software combinations as collections of services. Considering these characteristics, SOA can be described as essentially a superset of microservices.

SOA’s shared data model is sophisticated. There can be many complex relationships between data structures, models, and hierarchies. However, this tiered organizational structure facilitates messaging functionalities and efficient coordination between services.

Now let’s get to the key aspects of both architectures.

Services Decoupling

Because of the shared data model, the services and other components of the service oriented architecture will have tight data coupling, and more resistance to changes. Though this isn’t necessarily a demerit, it still demands additional re-testing in many instances to ensure that the changes haven’t negatively impacted any service.

Microservices, on the other hand, are designed to connect a single service and its data. This association considerably minimizes sharing of services. When sharing is demanded by the system, the architecture replicates common functions across services without going for data sharing. Data is essentially decoupled facilitating more frequent deployments, but limiting the testing scope.


SOA has an organized multi-tier model featuring a central messaging middleware layer, while microservices feature an API layer. This is where you can notice the not so subtle differences.

API Layer traits:

  • Comparatively simpler than SOA’s central messaging layer
  • Makes it easy to change internal data representations
  • Makes it easy to change services’ granularity

Central Messaging Layer traits:

  • Routing, message transformation, and mediation capabilities
  • Adds to the complexity
  • Elevated data and functional coupling degree
  • Increased maintenance costs

So conclusively, each comes with its own fair share of merits and demerits.


SOA, as mentioned above, brings order to the chaos thanks to a central hub controller. Microservices can also do the same but uses the inter-service communication protocol instead.

But the ‘service chaining’ aspect of microservices makes it unique. A microservice can call a different microservice when it needs the help of the latter to complete its function. The latter can call other microservices too if necessary. Though this ‘service chaining’ can make things less sophisticated from a technical standpoint, too much chaining can still have unexpected setbacks.


What’s evident from the analysis is the fact that SOA is more suited for heterogeneous applications in complex enterprise systems while microservices is generally the ideal option smaller and less-complex web-based applications that do not require explicit service coordination. The latter hence can play a key role in continuous deployment models of software development.

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