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Configuring and scaling data platforms when doing cloud native application development

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Configuring and scaling data platforms when doing cloud native application development

To succeed in evolving, software-driven markets, organizations must optimize the way they design, build, and use applications and data platforms. Along with increasingly popular cloud-native applications, data platforms are a big part of companies’ cloud infrastructure as a whole and, therefore, are an integral component of their cloud native application development cycle.

 

Faced with immense and ever-growing amounts of data being generated at quicker and quicker rates, software developers need to pay particular attention to the scalability of their data platforms and applications. This is one reason why K3s has become so popular, as it improves the flexibility and scalability of cloud native applications due to its lightweight container properties and minimal resource requirements, differentiating it from K8s. They must also design and configure platforms and cloud native applications that can handle an increasing number of concurrent users. It’s not easy and is a constant challenge, but developing for scalability is an indisputable necessity.

 

What is a data platform?

 

While we have looked at cloud native application development in previous blog posts, we haven’t considered the data platform which some consider to be the backbone of the modern, data-focused, cloud infrastructure.

 

A data platform is a comprehensive and necessary solution for consuming, processing, analyzing, and presenting data created by the many systems, applications, processes, and infrastructures of the contemporary digital enterprise. While there are a plethora of solutions and tailor-made applications for managing various aspects of the data lifecycle effectively, a true data platform ensures end-to-end data management.

 

A data platform goes much further than providing simple business intelligence statistics. While it does deliver relevant data to enhance an enterprise’s decision-making, a true data platform collects and organizes many more types and configurations of data across the company, including not only integral data used for security and privacy, but also technical IT operations data. Essentially, a complete data platform has the ability to manage ALL the data a company ingests or generates.

 

Data platforms are composed of data storage, servers, and data architecture. Aside from that, there are data consumption needs, data consolidation, and the ETL procedure.

Businesses routinely face challenges with data management as a whole, including the consolidation of diverse data types stored in various silos, cloud and on-premise servers. This is where effective data platforms show their worth.

The purpose of a data platform is to provide real-time insights through detailed analytics in a scalable, cost-efficient, and secure way. The most efficient data platforms can be held across distant geographies and in hybrid-cloud environments to strengthen business continuity plans. 

 

For a definition of cloud native applications, see this article

 

Why data platforms need to be built and configured for scale

 

Today, the majority of the fastest-growing and most successful businesses are data-driven in some way or another. From more online visitors to an increased desire for analytics, one way or another, data is always being generated that needs to be securely stored. While data frequently appears to be the answer to many business problems, its formidable nature, from its assortment of technologies, skill sets, tools, and platforms, can be complicated and hard to manage. 

Data’s complexity and businesses’ raised demand for it is an added challenge as it becomes more challenging to prioritize, grow the team, recruit leading talent, keep costs down, and satisfy clients and stakeholders. 

 

Whatever the reason for scaling a data platform, whether that be for an increased user or data volume, there are two many strategies: Vertical and Horizontal.

 

Vertical Scaling

Possibly the most straightforward way to scale is to do so vertically – deploy on a more advanced cloud server with more CPU power and memory. 

However, there are functional limitations to what can be accomplished through vertical scaling alone. Firstly, even the best machines and cloud servers may not be able to tolerate the immense data volumes and workloads required by modern cloud native applications. Secondly, the power and capacity required to effectively operate the necessary data platform will probably not be too cost-effective. 

Capacity management for single-server architectures can also be challenging, particularly for data platform solutions that will have inconsistent workloads. Having the capacity to manage peak loads could result in wasteful underutilization throughout off-hours. In contrast, having too little server capacity may cause performance to slow significantly during high usage. Moreover, expanding the capability of a single server architecture implies buying an entirely new machine or expanding cloud server storage.

In short, while it is crucial for cloud native applications and data platforms to utilize the full potential of the hardware or server on which it was deployed, vertical scalability on its own isn’t enough to achieve anything above the most stationary workloads.

 

Horizontal Scaling

For the reasons provided above, most institutions pursuing considerable scalability for their data platform will deploy on hybrid cloud architectures, scaling workloads and data volumes “horizontally” by spreading the load across multiple servers. Utilizing K8s, developers have found this to be most effective with the added benefit of more secure cloud storage.

Software developers will understand that no two workloads are identical. Some modern cloud native applications may be used by millions of users concurrently, with a lot of small transactions per second. Others may have only hundreds of users, but with petabytes worth of data. Both scenarios are very taxing workloads, but they demand different scalability strategies.

 

Conclusion

 

With constantly increasing amounts of data being generated, from more data-intensive apps and at quicker rates, developers are now required to pay particular attention to scalability when designing and building their data platforms and cloud native applications. Developers are primarily utilizing vertical and horizontal scaling methods to achieve this scalability by moving to a more advanced cloud architecture or spreading the load with a hybrid model.

 

Footnote:

 

Git Kubernetes, or git revert, is now one of the most popular commands among developers for its universal undo function. It doesn’t completely reverse the original command, however, as it is related to a specific commit. It doesn’t take away a commit from the versions history, alternatively, it creates a new commit with inverted content, reverting the project to the former state before the commit.

 

Soon, we will begin posting content on the ever-popular GitHub.

 

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How you can establish SaaS security thresholds when doing Cloud Native Application Development

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How you can establish SaaS security thresholds when doing Cloud Native Application Development

Cloud-native applications have been pinned as the future of software development due to their steady increase in proliferation over recent years. The Cloud-Native Computing Foundation calculated that there were about 6.5 million cloud-native developers active in 2020, a marked increase from 4.7 million in 2019.

 

New technologies used for developing cloud applications, including Kubernetes, containers, and serverless architectures, are changing the way companies build and deploy them. While the steady growth of cloud-native SaaS applications has accelerated the pace, efficiency, and success of business, this modern approach to development has introduced a myriad of new cloud security concerns. 

 

While cloud-native applications are inherently more beneficial than their on-premise counterparts, These new sets of security risks can’t be mitigated by applying traditional approaches to SaaS security. 

 

So, how can you establish effective SaaS security thresholds while doing cloud-native application development? 

 

What are cloud-native applications?

 

First, let’s remind ourselves of what ‘cloud-native’ refers to and what cloud-native applications are.

 

Cloud-native is a contemporary approach to creating, deploying, and running software applications that utilize the resilience, flexibility, and scalability of cloud computing. ‘Cloud-native’ comprises the different tools and techniques used by developers to create applications for the public cloud, rather than the conventional architectures suited to private data centers.

 

A cloud-native application, therefore, is one that is designed and built specifically for a cloud computing architecture. They are run and hosted in the cloud and are developed to leverage the intrinsic characteristics of a cloud computing software delivery model. 

 

Cloud-native applications utilize a microservice architecture that efficiently distributes resources to each service that the application uses, making it incredibly flexible and adaptable to a range of cloud architectures.

 

Satisfy both security and development objectives

 

The benefits of cloud-native application development are limitless, however, a lack of security continues to be one major problem. Modern development approaches and technologies, such as CI/CD, containers, and serverless, demand effective security that delivers immediate protection, earlier detection, and assurance that an organization’s cloud services fulfill security best practices, all while preserving speed and efficiency. 

 

 

Migrated security infrastructures aren’t cutting it 

 

Migrating applications to the cloud from traditional IT systems does not mean that organizations should accept a more vulnerable security stance in return for the conveniences and additional benefits that cloud computing provides.

 

There isn’t anything inherently less secure about public cloud infrastructures. In fact, cloud providers such as Google and Amazon adhere to the highest standards of security and compliance, taking their ‘shared responsibility’ very seriously, often exceeding what most private enterprises could maintain in their data centers. 

 

Security problems emerge from how businesses configure and use public clouds, especially SaaS (software as a service), IaaS (infrastructure as a service), and PaaS (platform as a service). Conventional application security measures often don’t work very well when using serverless or container architectures to create cloud-native applications.

 

Developers are adopting new codes of practice and techniques to establish effective security thresholds, as it’s clear that the key to this lies in the development phase of cloud-native applications.

 

How to establish SaaS security thresholds during application development – 3 steps

 

  1. Establish security infrastructure throughout development 

Before DevOps, dedicated security teams gave late-stage assessments and guidance before applications moved from the development phase into systems running in production. Security was frequently only considered toward the back end of development, creating substantial delays if issues emerged that required fundamental changes to the application. This attitude toward security is no longer acceptable in today’s more agile, cloud-focused development models, where efficiency, speed, and automation are key.

 

Developers are constantly under pressure to design, build, and launch applications quicker than ever and to frequently update them through automated procedures. To continually achieve these lofty goals, organizations now deploy applications developed on containers and functions straight into production, handling and overseeing them with orchestration tools like Kubernetes, and running them in the cloud. Consequently, productivity increases, but so does the security risk.

 

Hitting a balance between speed and effective security requires senior-level security officers to implement strategies to proactively address cloud-native security requirements with developers to make sure security infrastructures are thoroughly integrated into the software development lifecycle. Moreover, this allows businesses to catch security issues earlier in development without slowing down production.  

  1. Empower your developers the necessary tools 

Many companies still depend on traditional security instruments that can’t handle the speed, scale, and dynamic networking conditions of containers. The addition of modern, serverless functions heightens the problem by further abstracting infrastructure to supply a straightforward execution environment for microservices and applications. 

 

Cyber attackers search for misconfigured cloud infrastructure permissions and vulnerabilities in the serverless function code to reach services or networks that hold private information.

 

Enterprises can use CI/CD tools like Bamboo, Jenkins, and Azure DevOps to continuously develop, test, and ship applications. When utilizing containers to deploy cloud-native applications, developers can exploit base images and elements from internal and external repositories to accelerate their work.

 

Despite that, even container images from trusted and authorized repositories could possess vulnerabilities that can expose applications to attacks. The solution, and best first line of defense, is to provide developers and security teams with the necessary tools and techniques to block non-compliant images within the CI/CD pipeline.

 

Scanning images for vulnerabilities and malware in the development phase allows application developers and security teams to enforce the enterprises’ image assurance policies, block non-compliant images, and warn the developers of possible threats.

  1. Shared Responsibility

Another thing to consider is that the security of the application is somewhat reliant on the cloud provider. Moreover, due to the ‘shared responsibility model’, developers and security teams bear an extra burden when securing their application.

 

Organizations need to accept the new reality that specific aspects of security will need to be managed by their cloud provider, and others will remain with them. For example, Google takes the Shared Responsibility Model seriously and has invested heavily into it. This model allocates security of the cloud to the provider, who then tasks the customer (organization) with security in the cloud.

 

Specifics can change from provider to provider and service to service, but typically, the customer accepts responsibility and control of the guest operating system, including security updates and patches, as well as any other related software and the configuration of the cloud server. Ultimately, it’s a joint effort to achieve secure cloud-native applications and secure cloud storage.

 

Understanding and accepting this shared responsibility is essential to any cloud-native application developer establishing security thresholds during development. Not only important as a model for combined cloud maintenance and preservation, but also during the development cycle as developers can easily implement security thresholds and infrastructures using Kubernetes (GKE) specifically designed for cloud-native environments. Businesses should also understand that the security measures put in place by the cloud provider do not absolve them from their own accountabilities.

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Should I buy cloud services directly from the provider?

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Should I buy cloud services directly from the provider?

From reducing IT costs to accelerating innovation, there are many compelling reasons to embark on a cloud migration journey. 

The idea of migrating your data to the cloud may sound like a copy and paste task, but in reality, there are challenges, pitfalls, and many things to consider. This article defines and provides an overview of the cloud data migration process and suggests the best practices to turn it into a value-increasing opportunity for your business.


Сloud business process services

There are many cloud solutions. Most of them are designed to meet specific business needs, so cloud migration is always a flexible and business-tailored process.

With every year that passes, it becomes ever more apparent that migrating to the cloud is the only way for companies to truly compete and remain relevant in the long-term.

A growing number of businesses, from freshly-launched start-ups to Fortune 500 giants, are adopting cloud computing, meaning CIOs and business owners alike are met with an overwhelming number of providers, features, products, services, hybrid solutions and training options to consider.

Every organization has its own technological fingerprint; its own distinct set of requirements, goals, and operational nuances that need to be taken into consideration.

Let’s take a closer look at the top three names in the industry: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.


AWS: Pros and cons

AWS jumped into the game early as the very first major cloud vendor in the space around 12 years ago, claiming an impressive 33% of market share and generating $1.4bn for Amazon in Q1 2018 alone.

The biggest strength AWS possesses is undoubtedly its maturity and dominance in the public cloud market, with its success and popularity linked to the sheer scale of its operation.

Today, it stands tall as the most established and enterprise-ready vendor, offering perhaps the richest of capabilities when it comes to overseeing a massive number of resources and users. 

Microsoft Azure is gaining ground as the preferred service for existing Microsoft customers, with Google’s offering entering the cloud battleground relatively recently as a ‘leader’. While other formidable competitors such as Alibaba Cloud and Oracle Cloud have increased in popularity over the last few years, AWS remains a strong front runner in the cloud computing industry, with competitors Azure and Google Cloud carving out their own modest share of the market.


Microsoft Azure: Pros and cons

Microsoft showed up on the cloud scene a little later than AWS, but certainly made up for it by adapting its existing on-premises offerings for the cloud.

Seven years since its initial launch, Azure is a strong competitor to AWS, providing businesses with a great range of features, robust open-source support, and straightforward integration with other Microsoft tools.

As a Microsoft product, Azure no doubt benefits from user familiarity with the brand, which creates an immediate preference for Azure among loyal Microsoft customers.

While Azure is indeed classed as an enterprise-ready platform, in its aforementioned Magic Quadrant report, Gartner noted that many users feel that “the service experience feels less enterprise-ready than they expected, given Microsoft’s long history as an enterprise vendor”.

Users also cited issues with technical support, training, and DevOps support as some primary pain-points when using the provider.

 

Google Cloud: Pros and cons

As a latecomer to the cloud market, Google Cloud Platform (GCP) naturally offers a more limited range of services and doesn’t command the same global spread of data centers offered by AWS and Azure. 

It does, however, give customers a highly-specialized service in three main streams: big data, machine learning, and analytics, with good scale and stable load balancing, as well as those famously low response times. Google’s container offering provides users with a significant advantage as it developed the very Kubernetes standard now utilized by competitors AWS and Azure. 

Customers tend to choose GCP as a secondary vendor in a hybrid solution, though it is becoming increasingly popular with organizations that are direct competitors with Amazon, and ,therefore, cannot use AWS. It’s important to note that GCP is very open-source and DevOps-centric, and as a result, does not integrate as well with Microsoft Azure.

 

Why and when do you need to migrate to the cloud?

Moving to the cloud is a choice most modern companies are having to make. Below are some examples of when a company may decide to move to the cloud.

-Move from a legacy system. 40% of companies that migrate to the cloud from a legacy system do it to improve the security of their data. Cloud data migration also allows a company to deal with legacy system tech limitations.

-Get a competitive advantage. Migration to the cloud is also an opportunity to create a competitive advantage because of the possibility of cutting costs and making employee workflow more flexible. Time and money can also be redirected to other tasks aimed at business growth. Moreover, cloud migration creates new opportunities for businesses to leverage more efficiency when employees are working from home. In such an environment, using the cloud for data management is the best choice.

 

Conclusion

When looking for the right cloud vendor for your enterprise, be sure to consider your particular requirements and workload, and remember that the answer could indeed lie in a combination of two or three cloud providers.  Migrations as a whole, whether from a legacy system to the cloud or from a cloud to another cloud, can be hugely beneficial. 

While providing many notable benefits to do with efficiency and business infrastructure, one of the most notable advantages comes in the form of improved security, more compliant security, and cheaper security. Outdated legacy systems in the form of private servers are costly and require a lot of attention to maintain the level of efficiency and security of advanced cloud solutions. Popular cloud providers, on the other hand, have built their cloud from the ground up with state-of-the-art security and many other notable benefits.

 

So one might wonder how to choose? Book a free consultation with us and we will help you figure out all the intricacies.

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Healthcare Data Migration and Cloud Solutions in the Medical Industry

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Healthcare Data Migration and Cloud Solutions in the Medical Industry

It’s been a long time coming, but the medical industry is finally embracing the cloud and all the benefits that come with it. For hospitals, migrating to the cloud wasn’t as straightforward as first thought. Considering the highly confidential data they handle, the effectiveness of cloud security has come into question. Moreover, the complex nature of cloud migration tools such as GKE (Google Kubernetes Engine) may have caused further confusion, putting many off adopting this new technology due to misunderstanding and worry. 

In terms of technological progression, healthcare is more often than not at the forefront. Consider nanotechnology or 4D ultrasounds, for example. On the other hand, development and investment in IT infrastructure usually falls behind other sectors. Cloud computing solutions are a prime example. The healthcare industry has famously been one of the last industries to take the leap. Until a couple of years ago, that is.

Medical organizations looking for a flexible yet secure solution for storing and accessing large collections of data are steadily shifting to cloud data migration.  

While increasingly lower setup and support costs are now big attractions of cloud storage, medical institutions can also benefit from the versatility that cloud data migration can offer.  

Community health management, value-based care, and an ever-growing mobile user base require a storage infrastructure that can scale easily without requiring cumbersome investments into time and capital.

To take advantage of the cloud, however, companies need to correctly plan for this data migration process. Creating an effective and efficient data migration roadmap involves deciding which datasets and applications need to be moved to the cloud and what tools are available to facilitate the migration process.

 

The challenges of cloud migration for the medical industry

The cloud is far different than what IT leaders and executives are used to deploying in their legacy infrastructure environments. Medical organizations should begin with a solid understanding of the processes involved and skills required at all stages of the migration journey, including management and maintenance requirements.

Security and privacy

These have always been the main objections put forward by healthcare organizations when things like EHRs (Electronic Health Records) are at stake. However, the success and safety of online banking, for example, has quashed privacy concerns about the online storage of confidential medical records. Today’s leading cloud providers, including Google and Amazon, employ highly advanced protection and security far beyond that used by typical hospitals to keep their client’s data secure.

The confidentiality and privacy of medical information are, obviously, of paramount concern here. To learn more about medical privacy and the cloud, read our recent blog post discussing the benefits of cloud computing and effective cloud security solutions here: https://ostridelabs.com/medical-privacy-and-cloud-computing-security-solutions/

Data location and ownership

While there are strict rules relating to the use of the cloud for healthcare data, regulations preventing its use have slowly been relaxed. Today, there is an open market where healthcare providers can choose where and how they manage their data online.

Funding models

Funding models continue to be an obstacle, with hospitals and organizations working on ways to more easily procure cloud technologies. In a lot of cases, an IT policy may mandate a strategy that looks to incorporate cloud technologies, but the procurement department will not approve such purchases. Until these barriers are defeated and the benefits of cloud migration can be easily communicated on a larger scale to entire organizations, hospitals’ pathways to the cloud will stay blocked.

 

Cloud Migration Solutions

Simply, data migration includes:

 

  • Transferring data from legacy systems to the cloud.
  • Restructuring data for PII/PHI separation and encryption. 
  • Making sure the systems are made to be cloud-native.
  • Ensuring security via efficient network isolation controls with the least privilege. 

 

The migration process can be viewed as numerous iterative cycles where each application and its data is moved from its origin to its new cloud destination, one by one. To ensure a smooth transition, a tactic employed by many cloud solution providers is to use machine learning to find errors or misplaced data points when collecting data from multiple applications. 

 

Similar automation can also assure compliance with corporate policies and security standards established by the CTO, CIO, or CISO for the company. The DevOps team can also include phase gates to make sure policies are obeyed throughout the data life cycle.

 

For compliance purposes, PHI/PII data has to be physically isolated from the rest of the operational data. With the appropriate application of data encryption and least privilege combined with physical separation of data per tenant in the case of multi-tenant systems, the chance of data being held at ransom can be reduced. This also significantly reduces the absolute damage in case of a breach.

 

Furthermore, an open API standard system enables a security team to view how interactions are taking place between data repositories and how APIs are interacting with various databases. To allow applications to interact while remaining separate and secure, the mechanism working the API system needs network isolation control and the enforcement of least privilege. This provides the ability to observe the interference between databases and analyze possible threats.

 

Lastly, encrypting all communications is important. No organization can expect consistent vigilance from those using internal or external communication channels. At some point, patients, employees, or providers may share sensitive information. Encryption ensures that this information can not be retrieved by a wrongdoer.


The keys to a successful cloud migration

Cloud migration is complicated but necessary for healthcare organizations. Proper planning and consideration of workloads, applications, and the future of the industry, allow organizations to embrace the cloud for eventual data expansion and flexibility. These five key considerations can also be thought of as an effective roadmap to successful cloud migration.

  1. Choose The Right Cloud Service Partner 

While some medical organizations already have the internal technical expertise to successfully perform a cloud migration, the majority will have to procure the assistance of external partners. When selecting the right partner, it’s important to examine their past experience on similar projects, previous clients, and their readiness to address inquiries or concerns specific to your cloud migration. 

  1. Create a Long-Term Migration Strategy

Data migration should not be employed as a quick fix. While it may solve immediate problems, healthcare organizations should be making projections for at least five years in the future when making important decisions. For example, in the case of cloud migration, it is crucial to plan for future capacity requirements and forecast tech trends. Without considering long-term needs, healthcare providers will most likely have to engage in another expensive data migration in the next couple of years.

  1. Define the Data for Migration

Not every cloud migration demands a total relocation of all applications and data available. In a few instances, some legacy systems and data might be left in their place or transferred to a different location from the other data assets due for cloud migration.

Because of this, taking a comprehensive inventory of all current data assets and determining whether or not to move them is necessary. When data has to be transferred, the selected destination must be identified and defined. Above everything, this will limit delays and confusion when the migration reaches a critical stage and changes become more expensive and challenging to implement.

  1. Keep Data Integrity

This ensures that data stays consistent, accurate, and reliable while migrating between systems. Sufficient error checking and validation methods must be in place to make sure that data is not changed or duplicated during the transfer.

The majority of the work needed to maintain data integrity should be done at the pre-planning stage. It shouldn’t be assumed that there will be a direct relationship between fields and data types. For example, mistakes could occur that would leave patient records inaccessible or incomplete. Implementing a manual check to monitor the success of an electronic migration process is essential.

  1. Consider using a Hybrid Cloud Solution

Utilize using a cloud-based storage solution to augment your on-premises storage, instead of relying on one or the other. The majority of cloud service providers now offer better overall security and access restrictions than the best equipped internal IT teams can provide. Cloud infrastructure allows healthcare organizations to swiftly acquire more storage and computing resources as needed.

While regulatory compliance rules require healthcare providers to have in-house servers for the storage of sensitive data, most patient health records can actually be stored and managed in the cloud. Using a hybrid system that incorporates both internal servers and a cloud infrastructure may prove to be the best solution for large healthcare providers.

 

Conclusion

Migrating data and applications to the Cloud is not just a new, interesting initiative. There is actually a pressing urgency for healthcare organizations to move their data to the cloud and to make their systems cloud-native. In today’s technological environment, however, these providers must take a broader view to ensure lasting security and efficiency.

 

Healthcare organizations will gain plenty of advantages from the cloud once their data is successfully migrated. It will make their data more readily available while lowering operational costs and maintaining privacy. However, it’s important to carry out comprehensive planning before undertaking a cloud migration.

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K3S vs K8S – when to use each if you’re concerned about cloud security solutions?

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K3S vs K8S – when to use each if you’re concerned about cloud security solutions?

Kubernetes is unquestionably the most popular container orchestration tool, used by companies of all sizes around the world to migrate and host applications in the cloud. But, there has been a new addition of K3s, also known as lightweight Kubernetes, which is a smaller, simpler, more efficient, and faster version that accomplishes the same goals and has a smaller footprint.

Businesses nowadays scratch their heads when trying to figure out when to use K3s or K8s in their production. Deciding between them is a critical consideration for enterprises that are undertaking a digital transformation or want to migrate cloud-ready systems and applications to the cloud, as security solutions differ between the two. Each presents unique benefits and drawbacks that may lend themselves to certain companies and applications. So, let’s discuss what makes K3s and K8s different and when each should be used when cloud security is concerned. 

What is Kubernetes (K8s)?

We have a whole separate blog post dedicated to defining K8s, discussing its advantages, and detailing its use cases, so we won’t spend too much time on it here. That being said, it wouldn’t hurt to quickly refresh our memories:

Kubernetes, or K8s, is the most popular microservices container orchestration platform, eliminating many of the manual processes involved in deploying, managing, and scaling containerized applications.

Read more here: Kubernetes in Cloud Migration Solutions


What is K3s and how does it differ from K8s?

K3s is a lighter version of the Kubernetes distribution tool, developed by Rancher Labs, and is a completely CNCF (Cloud Native Computing Foundation) accredited Kubernetes distribution. This means that YAML can be written to work on normal Kubernetes and will operate as intended against a K3s cluster. 

 

In K3s, the memory trace or binary containing components to run a cluster is much smaller than that of K8s. Actually, K3s is specifically designed to be a single binary under 40MB that fully implements the Kubernetes API. To accomplish this, the developers removed many additional drivers that did not need to be there and were easily replaced with add-ons.

 

Thanks to its minimal resource requirements, it’s possible to run a cluster on a machine with at least 512MB of RAM, allowing pods and nodes to run on the master. Additionally, because it’s a small binary, it can be installed in a fraction of the time required to launch a K8s cluster! Generally speaking, it takes under two minutes to launch a k3s cluster with a couple of nodes, meaning apps can be deployed for learning and testing purposes in no time.

 

Both its adoption and reputation are growing swiftly too, with monthly users growing by the thousands, all while being crowned the best new developer tool by Stackshare in 2019.

 

Advantages of K3s

While K8s has many notable benefits, including its flexibility, portability, scalability, and ability to seamlessly accommodate much larger configurations, it is not the best choice in many situations. K3s edges ahead in many areas.

 

Small Size

The primary advantage of K3s is that it is small in size, under 100 MB, in fact, which allows it to launch a K8s cluster on smaller hardware with the least settings.

Fast Deployment

K3s can be installed and deployed with one command in less than 30 seconds.

Lightweight

Due to their small memory footprint, the Kubernetes can be up and running imminently, meaning that the binary, which comprises all the non-containerized components needed to run a cluster, can be much smaller.

Continuous Integration

Due to their small size and lightweight environment, continuous integration is more straightforward. It helps in automating the integration of codes from numerous contributors into a singular project.

Perfect for IoT and Edge Computing

Thanks to effective support for ARM64 and ARMv7, K3s is highly efficient for Kubernetes distribution in production workloads for resource-restrained IoT devices.

Simplified and Secure

A single binary file under 100 MB is able to package K3s, making it simple and easy to secure with far fewer complications.

 

K3s, K8s, and Cloud Security

For certain industries, such as healthcare or banking, where data privacy is imperative,  the security of their cloud environment or cloud-based applications and data will be the primary concern. While the choice between K3s and K8s may be forced due to the size of applications, data, and infrastructure required, it might be important to consider selecting the suboptimal option if it provides better security.

 

Before we continue, it’s important to point out that K3s benefit from tighter security deployment than regular Kubernetes thanks to their small attack surface area.

 

While it might be cumbersome to utilize K3s to secure clusters for cloud migration and hosting of large applications and vast amounts of data, the extra time and effort required may be beneficial if security is heightened.

 

For example, for an industry such as healthcare that has been slow to undertake digital transformations because of complex decisions surrounding private health records, the more secure K3s may be preferred.


Should You Choose K3s or K8s?

It is evident that both K3s and K8s have their advantages and disadvantages which make them uniquely different from each other, while on the face of it, it may seem that they are two similar versions of the same thing. Both are very useful, but given various business and usability situations, certain features can have a marked impact.

 

We have seen how K8s can benefit large systems and applications, and with that in mind, large enterprises with an abundance of critical data that distribute their workload via several cloud servers may choose to use K8s, which will benefit them in many ways.

 

Small-to-medium-sized businesses may decide to use both K3s and K8s because the actual application size will not remain constant throughout its lifecycle. It will be beneficial for them to use K8s to cope with the heavy workload, but to quickly and efficiently test a single cluster in smaller productions, K3s provide many lean advantages. Keeping a steady balance between K8s and K3s helps businesses save crucial time and money while maintaining an efficient and agile workflow.

 

Small businesses that do not deal with large applications often automatically select K3s because they are much quicker at deploying applications with smaller workloads, and installation, operation, and updates are easy.

 

Developers that spend a lot of time with IoT (Internet of Things), and edge computing have a sizable advantage when choosing K3s as their chosen Kubernetes distributor. Especially considering they will be working with resources that contain low-end computational hardware such as Raspberry Pi. K3s use one small binary file that runs on IoT devices thanks to ARMv7 and ARM64 support.


Conclusion

K8s solved the distributed computing dilemma but has since become rather complex. Rancher took all Kubernetes’ principal workflows and modified the tool into a lighter version of Kubernetes, named K3s.

 

Now you know about the important differences between K3s and its predecessor, K8s, and which situations are best to use each one, such as when using a Raspberry Pi or ARM device, or if you just want to easily set up a simple and quick development environment. This is also an important benefit for businesses that will be migrating applications to a cloud cluster such as GKE or Azure, as the transition becomes easier.

 

Moreover, when considering continuous everything as it relates to a specific system or application, companies may find utilizing a set of principles such as GitOps to be easier and more efficient with K3s. GitOps merges Git with Kubernetes’ convergence features and works as an operating framework for creating and delivering Kubernetes-based environments and applications.

 

You might think k3s is the superior option to regular Kubernetes (K8s), but limitations do exist. Currently, it doesn’t support more than a master or any database other than SQLite on the master node. So, defining requirements and objectives is vastly important when choosing a default container orchestration tool.

 

If your primary concern is ensuring your cloud-based environment and applications are secure, K3s is your best option.

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