What is angular ?

Angular is a popular open-source front-end web application framework developed and maintained by Google. It is primarily used for building dynamic single-page applications (SPAs). that are dynamic, interactive, and responsive.

What is top use cases of angular?

Some of the top use cases of Angular include:

1. Web Application Development: Angular is highly suitable for building complex web applications with rich user interfaces. It offers a wide range of tools and libraries that enable developers to create interactive and responsive web experiences.
2. Mobile App Development: Angular can also be used to develop mobile applications using frameworks like Ionic. This allows developers to build cross-platform mobile apps using the same codebase, saving time and effort.
3. Progressive Web Apps (PWAs): Angular provides the necessary tools and features to create PWAs, which are web applications that can be installed on a user’s device and function offline. This enables users to access the app even without an internet connection.

What are feature of angular ?

Features of Angular:

• MVC (Model-View-Controller) architecture: Angular follows the MVC architecture, which separates the application’s data, presentation, and behavior. This makes the application easier to understand, test, and maintain.
• Two-way Data Binding: Angular allows for seamless synchronization between the model and the view, ensuring that any changes made in the model are immediately reflected in the view and vice versa.
• Dependency Injection: Angular makes use of dependency injection to manage the application’s components and their dependencies. This promotes code reusability and simplifies the development process.
• Directives: Angular’s directives enable developers to extend HTML with new attributes and tags, making it easier to create dynamic and interactive web applications.

What is the workflow of angular ?

The workflow of an Angular application typically involves the following steps:

1. Create an Angular project using the Angular CLI (Command Line Interface).

2. Define components, services, and modules to structure your application.

3. Write HTML templates and CSS styles for the user interface.

4. Implement data binding and handle user interactions within components.

5. Configure routing for navigating between different views.

6. Test your application using automated unit tests and end-to-end tests.

7. Build and deploy your application for production.

How angular Works & Architecture?

How Does Angular Work:

Angular works by combining HTML templates, CSS styles, and JavaScript code. When a user interacts with the application, Angular handles the events and updates the view accordingly. It uses a concept called two-way data binding, which means that changes in the model are automatically reflected in the view, and vice versa. 

Architecture of Angular: Angular follows a modular and scalable architecture, which makes it easy to maintain and extend the application. It consists of several key components:

1. Modules: Angular applications are divided into modules, which are self-contained units of functionality. Each module can have its own components, services, and other resources.
2. Components: Components are the building blocks of Angular applications. They encapsulate the user interface and handle user interactions. Each component consists of a template, styles, and code.
3. Services: Services provide functionality that can be shared across multiple components. They are used to manage data, perform HTTP requests, and handle other tasks.
4. Directives: Directives are used to extend the functionality of HTML elements. They allow you to add custom behavior to existing elements or create new elements.
5. Templates: Templates define the structure and layout of the user interface. They are written in HTML and can include Angular-specific syntax.
6. Dependency Injection: Angular uses dependency injection to manage the dependencies between different components and services. This allows for loose coupling and easier testing.

How to Install and Configure angular ?

Installing and Configuring Angular:

1. Install Node.js: Download and install Node.js from https://nodejs.org.
2. Install Angular CLI: Open a terminal and run the following command to install the Angular CLI globally:

npm install -g @angular/cli

Step by Step Tutorials for angular for hello world program

Step-by-Step Tutorial for a Hello World Program in Angular:

1. Create a New Project:

ng new hello-world-app
cd hello-world-app

2. Generate a Component:

ng generate component hello

3. Edit the Component Template:

Open src/app/hello/hello.component.html and replace its content with:

<h1>Hello, Angular!</h1>

4. Use the Component:

Open src/app/app.component.html and replace its content with:

<app-hello></app-hello>

5. Start the Development Server:

ng serve

This will start the development server. Open your browser and navigate to http://localhost:4200 to see your “Hello, Angular!” message.

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Scientists, researchers, and engineers are solving the world’s most important scientific, industrial, and big data challenges with AI and high-performance computing (HPC). Businesses, even entire industries, harness the power of AI to extract new insights from massive data sets, both on-premises and in the cloud. NVIDIA Ampere architecture-based products, like the NVIDIA A100 or the NVIDIA RTX A6000, designed for the age of elastic computing, deliver the next giant leap by providing unmatched acceleration at every scale, enabling innovators to push the boundaries of human knowledge and creativity forward.

The NVIDIA Ampere architecture-based products implements ground-breaking innovations. Third-generation Tensor Cores deliver dramatic speedups to AI, reducing training times from weeks to hours and providing massive inference acceleration. Two new precisions – Tensor Float (TF32) and Floating Point 64 (FP64, NVIDIA A100 only) accelerates AI adoption and extends the power of Tensor Cores to HPC.

TF32 works just like FP32 while delivering speedups of up to 10x for AI without requiring any code changes when utilizing sparsity.  Automatic mixed precision and FP16 can be invoked for performance optimization by adding just a couple of lines of code. With support for bfloat16, INT8, and INT4, NVIDIA’s third generation Tensor Cores are an incredibly versatile accelerator for AI training and inference. By bringing the power of Tensor Cores to HPC, the NVIDIA A100 enables matrix operations in up to full, IEEE-certified, FP64 precision.

Every AI, data science, and HPC application can benefit from acceleration, but not every application needs the performance of a full Ampere architecture-based GPU. With Multi-Instance GPU (MIG), supported by the A100, the GPU can be partitioned into up to seven GPU instances, fully isolated and secured at the hardware level with their own high-bandwidth memory, cache, and compute cores. This brings breakthrough acceleration to all applications, big and small, and delivers guaranteed quality of service. IT administrators can offer right-sized GPU acceleration for optimal utilization and expand access to every user and application across bare-metal and virtualized environments.

The A100 SXM4 configuration with 40 GB of GPU memory brings massive amounts of compute performance to data centers. To keep these compute engines fully utilized the DGX A100 provides class leading 1.6 terabytes per second (TB/sec) of memory bandwidth, a 67 percent increase over the previous generation. The A100 also has significantly more on-chip memory, including a 40 megabyte (MB) level 2 cache – 7x larger than the previous generation – to maximize compute performance. The PCIe board version retains the 40 GB of HBM2 GPU memory, with a memory bus width of 5120 bits and a peak memory bandwidth of up to 1555 GB/sec, easily taking the performance crown from the prior generation Tesla V100.

Scaling applications across multiple GPUs requires extremely fast movement of data. Third generation NVIDIA NVLink in the A100 SXM4 doubles the GPU-to-GPU direct bandwidth to 600 gigabytes per second (GB/sec), almost 10x higher than PCIe Gen 4. The PCIe 4.0 A100 implementation also features a total maximum NVLink bandwidth of 600 GB/sec. NVIDIA DGX A100 servers can take advantage of NVLink and NVSwitch technology via NVIDIA HGX A100 baseboards to deliver greater scalability for HPC and AI workloads. For those who prefer to deploy PCIe motherboards the NVIDIA A100 PCIe option fully supports NVLink.

Contemporary AI networks are big and getting bigger, with millions and in some cases billions of parameters. Not all of these are necessary for accurate predictions and inference, and some can be converted to zeros to make models “sparse” without compromising accuracy. Ampere architecture-based Tensor Cores in the NVIDIA A100 or RTX A6000 provide up to 10x higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also be used to improve the performance of model training.

NVIDIA Ampere architecture-based second-generation RT Cores in the NVIDIA RTX A6000 and NVIDIA A40 GPUs deliver massive speedups for big data analytics, data science, AI, and HPC use cases where seeing (visualizing) the problem is essential to solving the problem. RT Cores enable real-time ray tracing for photorealistic results and work synergistically with Tensor Cores to deliver AI denoising and other productivity enhancing features.

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If ever there has been a need to adapt, 2020 has let it be known that this is the year. COVID-19 has brought about all kinds of workplace changes, from remote work with video meetings to temporary layoffs. It may seem like just another buzzword, but microservices can help human capital management (HCM) systems face the multitude of challenges that 2020 brings and help ensure workforce resiliency.

What microservices are

Darshan Kapadia, director of platform engineering at Lifion by ADP, explains that microservices are a way of designing and developing software so that each of the individual components can be worked on and deployed independently of the others. The key here is components are self-contained. This architecture allows for a rapid software release cycle because one component team doesn’t need to wait for another to finish before deploying.

Compare this model to more traditional monolithic architecture, which is still used widely: The product is developed as a whole by one large team and released as one large product. That means all the smaller teams working on components need to coordinate and deploy the product simultaneously.

Why microservices work so well

There are two reasons Kapadia touches on that explain why microservices have an edge.

1. Releasing a new product in small pieces that can run independently, instead of one large product, allows teams to focus on their own smaller piece of code. There’s a lower chance of failure when a new product is deployed, “and the blast radius is small,” he says. In addition, bug fixes and performance updates can be deployed as they are ready on an individual basis vs having to sustain a longer cycle between updates.
2. With constant innovations in technology, microservices enable organizations to add new components without waiting for a complete overhaul of an outdated system. Kapadia explains how at Lifion, they went from self-managed databases to entirely genuine in just under nine months. “That is unimaginable if you are in a traditional monolithic system with multiple databases,” he says. “It isn’t possible because you have to coordinate everything before that can happen,” something that can take a year’s depending on system complexity.

Moreover, the research and improvements happening in the UX space are significant right now, and in order to get those tools into the hands of customers, UI needs to be a separate component that can be deployed independently.

Microservices also employ simple routing methods, receiving requests, processing those requests, and then responding accordingly. This is in contrast to architecture involving enterprise service buses which use more complex integration approaches.

The HCM pivot during COVID-19

Most, if not all, organizations have needed to pivot as the pandemic changed the way we work. In the case of HR products, there have been changes in taxation and new payroll terms added as well as return-to-work protocols.

Microservices can expedite these types of changes, making them easier to deploy than a traditional product, since the tech team is able to work continuously behind the scenes, releasing updates as they are ready.

Not only that, with everyone working from home for months at a time communication can become a problem no matter how many video calls are scheduled. With microservice architecture, however, “communication doesn’t become a barrier,” explains Kapadia. “That’s because the microservices are talking to each other with a very strict set of contracts that can’t be broken.” This allows team members to perform updates behind the scenes with minimal communication, he says.

Aside from the presence of the global health event, what makes microservices so valuable to HCM is the ability to mitigate costs by scaling services as needed and independently of others. Spikes in traffic for specific systems, such as payroll, time, or benefits, come at predictable times, Kapadia says, so scaling each one individually rather than altogether for every single one of those times can save money.

What you should have first

Kapadia explains that there are some circumstances in which a company won’t benefit from microservices. An organization should be completely on board with the microservice paradigm, since services will be divided into small chunks for different teams. All teams should be in agreement, he says, because while each piece is developed independently, they need to all work together. It’s also critical to ensure that the boundaries of microservices are well defined. If a large number of microservices need to communicate for performing a single task, it can cause performance issues due to network latency between services.

In addition, it’s important to have a robust Continuous Integration and Continuous Deployment (CI/CD) pipeline in place because developing, testing, and deploying products will take place independently. The right tools and platforms should also already be in place, with active support for flexible runtime deployment and automated monitoring, as well as container hot deployment.

Organizations need to be agile and willing to adapt to a larger variety of scenarios than ever. The ability to pivot and adapt quickly is exactly what microservice architecture is designed for.

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In order to operate correctly, microservices need to discover each other in an intelligent manner. Some development teams try to manage this by locating where particular microservices are at a single point in time, and then writing code that links these locations together. The problem is the location of these microservices can change abruptly. If the code is tied directly to those locations, the services will fail as the architecture changes.

Organizations need a mechanism to change how microservices transmit their state and location. This is where service discovery comes to the rescue, which uses a dynamic database registry of available microservices to determine where each instance resides at any given time. This registry operates through both a management and a query API set, where the management API registers the microservices and the query API calls them.

In this tip, we’ll examine the basics of service discovery in a microservices architecture, including:

• the differences between client-side vs. server-side discovery;
• the specifics of self-registration vs. third-party registration; and
• the three most common service registry scenarios.

Client-side vs. server-side discovery

With client-side service discovery, it’s the client’s responsibility to identify where the needed microservices are at any time. The client operates its own service registry by maintaining and regularly updating a dynamic list of available microservices. Although this approach has its pros, such as being helpful with load balancing, the major drawback is that every client must constantly retain the logic it uses to interact with the microservices.

Server-side service discovery keeps the registry on the other side of the fence, generally behind a load balancer. Again, this service registry contains a dynamic list of all available microservices and their current location. The benefit here is that it only requires a single service registry. However, architects must ensure that this registry maintains high availability, perhaps via live mirroring of the registry across the cluster of instances. Also, they need to carefully manage service caches to mitigate data inconsistencies.

Self-registration vs. third-party registration

Microservices must be able to both add and remove themselves from a service registry. This can be accomplished via either a self-registration or third-party registration approach.

In the self-registration approach, the microservice retains the code specific to the service registry in use. For example, Netflix OSS has its own service registry, Eureka. Any microservice that wants to register with it just needs to have the @enableEurekaClient annotation within its code. A more generic approach to this would use lines such as:

Register("microservicename, "<ipaddress:port>")
Heartbeat()		
Unregister()

With self-registration, it’s the microservice’s job to tell the registry when it is and isn’t available. This requires that the microservice be able to send a message to the registry even in the event of a total failure. As with client-side discovery, self-registration needs to be applied for every framework being used.

In third-party registration, a service registrar takes over the job of constantly registering and monitoring the microservices in the service registry. The registrar investigates the general collection of services, and creates the code required to add the microservice to the registry once it finds the right one. It will also maintain a list of non-available microservices, eliminating the need for the individual microservices to announce a failure.

Of course, this registrar must be able to recognize new microservice entities and readily support the specific service registry you use. Preferably, the registrar should support a large number of registry types from the get-go.

The three main types of service registry scenarios

There are three main types of service registry scenarios you are likely to come across:

• A “home-grown” environment where development teams coded their own service registry to deal with client-side or server-side microservice interactions. This is not recommended as maintaining such a platform is difficult and prone to error.
• A distributed data store service that provides a service registry, but requires self-registration. Platforms such as Apache Zookeeper fall into this camp.
• An orchestration framework that provides both a service registry and registrar, and automatically handles third-party registration. Examples of these orchestration frameworks include Netflix OSS, Kubernetes and Marathon.

The choice comes down to the development team’s preferences — it is not recommended to try and mix these approaches. Self-registration offers a slightly higher degree of control and granularity in regard to the registration process. On the other hand, third-party registration provides simplicity and automation.

However, it may be your existing platforms that force the decision. If your team is already using Kubernetes, then automatic registration is the easy option. If using etcd, then you can choose to either use self-registration or introduce a separate third-party registrar. Registrator, an open source system that supports etcd, is an example of one of these third-party registrars.

Overall, third-party registration with server-side service discovery in microservices is the preferred approach, as it provides a way around the problems of complex client-side architectures. However, the precise details of your own existing platform will likely dictate your choice.

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Though containers and microservices have transformed back-end development, they have yet to make significant inroads in the client- or front-end tiers. But developers have now found ways to apply the successful software development strategies with back-end microservices to client-tier applications as well.

Client-side applications that are developed and deployed with the same development and deployment strategies that have made microservices so popular are referred to as micro front ends. The software industry has recently taken a great deal of interest in them and micro front-end architectures. For example, the latest version of Red Hat Process Automation allows teams to weave dynamic AI modules into these micro front-end applications that are easier to decentralize.

Container-based micro front-end architecture

Early micro front-end projects have focused on how to provide better separation of logic and UI elements into smaller, more dynamic components. But modern micro front ends have moved far beyond the idea of loose coupling code to full scale Kubernetes-based deployment. There’s even been a recent trend of micro front ends containerized as microservices and delivered directly to the client.

For example, the H2 app by Glofox recently adopted this approach to implement a PaaS for health and fitness apps, which gyms and health clubs then customize and provide to clients. The app uses the edgeSDK from Mimik Technology Inc., to manage the containerized micro front-end microservices deployment to run natively across iOS, Android and Windows devices. 

In addition, a micro front-end deployment reduces the server load. It only consumes client-side resources, which improves response times in apps vulnerable to latency issues. Users once had to connect to databases or remote servers for most functions, but a micro front end greatly reduces that dependency.

Micro front-end architecture pros and cons

ThoughtWorks, a global software consulting firm, has recently shifted its recommendation from micro front-end trials to adoption. This approach promises smaller, more cohesive codebases, easier development across teams and easier component updates in a more incremental fashion.

It’s still early days for micro front-end development. But Cam Jackson, a full-stack web developer and consultant at ThoughtWorks, cautions that developers who work with micro front ends face a number of challenges, including over engineering. Independent developer components can duplicate common functions, which in turn leads to larger apps. There can also be environmental differences between where they develop and deploy components.

Micro front ends can also lead to more operational complexity as a result of more repositories, build pipelines, servers and domains to manage.

Developers have expended a great deal of energy in the creation of microservices frameworks and strategies that has resulted in revolutionized back-end development and deployment. With micro front ends, we now begin to see that expended energy pay off with client-side development as well.

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A monolithic architecture is one built from a single piece of material; therefore, a monolithic application has a single code base with multiple modules that are divided into business features and technical features.

Microservices is an architecture used to separate a monolithic application into several independent services. A microservice application consists of a collection of services. Each service can have multiple runtime instances and be deployed independently.

Both monolithic and microservice architectures have several advantages and disadvantages when compared to each other, particularly when it comes to operational overhead requirements. Let us look at the comparison between monolithic vs microservices.

Advantages of monolithic architecture.

One codebase: Monolithic architecture is built as one large system and is usually one code base services with their codebase within an application.

• Application integration: The idea of application integration in monolithic architecture has made it easier to use the system. Monolithic architecture integrates well with services such as messaging and Rest API.
• Accessibility: Monolithic apps can easily handle a range system that makes one to access it easily such as monitoring performance, logging, and configuration management.
• Technology: A monolithic application must use the same technology stack throughout.
• Memory: Components in a monolith typically comes with a performance advantage, the reason behind it is that its shared memory access is faster than even ICP.

Disadvantages of monolithic architecture.

• An error in any of the modules in a monolithic kernel can bring the entire application down.
• Changes to the technology stack are sometimes expensive although this will vary with many reasons, both in terms of the time and cost is involved.
• Issues with security may occur because there is no isolation among various servers present in the program.
• The large codebase of a monolithic application can be hard to understand.

What are microservices?

• Microservices are a way of breaking large software projects into loosely coupled modules. They communicate with one another through application programming interfaces.
• Advantages of microservices architecture.
• Microservices can be more beneficial for complex and evolving applications. It offers practical solutions for handling a complicated system of different functions and services within one application.
• The user only needs to scale certain components of the application, which optimizes resource usage to scale a microservices-based application.
• Microservices components are loosely coupled, so they are not interdependent and can be tested individually.

Disadvantages of microservices.

• As the application grows, so does the code base and this may overload your development environment every moment it loads the application. In turn it reduces the developer productivity.
• Sometimes the application may be packaged in one EAR or WAR thus when one wants to change the technology stack of the application might be a challenge.
• On the other hand, if any single application function fails then the entire application goes down. In other scenarios. if a particular function starts consuming more processing power then the entire application, performance can become compromised.

Conclusion

The article has illustrated the difference between monolithic vs microservices architecture. Having said that, you will understand a microservice is basically used to separate a monolithic application into several independent services.

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Almost all insurers are currently investing three-digit millions in the conversion of their application landscape. The reason for this is that existing self-developed inventory management and damage systems are getting old and cannot meet current requirements for flexibility, running costs or innovative ability. The legacy systems are mostly replaced by standard software from the market, which is often extensively adapted to the needs of the insurer. At the end of the migration project, some of the legacy applications that were still programmed in COBOL will be replaced by software that was developed in a future-proof programming language. A guest post .

In parallel to the replacement of the legacy systems, most insurers are also pushing ahead with their cloud strategy and want to migrate their systems to the cloud or obtain functionalities from the cloud “SaaS” from third parties. This should save costs and improve scalability.

The architecture of the application and infrastructure landscape needs to be adapted

On closer inspection, however, the high expectations are not fulfilled with this procedure. With the introduction of standard software, the largely BiPRO-compliant business process standards of the manufacturer are adopted, but the basic architecture of the application and infrastructure landscape is not changed. Ultimately, only old applications are replaced by new ones and one or the other application is no longer operated in the company’s own data center, but in the cloud.

The classic architecture of front-end, middleware and back-end remains unchanged, but the desired improvements in terms of scalability and flexibility cannot be realized with this. The hoped-for cost savings are usually not achieved either because the purchased standard software is extensively adapted, which leads to increased costs for further development and operation, and secondly, extensive batch runs and complicated job networks drive the costs for the cloud into the Height.

Development of a state-of-the-art architecture and implementation of microservices

An alternative approach is the development of a state-of-the-art architecture that follows a decentralized service-oriented approach and increasingly focuses on the definition and implementation of microservices. For this purpose, the necessary functionalities such as tariffing, commission or claims management must first be identified and described, for example with the help of a functional architecture map (capability map) or along the customer journey.

The next step is to consider how the corresponding services are provided. One possibility is the in-house development, whereby either existing software is adapted or the functionalities are reprogrammed. The second variant is the use of standard software. Thirdly, technical services can be obtained from the market as software as a service (SaaS). It must be decided in each individual case which of the three options is best suited for which service. It makes sense to develop the individual design of front ends for agents and customers on the basis of quickly changing microservices, while classic, stable and automatable backend processes can be mapped using adapted standard software.

In the case of self-developed or standard software, the insurance company must clarify where the applications are operated. For the time being, the company’s own data center is still available, and a private or public cloud can also be used. When deciding on a cloud service provider, possible risks, such as ensuring data protection, a possible vendor lock-in and regulatory requirements must be examined and assessed. A multi-vendor strategy makes sense to minimize risk, in order to ensure the greatest possible flexibility when changing providers or outsourcing models, for example to comply with stricter regulatory guidelines.

A challenge with this approach is to orchestrate the various decentralized (micro) services between your own data center, the various clouds and the SaaS service providers. For this purpose, the services are connected to each other via an integration layer and corresponding API gateways and controlled and monitored using a cloud management platform. It also determines where which data is kept and evaluated, for example, for analytics, since the data may be distributed across different cloud providers and your own data center.

For most insurers, both appropriate sourcing models and consistent orientation towards (micro) services are new territory, but ultimately the requirements of digitization can only be implemented with a modern architecture.

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Microservice architecture is growing in popularity and its adopters are enjoying considerable success, suggests a new report from O’Reilly. 

The “Microservices Adoption in 2020” report, based on a poll of 1,500 software engineers, systems and technical architects, engineers and decision-makers, states that more than three quarters (77 percent) of businesses have now adopted microservices.

Of those adopters, meanwhile, almost all (92 percent) reported a high level of success. Further, a significant portion of businesses (29 percent) are “betting big” on the technology, looking migrate the majority of their systems to microservices.

“The majority of organisations have already started to migrate their monolithic systems, applications, and architectures to microservices, and many more are looking to begin that transition,” said Mary Treseler, Vice President of Content Strategy at O’Reilly.

“Breaking a monolith into microservices has clear engineering benefits including improved flexibility, simplified scaling, and easier management – all of which result in better customer experiences.”

Containers are also an important factor, according to the report, as half (49 percent) of businesses that claim “complete success” with microservices also deploy at least three quarters of their microservices in containers.

Overall, almost two thirds (62 percent) used containers to deploy at least some of their microservices.

“While container adoption in microservices contributes to microservices success, we saw a lower percent of container adoption than we did in our 2018 report,” said Treseler.

“For some adopters, technical debt from proprietary or monolithic systems might constrain them from using containers and it might be faster and less costly, at least in the short term, to deploy microservices in a database or application server.”

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A migration to microservices is typically driven by flexibility, productivity and scalability demands. However, just because enterprises want these benefits, it doesn’t mean that their business, or IT teams, are ready for the change.

Get advice in the five articles featured here to determine if microservices adoption is right for your project, learn how to get teams ready for such an architecture, plan and execute the migration, and avoid common pitfalls.

Is your team ready to migrate to microservices?

While many have benefitted from a microservices architecture, that doesn’t mean it is a perfect fit everywhere. Two factors come into play: whether an application will succeed divided into microservices, and if the enterprise software team possesses abilities to create and manage them.

Teams face operation complexities when they go through a migration to microservices. Determine their readiness for the new architecture by evaluating standards for code reviews, CI/CD use, testing practices and other aspects of the code delivery workflow.

Joydip Kanjilal, software architect, proposes investment in these areas to tackle a microservices architecture: componentization, decentralized data management, infrastructure automation and resilient fault tolerance.

Flag any team processes that will not work with microservices. Consider restructuring Agile roles and responsibilities that focus on monolithic application development to work in the new setup.

Who does what on a microservices team?

After determining readiness for microservices architecture, the next step is to structure the development team to design and implement them. In this tip, Kanjilal stresses that a proper structure can help enterprises meet business and development goals faster and with more efficiency. But what type of team is right for you? Three appropriate microservices team structures are:

Conway’s Law. This law is an observation that the design of any system is significantly affected by the communications structure of the development team. Build vertical teams in which each team can handle the UI, APIs, business logic and data access layers.

Build-and-run team. Have a team that is responsible for a single piece of software from end-to-end and can function independently from other teams.

Decentralized structure. Rather than separate teams into silos with no overlap, take a decentralized approach to reduce delays.

Establish the right team culture. Business and software team leaders should work closely with development teams. Development teams should be empowered and have the freedom to make technological recommendations, such as tooling, to the architect.

How do we migrate to microservices?

With your team ready, plan the migration to microservices. First, find the appropriate service granularity for the application source code and design. The wrong service size, too large or too small, can create complexities and overhead costs. Coach the development team to create services that are as independent and self-sufficient as possible.

Next, ensure developers have a diverse tech stack and a framework that can manage front-end components independently from the back-end infrastructure.

Finally, start the migration by decomposing the monolith. Rely on proven techniques, such as the strangler pattern that gradually replaces old systems with new ones, to minimize user-facing disruption.

According to Twain Taylor, analyst, monitoring is vital to a migration to microservices. Two areas that enterprises need to address are services communication and service failure.

Microservices execute independently but communicate to fulfill a user request. Designers might use an API gateway to manage external communication. For service-to-service communications, add a distributed tracing tool to gain more visibility. Additionally, consider a service mesh to enhance service discovery and monitoring capabilities.

Service failures shouldn’t take down an app. A popular option is the circuit breaker pattern in which a trip mechanism breaks the connection between services in the event of a fault. In this pattern, it retries the connection to test it and restores the connection when the failure is fixed.

5 big pitfalls to avoid during migration

Moving from a monolith to a microservices-based architecture is an admirable task, but development teams can find themselves in a heap of trouble in the process. Taylor warns that teams must absolutely avoid these five migration pitfalls:

1. Moving too quickly. Review the state of existing apps and verify that the organization has the required Agile and DevOps skills.

2. Making services too small. If teams break their legacy applications into services that are too granular, they will end up with more services than they can manage.

3. Coupling services too tightly. Development teams migrating from a monolithic approach to microservices tend to couple the services too tightly, which can create what is known as a distributed monolith.

4. Storing logs in containers. It is an intense process to log microservices, especially when logs are placed inside distributed container instances and frequently terminated. There are better options.

5. Failure to consider monitoring. The migration to microservices introduces a frightening monitoring scenario, as services are often developed on different stacks. Expect tooling investments.

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As developers, the prospect of working for a big company might bring about feelings of oppression, rather than opportunity. Envisaging endless team syncs, pointless meetings and processes wrapped up in bureaucracy inside a set of procedures. It’s easy to imagine why some are put off. For many though, the fear comes from the thought of being lost in a corporate structure where input is not valued, and skills are wasted.

But every day, as developer concepts become further rooted into corporate behaviors, the line between the two worlds continues to blur. Products are no longer dreamed up in product silos, but from agile teams made up of a diverse cross section of a company. Marketing has dropped drip campaigns in favor of A/B testing, rapid iterations of messaging and data-driven analytics. Big companies no longer mean old school thinking, but fast-changing, inclusive places to really make an impact and be heard. 

Working within the Scaled Agile Framework (SAFe) with an agile architecture of microservices helps us to do our jobs, but the learnings we’d like to share in this article are universal. An organization may not require the SAFe methodology, but the experiences and methods of working can be applied to many roles and are based on three fundamental questions every developer should ask themselves. 

How Do I Plan My Day? 

As with any role, a good start for us is knowing that the previous day went well; that the code we wrote meets the standards required for deployment. Before that, our individual microservices teams will meet to discuss immediate priorities. We let one another know what’s top of mind for us, and any issues we might be facing — and we resolve them together before a midday Scrum meeting.

The Scrum meeting involves the Scrum master, product owner and the developer team and gives us a chance to give feedback and dive into the wider challenges or blockers we’re facing with our own tasks. We might be thought of as solitary creatures, but we actually tend to solve problems together. So, while we have ownership of our own tasks, problem solving comes from collaboration.  

Whether working within the agile framework or not, communication is a huge part of our lives. Our advice to developers everywhere is to be vocal about the need to engage within their organizations. And to business IT decision makers, we know that not every firm has the deep pockets to fund a dedicated DevOps team to implement new tools, change culture or break down silos, but there are many communications and open source DevOps tools that boost collaboration and improve workflows — most are well-known to developers. So, give them the chance to explore how they might bring efficiencies to their work. Help them help the business and maybe suggest trialing some new tools. 

How Do I Upskill on the Job?

Developers are problem solvers. We love the opportunity to help each other overcome challenges. Peer-to-peer communication doesn’t just allow us to get back on course quicker, it lets different teams share knowledge and pitch in to solve problems. We don’t mind being transparent with our peers when we face an issue – in fact, we quite like it, as the offshoot of working together often leads to new skills and technical knowledge. 

Learning the interactions between each microservice, for example, helps us map out the architecture and understand how our own work fits into the bigger picture. The more we talk, the more we see where bottlenecks might occur — and the better equipped we are to achieve larger objectives. As we have complete ownership of our services, from initial coding to deployment, it is our duty to ensure our services perform as they should in production. 

This ownership is something all developers should aim for. Even when working in a more rigid waterfall approach, opportunities to collaborate should be seized and encouraged — even if the only payoff is personal development. Implementing a system of peer code reviews, for example, not only delivers more robust releases, it fosters a collaborative mindset.

Do I Need to Be in this Meeting?

Working with SAFe methodology allows the job to be broken down into iterations linked to business objectives. The most significant of these is determined by meetings that occur every four months, where goals are set and roadmaps established to achieve them. Engineering teams then divide up the features that will be passed on to the developer teams, who break them down into two-week sprints. 

The sprints are what the developer teams work to, under direction from the Scrum masters and product owners. Breaking iterations into smaller sprints ensures we have defined goals, which keep meetings and interactions on track (i.e., daily Scrum meetings and bi-weekly sprint meetings). Without this focus, there’s a risk of derailing the prep required to focus on writing and reviewing code. If we need a discussion about certain features, we make sure to identify exactly who needs to be in that meeting. Above all, we’re all about optimizing our time together. The longer we spend away from our desks, the less time we have to perform our core roles. 

Whatever your part in the software development lifecycle, never be afraid of asking the question, “Do I need to be here?” Your energies may be best used elsewhere.

As you can see, these three questions are critical to developers but can be applied to just about any role in just about any organization. Planning, learning and focusing – seems we’re not so different after all.

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