“The big question that organizations must ask themselves in the digital age is how to respond effectively to the increasing digitization of society, not only in terms of how to avoid becoming obsolete in the face of competition but also how to adapt and lead the way. Digital disruption”.

Digital transformation is a continuous process over time, in which many factors beyond the technological ones are involved. It is of little use to digitize a company if employees are not empowered to adopt digitization in their work. Therefore, an organization’s cultural change is considered the most complex challenge of digital transformation for all companies.

Sustainable digital transformation focuses on carrying out a progressive digital immersion, divided into a series of phases or steps so that the next step in projects are not addressed until the different initiatives of the common step have been completed. To carry out the most complex digital initiatives, it is necessary to go through the following degrees or steps of maturity or digital immersion, the so-called Immersion ladder.

Phases of Digital Transformation 

Digital Foundations: In the first phase, what we call Digital Foundations is established. In this step, initiatives are carried out to establish a base on which the digitization of a company will be built on. The architecture and strategies that coordinate the different digital actions to be carried out include a social media plan, CRM execution, and different digitization processes.

Digital Expansion: The second step is the stage that we call Digital Expansion. In this phase, symptoms begin to appear outside the organization, known as digital contact points. Employees are also being empowered in digital skills, and the results of actions carried out in digital media are being analyzed.

Digital Optimization: We identify the third stage as Digital Optimization. At this point, the actions of the previous step are delved into. The companies that are in this echelon have a true digital culture. They can analyze digital information in an advanced and predictive way, and with self-learning processes. They also interact with their customers through the channels they always prefer, collect their feedback in an advanced way, and can enhance the innovations derived from co-creation.

Digital Maximization: The last step, which few companies reach today, is Digital Maximization. At this stage, automated processes are seen in which authentic artificial intelligence is applied. Large amounts of data, both internal and external, are analyzed, allowing for robust personalization of the customer experience. This allows creating new business models based on the digital world, such as virtual reality or augmented reality.

Pillars of Digital Transformation 

A company that wants to start a digital transformation process must consider key elements that help drive the process forward:

Leadership: The success of digital transformation in a company does not depend only on the degree of digitization but also on its managers and leaders’ ability to drive change. This implies adopting agile management styles that facilitate the evaluation and implementation of new models, sources of income, and opportunities.

Customer Experience: Digital Transformation is closely linked to Customer Experience. It must aim to use technology to create new ways of communicating, predict customer needs and behaviors, and improve omnichannel strategies. 

Business Model: A business model is a way a company creates, delivers, and captures value. Within the Digital Transformation, companies must be willing to evaluate and modify:

• Value proposition: that is, the products and services it offers.
• Value delivery: these are the distribution channels, customer segmentation, and the relationship with them.
• Value creation: the resources and alliances to create the products.
• Value capture: An adequate Digital Transformation also implies a revolution in costs and sources of income. Therefore, the need to have the support of the directives in this function.

Organizational Culture and Agility

Technology within the Digital Transformation is the great pillar within the path of change. However, technology could not act in the desired way if it is not accompanied by processes of practice and relationships between human agents. In this way, technology is linked and shaped by the culture and the organizational context; thus, Digital Transformation will be a more complex process that involves all the actors in the organization.

Digital transformation requires investment in human capital and culture. Also, for a company to respond adequately to rapid changes in society, it must become an agile company.

To learn more about how agile is changing the way we work, listen to this podcast on agile culture and how companies are beginning to adopt it to improve their processes and make them more efficient in the fourth industrial revolution.

Benefits of Digital Transformation 

An adequate Digital Transformation will have 7 positive impacts on:

1. Competitive advantage: Digital transformation allows a company to create new products and services according to customer needs, and this undoubtedly allows diversifying services, making better decisions, and driving growth versus versus its competitors.

2. It promotes the culture of innovation: Agile innovation management in companies is one of the most important tools to adopt new methodologies that deploy the creation of new products, solutions, and business opportunities.

3. Improves productivity: When automation processes are adopted in companies, employees’ better performance is guaranteed, who see digital tools as the ally to achieve the objectives.

4. Greater brand presence: When we talk about a company having an omnichannel strategy in its digital and physical channels, it is because the said company has understood the importance of providing the best service to its customers, and the digital transformation allows these channels to communicate and flow with each other, avoiding setbacks in customer service processes.

5. Give importance to data:  A company’s databases must be converted into stocks to have a greater impact on the market. Digital transformation allows better decisions based on the big data generated through all areas of the company.

6. Reduce costs: When methodologies such as Agile or DevOps are adopted to develop technological products in an agile way, errors in their production are reduced, which gradually lowers the company’s costs. In addition to the automation of processes and cloud computing for information storage, avoiding the purchase of physical servers and their maintenance.

7. Customer satisfaction: It is perhaps the greatest benefit that digitalization brings to companies. Knowing customers allows providing a better experience, more agile and secure services, and direct communication to help the company attract, convert, and retain loyalty more appropriately.

Technology applied to Digital Transformation

Big Data: Big Data is the tools and techniques that allow the real-time processing of large amounts of data collected from different organization’s internal and external sources.

The processing of this data and its subsequent analysis will make decisions based on predictions, anticipate people’s needs, and distribute budgets more intelligently.

Cloud Computing: Cloud computing has great advantages for digital transformation. 

Cloud services allow computer tools such as databases, servers, analysis, networks, and software, within a flexible and low-cost infrastructure. 

The cloud also facilitates access to different technologies, deploys services almost immediately, and has architectures based on microservices, delivering greater agility and scalability.

Mobile: With the arrival of smartphones, mobile technologies’ development has been escalating rapidly, promoting apps that open a new form of relationship between organizations and their customers, suppliers, and workers.

Artificial Intelligence: AI is a set of techniques that allows machines to perform actions rationally. In this way, companies can understand customer needs, suggest the perfect product to the right person and streamline the sales process.

For companies’ benefit, artificial intelligence is used to automate processes and capture and analyze information, bringing benefits such as cost reduction and optimization of services and products. This type of service is widely used in the financial sector, which has pointed to banking’s digital transformation for a few years.

Conclusions 

The digital transformation process in the company is not something that is not trivial and must be implemented with strategic planning. It involves profound changes in the company at all levels and must necessarily encompass all departments from senior management to operators. The digital transformation methodology will help the company develop a digital strategy that will improve the company’s processes, which will translate into an increase in productivity and an increase in the efficiency of the processes.

It can be rather challenging for legacy monitoring tools to monitor ephemeral and fast moving environments like Kubernetes. The good news is, there are many new solutions that can help you with this.

When you are carrying out a monitoring operation for Kubernetes, it is crucial to make sure that all the components are covered, including pod, container, cluster, and node. Also, there should be processes in place to combine the results of the monitoring and creating reports in order to take the correct measures.

Before moving forward, your DevOps team has to understand that monitoring a distributed system like Kubernetes is completely different from monitoring a simple client-server network. That is because monitoring a network address or a server gives much less relevant information as compared to microservices.

Since Kubernetes is not self-monitoring, you need to come up with a monitoring strategy even before you choose the tool that can help you execute the strategy.

An ideal strategy will have highly tuned Kubernetes that can self-heal and protect itself against any downtime. The system will be able to use monitoring to identify critical issues before they even arise and then resort to self-healing.

Choosing Monitoring Tools And When To Monitor

Every monitoring tool available for Kubernetes has its own set of pros and cons. That is why there is no one right answer for all types of requirements. In fact, most DevOps teams prefer to use a combination of monitoring tool sets to be able to monitor everything simultaneously.

Another thing to note is that many DevOps teams often think about monitoring requirements much too late in the entire development process, which proves to be a disadvantage. To implement a healthy DevOps culture, it is best to start monitoring early in the development process and there are other factors that need to be considered as well.

Monitoring during development – All the features being developed should include monitoring, and it should be handled just like the other activities in the development phase.

Monitoring non-functionals – Non-functionals like requests per second and response times should also be monitored which can help you identify small issues before they become big problems.

There are two levels to monitor the container environment of Kubernetes.

• Application process monitoring (APM) – This scans your custom code to find and locate any errors or bottlenecks
• Infrastructure monitoring – This helps collect metrics related to the container or the load like available memory, CPU load, and network I/O

Monitoring Tools Available For Kubernetes

1. Grafana-Alertmanager-Prometheus

The Grafana-Alertmanager-Prometheus (GAP) is a combination of three open source monitoring tools, which is flexible and powerful at the same time. You can use this to monitor your infrastructure and at the same time create alerts as well.

Part of the Cloud Native Computing Foundation (CNCF), Prometheus is a time series database. It is able to provide finely grained metrics by scraping data from the data points available on hosts and storing that data in its time series database. Though the large amount of data it captures also becomes one of its downsides since you cannot use it alone for long-term reporting or capacity management. That is where Grafana steps in since it allows you to front the data which is being scraped by Prometheus.

To connect to Grafana, all you have to do is add the Prometheus URL as a data source and then import the databases from it. Once that integration is done, you can connect Alertmanager with it to get timely monitoring alerts.

2. Sysdig

Sysdig is an open source monitoring tool which provides troubleshooting features but it is not a full-service monitoring tool and neither can it store data to create trends. It is supported by a community, which makes Sysdig an affordable option for small teams.

Sysdig can implement role based access control RBAC into tooling, it has an agent-based solution for non-container platforms, and it also supports containerized implementation. Since it already has a service level agreement implementation, it allows you to check and get alerts according to response times, memory, and CPU. There are canned dashboards that you can use.

The only downside to using Sysdig is that to use it you need to install kernel headers. Though, the process has now been made much simpler with Sysdig able to rebuild a new module everytime a new kernel is present.

While Sysdig is the free and open source, there is premium support available for enterprise customers through Sysdig Monitor, and there are two types of solutions available- SaaS full-service monitoring and on-premise.

3. DataDog

DataDog is a SaaS-only monitoring tool which integrates APM into its services. It provides flexibility with alert monitoring, and you also get access to dashboards through a UI. It can also provide APM for Python, Go, Ruby, and there will soon be a Java support as well.

You can connect DataDog to cloud provider environments, and it can consume data from sources like New Relic and Nagios. With its dashboards, you can overlay graphs, and it can process other APIs which expose data.

There is a service discovery option which allows DataDog to monitor dockerized containers across environments and hosts continuously.

In conclusion

As we mentioned above, monitoring Kubernetes is not an option but a crucial requirement, and it should be implemented right from the development phase.

Microservices and distributed computing have become the new buzz word among DevOps. Everyone wants to migrate their architecture to microservices, mostly because it is the new trend. Because of that, monolithic applications seem more like a burden on cloud computing.

The strange thing here is that monolithic appalications were never claimed as the best option, they just seemed like the most common and convenient option. That is why many companies still start their operations by coding a monolithic core.

Monolithic architecture brings with it many sturdy benefits that we cannot ignore, all because it doesn’t fit well with the modern architectural practices. On the contrary, microservices add complexity to an application, which is not always necessary.

Here are some reasons why you may want to stick to a monolithic architecture:

1. Monoliths are better for complex enterprise apps

The reason why microservices have gained popularity in the last few years is that a number of leading internet companies have migrated to it, including Uber, Netflix, Apple, and even Amazon. While implementation of microservices makes more sense for these cloud-based companies since they have a large customer base and downtime in one of part of the application does not affect the whole. It is also easier for developers to continuously update the code or add bug fixes without any downtime.

But the complexity that microservices brings with it, may not be worth it if the application is not that big or it is an enterprise level application. With a monolithic core, developers do not have to deploy changes separately; they can do it all together — thus saving them a lot of time.

2. Testing and Debugging

It is much easier to debug monolithic applications, as compared to microservices. That is because, with microservices, there are hundreds of new variables introduced, and any of them could go wrong and create problems.

Not to mention, lesser dependency among variables in microservices means, it can be difficult to determine when an interface contract or compatibility is broken. To put it simply, you may not even know what has gone wrong until you are at runtime.

3. Performance

For an application that is accessed by millions or thousands of users every day, adding the complexity of microservices may be worth the extra effort. But, what you need to remember as a developer is that most enterprise business applications do not even come near that many number of users.

Now, if you create a new application that takes several seconds to load every new screen all because it needs to make 50 API calls to 50 microservices, then your end users are not going to care about your modern architecture. All they will see is an application which takes a lot of time to load. You could add request collapsing and clever caching, but that is just an extra set of complexity that you did not even need in the first place.

4. Security

Diving an application into hundreds of microservices does not just mean that you will have to decide how these microservices interact with each other but you will also have to decide a security protocol for each one of them. For instance, some microservices might have access to sensitive company data, while some may not. To manage a coarse-grained application like that, you will have to define security borders, even before you start to segment microservices.

5. Designing the Architecture

Designing a microservices architecture can takes weeks to months of initial planning just to get your project off the ground. It also means that you would have a higher upfront design cost and may even need to hire more developers just to break down microservices. There is a continuous risk of over-architecting and you may end up creating more microservices than you need, which will, in turn, increase the complexity of the architecture.

With a monolithic architecture, the development takes much lesser time, both in planning and designing. It also saves overall costs.

In modern architecture, monolithic architecture and applications have become a thing of the past, and every organization is moving forward to break them down. After all, moving away from monoliths is a logical decision since its more complicated and there are also many dependencies and issues with deployment and testing.

For most developers, microservices seem like the most obvious solution to replace monoliths. But in this article, we will be discussing how self-contained systems can also be a successful option for breaking down monoliths.

Microservices

One of the most obvious benefits of microservices is that it allows users to do continuous deployment and change, debug, or replace a part of the application without affecting the rest of the application. Using microservices architecture means, if anything goes wrong in a part of the application, it is contained in that very part, and the rest of the application continues to work without a glitch.

However great microservices may sound, transforming a monolith into microservices architecture is easier said than done. Depending on the size of the monolithic core, it can take a few months to years, just to convert a monolithic core into multiple microservices.

Pros

•     Maintaining microservices is comparatively much easier because each one of them has its own purpose and built with a laser-like focus on that very purpose. It also allows developers to jump in and out of microservices quickly. They are easy to run and quick to deploy.
•     Since all the microservices in an application are isolated from each other, if one part fails, it does not affect the other and does not lead to downtime. For instance, even if the microservice that handles adding new orders is down, your customers would still be able to check the status of their existing orders.

Cons

•     Every application may have hundreds of microservices, making them operationally complex. Developers cannot manage it all at once on the same server or deploy them one by one. Instead, they will require automation to handle everything.
•     Communicating messages from one microservice to another takes a lot of effort because developers need to make sure the data is transferred sensibly and consistently. More often than not, you will have to create a new microservice to handle transfer and authentication of data.

Even with those cons, microservices are still preferred over monoliths. The initial time taken to break down a monolith into microservices may be a lot but after that, what you get is an easy to manage architecture.

Though, if the initial costs and time for creating microservices is a lot for your organization and handling monolithic applications has become incredibly complicated, you might want to consider the self-contained system as an option.

Self-Contained System

Self-Contained Systems (SCS) are similar to microservices in the way that they allow you to breakdown a monolith into smaller and independent parts. But, there are many differences between SCS and microservices:

•     In SCS, you break a monolith down into replaceable and autonomous web applications, which isn’t the case with microservices
•     The SCS units are within software and larger than microservices
•     SCSs have their own autonomous user interface (UI), data storage, and business logic, making them more customizable than microservices
•     While API level integration of SCS is possible, UI level is a more preferred integration

Since SCS is bigger than microservices, when you break down a monolith, creating SCSs takes much lesser time than microservices. Instead of being a complete redesign, SCS makes an application more agile by dividing it into small steps to reduce chances of failure.

Pros

•     One of the biggest advantages of a self-contained system is that you can build several SCS units, each with different databases and languages.
•     With monolith broken down, you can easily handle the coding and deployment of the application. Since the data is internal, you do not need to worry about how messages get passed from one SCS unit to another.

Cons

The line between SCS and microservice does exist, but it is slightly blurry, which means, it can be difficult for you to define an SCS architecturally. There is a lot more planning that goes into it before you can begin to break a monolith down into a self-contained system

Choosing Between Microservices Vs Self-Contained Systems

If you want to break down a monolithic and your end goal is microservices, you could still start with a self-contained system and then move towards microservices. Though you have to be patient while breaking down SCS units to make your software more agile.

As mentioned above, planning is more important than ever otherwise you might end up with SCS units that get bulkier and bigger with time. If that does happen, you will have to start breaking them into microservices.