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Category: DevOps Onramp

  • The Benefits of a Distributed Cloud

    The Benefits of a Distributed Cloud

    The way applications are created has changed dramatically in just a few short years. It’s now all about rapid assembly and integration; applications built using microservices and containerized. Containers and the cloud have certainly been the catalyst for this progress and will continue to be the infrastructure on which developers create new and innovative applications. 

    As workloads are moving to the cloud, however, more and more businesses are realizing they need a cloud that can be rapidly adapted to their specific needs. Computing environments may be a blend of server types: On-premises, bare metal, colocated and distributed across multiple continents. No single cloud architecture can offer consistently exceptional application performance for all users.  

    Between hybrid, on-premises, multi-cloud, colocated, centralized and distributed clouds, it can be hard to keep all the terminology straight. What are the differences between these cloud architectures? Why use one rather than the other? In this article, we’ll focus on just one of these: The distributed cloud. 

    What is a Distributed Cloud?

    As opposed to the large public cloud where all computing is done in a central location, a distributed cloud consists of a system of servers that are not all in the same place—even though they are managed through a single cloud provider. The servers in a distributed cloud can be actually anywhere in the world. This allows for computing to be done closer to the end user, which improves the user experience in a multitude of ways.

    What Are the Advantages of Using a Distributed Cloud?

    Below are six of the key benefits of using a distributed, rather than a centralized, cloud.

    One Size Does Not Fit All

    Computing environments may be a blend of server types: On-premises, bare-metal, colocated and distributed across multiple continents. Cloud providers need to offer flexibility to customize a cloud for their end users and their business needs.

    Data Regulation Compliance

    Many countries have specific compliance requirements for their citizens’ data and do not allow for personal/financial data to be hosted outside of their borders. Thus, having a distributed server within those countries alleviates that issue. 

    Reduced Latency

    In a distributed cloud model, servers can be placed in a wide array of locations, including in an edge cloud. When servers are placed closer to end users as opposed to in a centralized location, data does not need to travel as far to get back to the server. 

    Improved CDNs

    A distributed cloud can be used to deploy content delivery networks, or CDNs, to speed up content and service delivery. Heavy content (e.g. videos) can be stored and delivered by the distributed cloud closer to end users, which enhances the user experience.

    Network Resilience

    Network connectivity is always a worry for cloud providers. If their system goes down, none of the user’s workloads will run. In a distributed cloud system, however, the network is at a reduced risk of failure because if the system crashes, cloud services can reside in a local subnet.

    Location Flexibility

    A distributed cloud can always become more distributed. More locations can be added to the network, broadening the global compute zones available within that cloud. Companies that want to reach users in specific locations around the world can use a distributed cloud to reach them.

    Changing How People Think About Cloud Computing

    To address the demand for versatility, a new type of cloud has emerged: The unified distributed cloud (UDC), in which workloads can run in thousands of locations anywhere on earth. Such a cloud connects to existing infrastructure around the world via one API. Businesses can enjoy the benefit of running workloads in multiple locations without needing to maintain any of the distributed infrastructure. From a single UI, users pick an existing data center to send their workloads to and, within minutes, begin deploying. 

    A UDC transforms heterogeneous infrastructure into a homogeneous cloud computing platform, which can then be leveraged to support the delivery of cloud services in proximity to end users. It is designed to be interoperable with any private, hybrid cloud or multi-cloud architecture. As a result, enterprises can create an optimized cloud computing strategy, taking advantage of existing and available infrastructure in the location—or locations—of their choosing.  And because it sits on existing infrastructure, it doesn’t require any additional capital expenditure.

    As opposed to the traditional cloud model, the UDC can change the way people think about the public cloud. While the large public cloud providers benefit from economies of scale, a distributed cloud takes advantage of the economies of locality and distribution and thus has the flexibility to handle emerging location-dependent apps, such as the new wave of internet-of-things applications.

  • MLOps Vs. DevOps: What’s the Difference?

    MLOps Vs. DevOps: What’s the Difference?

    Machine learning operations, or MLOps for short, is a key aspect of machine learning (ML) engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others.

    MLOps can help organizations create and improve the quality of their AI and machine learning solutions. Adopting MLOps allows machine learning engineers and data scientists to collaborate to improve model performance by implementing continuous integration and continuous deployment (CI/CD) practices. It accelerates the ML model development process by incorporating the appropriate monitoring, governance, and validation of ML models.

    What Is DevOps?

    DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks usually associated with separate application development and IT operations teams. DevOps, in its broadest sense, is a philosophy that encourages improved communication and cooperation between these (and other) teams within an organization. 

    In its narrowest sense, DevOps refers to adopting practices that enable the deployment and maintenance of iterative application development, automation, and programmable infrastructure. It also includes changes in the workplace culture, like trust-building and bonding between developers, system administrators and other team members. DevOps aligns technology with business objectives and can transform the software delivery chain, job functions, services, tools and best practices.

    MLOps Vs. DevOps: Key Differences

    Here are some of the main differences between MLOps and traditional DevOps.

    Development

    The concept of development refers to different things in each model, with a slightly different CI/CD pipeline.

    DevOps:

    • Usually, the code creates an interface or application. 
    • The code is wrapped into an executable or artifact before being deployed and tested with a set of checks. 
    • Ideally, this automated cycle will continue until the final product is ready.

    MLOps:

    • The code enables the team to build or train machine learning models. 
    • The output artifacts include serialized files that can receive data inputs to generate inferences. 
    • Validation involves checking the trained model’s performance based on the test data.
    • This cycle should also continue until the model reaches a specified performance threshold.

    Version Control

    DevOps:

    • Version control typically only tracks changes to code and artifacts.
    • There are few metrics to track.

    MLOps: 

    • MLOps pipelines usually have more factors to track. Building and training an ML model involves an iterative experimentation cycle, requiring tracking of various metrics and components for each experiment (essential for later audits). 
    • Additional components to track include training datasets, model building code and model artifacts. 
    • Metrics include hyperparameters and model performance indicators, such as error rates.

    Reusability

    DevOps:

    • DevOps pipelines focus on repeatable processes.
    • Teams can mix and match processes without following a specific workflow.

    MLOps: 

    • MLOps pipelines repeatedly apply the same workflows. The common framework across projects helps improve consistency and allows teams to progress faster because they start with familiar processes. 
    • Project templates offer structure, enabling customization to address the unique requirements of each use case.
    • Uses centralized data management to consolidate the organization’s data to accelerate the discovery and training processes. Common approaches to centralization include a single source of truth and data warehouses.  

    Continuous Monitoring

    Monitoring is essential for both DevOps and MLOps, but for slightly different reasons.

    DevOps:

    • Site reliability engineering (SRE) has been trending over the past few years, emphasizing the need for monitoring software from development through to production deployment. 
    • The software does not degrade in the way an ML model does. 

    MLOps:

    • Machine learning models can degrade quickly, requiring constant monitoring and updating.
    • Conditions in the production environment affect the model’s accuracy. After deployment to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance. 
    • MLOps ensures that algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.

    Infrastructure

    DevOps and MLOps both rely heavily on cloud technology, but have different operational requirements.

    DevOps relies on infrastructure such as:

    • Infrastructure-as-code (IaC)
    • Build servers
    • CI/CD automation tools

    MLOps relies on infrastructure such as:

    • Deep learning and machine learning frameworks
    • Cloud storage for large datasets
    • GPUs for deep learning and computationally-intensive ML models

    DevOps and MLOps Trends

    Here are some of the major trends driving the development of DevOps and MLOps.

    GitOps

    A new evolution of the DevOps workflow, GitOps is a new paradigm for controlling and automating infrastructure. A Kubernetes-oriented paradigm enables developers and operations teams to manage Kubernetes clusters and deliver containerized applications using Git. Implementing Git workflows for operations and development teams allows developers to leverage Git pull requests to manage software deployments and infrastructure.

    GitOps incorporates existing development tools to manage cloud-native and cluster-based applications with CI/CD. It automatically deploys, monitors, and maintains cloud-native applications using a Git repository as the single source of truth.

    GitOps is a way to implement and maintain clusters in Kubernetes. Continuous delivery and deployment allow developers to build, test, and deploy software faster through incremental releases. Kubernetes continuous integration and runtime pipelines must be able to read and write files, update container repositories, and load containers from Git. GitOps helps businesses manage their infrastructure with version control, real-time monitoring, and alerting of configuration changes.

    Synthetic Data

    Synthetic data is any information generated artificially instead of collected from real events. Algorithms generate synthetic data for use as a replacement for operational and production test datasets. Synthetic datasets are also useful for validating mathematical models and training machine learning models.

    Benefits of synthetic data include:

    • Minimizing the constraints associated with using sensitive and regulated data.
    • Customizing data to specific requirements and conditions not available in real-world data.
    • Generating data for testing software quality and performance for DevOps teams.

    Codeless Machine Learning and AI

    Machine learning often involves computer code to set up and process model training, but this is not always the case. Codeless machine learning is a programming approach that eliminates the need for ML applications to go through time-consuming processes.

    Codeless ML eliminates the need for experts to develop system software. It is also simpler and cheaper to deploy and implement. Using drag-and-drop input during machine learning processes can simplify training efforts in the following ways:

    • Evaluating results.
    • Dragging and dropping training data.
    • Creating predictive reports.
    • Using plain-text queries.

    Codeless ML gives developers easy access to machine learning applications, but it is no substitute for an advanced, nuanced project. This approach is suitable for small businesses that lack the capital to maintain an in-house data science team.

    TinyML 

    TinyML is a new approach to machine learning and AI model development. It involves running models on devices with hardware restraints, such as microcontrollers powering smart vehicles, refrigerators and electric meters. This strategy is the best fit for these use cases because it speeds up the algorithm—there is no need for the data to go back and forth to a server. It is especially important on large servers and can speed up the entire ML development process.

    Running TinyML programs on an IoT edge device has many benefits:

    • Lower energy consumption.
    • Reduced latency.
    • User privacy guarantees.
    • Reduced bandwidth requirements.

    Using TinyML offers greater privacy because the computation process is entirely local. It consumes less power and bandwidth, resulting in lower latency because it doesn’t require sending data to a central location for processing. Industries that are taking advantage of this innovation include agriculture and healthcare. They typically use IoT devices embedded with a TinyML algorithm to monitor and predict real-world events using collected data.

    Conclusion

    In this article I covered the key differences between MLOps and DevOps:

    • Development—DevOps pipelines focus on developing a new version of a software product, while MLOps focuses on delivering a working machine learning model.
    • Version control—DevOps is mainly concerned with tracking binaries and software artifacts, while MLOps tracks additional factors like hyperparameters and model performance.
    • Reusability—DevOps and MLOps both strive to create reusable processes and pipelines, but use different strategies to achieve repeatability. 
    • Continuous monitoring—Monitoring is important to DevOps, but even more important in MLOps because models degrade in performance due to model and data drift.

    Finally, I covered a few key trends that will transform DevOps and MLOps in the near future. I hope this will be useful as you discover your place in the new and exciting development ecosystem.

  • Putting the Security Into DevSecOps

    Putting the Security Into DevSecOps

    The non-Newtonian fluid that’s composed of cornstarch and water has been around a long time, but Dr. Seuss’ 1949 book was the impetus for what it’s often called today – Oobleck, from “Bartholomew and the Oobleck.” When not under pressure, Oobleck is a thin liquid; when pressure is applied, its resistance increases to the point of being solid. A couple of other examples are quicksand and Silly Putty ™ (for fun, this video shows how resistant Oobleck is against Thor’s Hammer). Company application security is much like Oobleck. When not put under stress, security appears to be a fluid mess of policies, procedures, guidelines and a host of other controls that seem to have no real connection. But when there’s an event of interest, incident or even a breach, those policies and procedures (P&Ps) come together to make their worth known.

    DevOps—What’s in a Name?

    DevOps has been around for a while now, and its definition is akin to a Newtonian fluid: Not well-defined except within each company or organization. This lack of a standard definition has its merits, though; giving each company flexibility in determining what it means for them, and the benefit of maintaining the best of development, security and operations no matter what it is called. Attempts at bringing all aspects of these disciplines together, regardless of the nomenclature used, are both old and recent. It’s a reframe of Shakespeare’s line, “What’s in a name?”

    Security has to be part of the DevOps culture, whether it’s called DevSecOps, SecDevOps, SecOpsDev or just simply DevOps. Creators of software need to go beyond the desire to have a good process and move to understand that customers need the software to be secure in addition to being useful, delivered on time and with a good UX and that the business needs to develop and maintain a positive reputation and avoid breaches to the best of its ability.

    How does one put security into the DevOps process? One approach is to proceed as if the goal is to attain SOC 2. SOC 2 reports hold great value. While not universally recognized (SOC 2 is a US-focused security controls examination), they give useful details about the controls used. While SOC 2 is not on the level of ISO 27001, for international (to the USA, that is) prospects, it still divulges security control details that aren’t present in other standards.

    What if Attestations Are too Expensive?

    AppSec transparency is not only good for improving sales but for mitigating the problems caused by third-party breaches.

    For some vendors, especially newer or smaller ones, something like SOC 2 is on their radar but not yet feasible. Why? Cost–those and other attestations are often expensive. Is it worth it? Yes, but not if the financial expense will put the company at risk. Whether one has achieved SOC 2 or not, potential customers want to see transparency in a company’s security controls.

    In the process of going for SOC 2, there are two advantages: 

    • You’ll improve your security posture.
    • You’ll be ready for when those opportunities roll around.

    Let’s get started.

    Pieces of the AppSec Puzzle

    Actually, let’s back up a bit first. There are some policies that cover multiple areas of the company or product: Infosec policy, risk assessment, vendor management and acceptable use (AUP). These will require full company interaction and arrangement. I want to focus on what you can do, starting today, to improve your product security.

    Here are the policies to work on to put security into DevSecOps.

    • Software development life cycle (SDLC)
    • Incident response
    • Access control
    • Passwords
    • Change management
    • Logging and monitoring
    • System backup and recovery

    Each of these will require, for full effect, more people and departments than just you and your own. But these P&Ps can be directed internally toward your development and production teams; perhaps even IT and security.

    Here’s a list of each policy and its purpose:

    Software Development Life Cycle (SDLC)

    This is one of the first P&Ps to develop. You’re already doing this – just because it’s unwritten doesn’t mean you don’t do it. In the world of customer assurance, if it isn’t written, it doesn’t exist. Make it real and purposeful by codifying what you already do and improving as you go. This is a primary tool for AppSec (which includes web applications and APIs) and is almost solely within the purview of devs, engineers and QA. The product or service provided needs to have this foundational level of control to prevent common forms of attack.

    Incident Response

    This is also high on the list of P&Ps on the list. This is separate from the corporate IR plan; DevOps should have its own plan for dealing with technical emergencies, incidents and breaches. And this plan can be part of the overall company plan.

    Access Control

    Access control includes how users are authenticated and authorized, and includes least-privilege access, multifactor authentication (MFA) and service accounts. Prospects need to know that access to your production environment is well controlled.

    Company Passwords

    Company passwords as a whole are managed by IT and security, and often have a minimum of eight characters, require a certain number of special characters and other industry-standard requirements. But your software environment can go beyond that to enforce more stringent controls.

    Change Management

    Customers need to be aware that the software they bought is changed in an orderly and authorized manner, not through an ad hoc and ad lib process.

    Logging and Monitoring

    This is important for ensuring that activities such as access control and change management in your company have been performed properly, in addition to providing information for forensics in case there’s a breach.

    System Backup and Recovery

    Document and ensure your capabilities to restore data in a timely manner. Things happen; not just attacks—user errors, natural disasters, provider outages and the like.

    The process of each P&P looks something like this:

    It’s a straightforward and natural process, but the simplicity of the ingredients (like cornstarch and water) makes it very easy to overlook its effectiveness.

    Additionally, make these P&Ps available—whether in full or by using excerpts or summaries—to prospects and customers. A major part of driving security is opening oneself up to public scrutiny. This could be via a secure portal, a public-facing page or a ticket/request system.

    BONUS Assessment for Transparency

    Take a look at the Consensus Assessment Initiative Questionnaire (CAIQ) by Cloud Security Alliance (CSA). Level one, which is a free self-attestation, is an often-requested assessment. It’s fairly rigorous and may require you to level up security in your company, but it’s a well-respected and no-cost attestation and is available free for prospects and customers to view.

    The Next Step

    Inserting or improving security in the DevOps process is part of the true DevOps process. It’s a mindset and culture more than a title or official designation–and it’s possible.

  • What Are the Seven Layers of the OSI Model?

    What Are the Seven Layers of the OSI Model?

    Working in the software field, it’s not uncommon for engineers to refer to different “layers.” Perhaps you’re working with protocols at the “networking layer” or evaluating a solution that sits at “Layer 4” or “Layer 7.” Although these concepts are obvious to some, not everyone knows what these layers refer to. To get you up to speed, below, we’ll define the seven layers of the OSI Model.

    What Is the OSI Model?

    The Open Systems Interconnection model (OSI model) is a conceptual model to help visualize a computerized network. Made up of seven layers, it represents the distinct levels that make up an end-to-end computing system. In the spirit of promoting open interoperability, the model is intended to represent a universal standard that’s agnostic to a specific technology or vendor.

    The OSI Model was co-conceived by International Organization for Standardization (ISO) and Internet Engineering Task Force (IETF) and first published in 1984. It has since been redefined as ISO/IEC 7498-1:1994. Although the OSI Model has been around for decades, it’s still referenced quite often.

    The Seven OSI Model Layers

    The OSI Model is split into seven abstraction layers: Physical, data link, network, transport, session, presentation and application. You can think of the bottom one, Layer 1 (the physical layer), as the closest to the most rudimentary electrical connections. The farther up you rise, the closer you get to Layer 7 (application layer), which is the most user-facing category.

    1. Physical Layer

    The physical layer, Layer 1, represents the lowest level of data transmission. This category refers to raw unstructured data bits—and the process of converting them into electrical signals for a device to read. “Physical” hardware like network hubs, modems, adapters, cabling and network controllers work in this layer. Bluetooth, Ethernet and USB describe physical layer specifications.

    The data link layer deals with data packaged into frames. Technologies in this layer assist in node-to-node data transfer, and the protocols here describe how to establish and terminate a connection and how the connection should flow. The data link layer is further divided into two sublayers: Media access control (MAC), responsible for how nodes connect with one another, and logical link control (LLC), which checks for errors and orchestrates the frame flow and synchronization. An example data link layer protocol is Point-to-Point Protocol (PPP). MACsec also can work to apply encryption at this layer.

    3. Network Layer

    The network layer is responsible for sending and receiving data frames structured in packets. Technologies in the networking layer use routers to send packets to nodes on different networks. The network layer might split or fragment the message if it exceeds the maximum network packet sizes. You’re probably familiar with the network layer if you’ve looked up your IP address—the network layer uses the IP protocol (or other logical protocols) to find locations. Such protocols specify a message along with the address of the intended recipient node.

    4. Transport Layer

    The transport layer deals with the transmission of data segments, which are sequences of data at variable lengths. The goal of the transport layer is to optimize data transmission by performing data segmentation and adjusting the size of the package or the rate of transmission. Transport layer protocols include TCP and UDP. Tunneling protocols also operate at the transport layer. The OSI Model also defines five classes of the connection-mode transport protocol.

    5. Session Layer

    The session layer handles the communications between two or more computers. Protocols here are used to create a “session” between entities, which is common in applications that use remote procedure calls. The session layer handles the connection and authentication between a client or server, including actions like logon, look up, log off or session termination. DNS, along with name resolution protocols, operate in the session layer.

    6. Presentation Layer

    The presentation layer is about data translation and formatting. In this layer, protocols handle things like encryption, decryption, compression and decompression. The goal of the presentation layer is to transform data in such a way that it can be sent over a network in syntax that fits the constructs specified by the application layer. For example, technologies that serialize data structures to XML or JSON can be thought of as working for the presentation layer. This sort of data translates into what is graphically displayed for the end-user.

    7. Application Layer

    The application layer is the top layer of the OSI Model and sits closest to the end-user application. User-facing software directly interacts with the application layer through functions like file sharing, message handling or database access. High-level protocols such as HTTP and FTP are used in this layer to share resources. Web browsers and email clients are examples of applications that interact with the application layer.

    The OSI Models: A Helpful Taxonomy

    There you have it—a primer or refresher of all seven OSI Model layers. It’s important to note that the model does not intend to serve as an implementation specification itself—it’s purely a conceptual framework. But it does help provide context into where tools operate and how they interact with elements of a distributed computing system.

    In the context of DevOps technologies, Envoy-based service mesh is often described as working on Layer 4 (the transport layer) and Layer 7 (the application layer). Or, since eBPF filters network frames, it can be said to work at Layer 2 of the OSI Model.

  • A Guide to Cloud Migration Trends and Strategies

    A Guide to Cloud Migration Trends and Strategies

    For several years, we have witnessed widescale cloud adoption as a software infrastructure growth and maintenance strategy. Spending on cloud application services (SaaS) continues to grow as monolithic applications are replaced by microservices-based apps and monoliths become relics of the past. In 2021 alone, companies spent $410.9 billion on public cloud services, and Gartner expects this number to rise by 20.4% in 2022 to reach $494.7 billion. But as this movement continues to evolve, engineering leaders preparing their cloud migration strategy must be up to speed on the latest key trends.

    Is Migrating to the Cloud Cost-Effective?

    For companies migrating to the cloud now, the process is no longer a simple transition from on-premises to remotely hosted servers. Cloud service providers now offer a wide variety of options, and companies run the risk of overspending in the cloud marketplace.

    A lack of clarity of their business and product needs or submission to shiny object syndrome can drive companies to spend on non-essential services, especially during migration. It is easy to fall into this trap; companies only realize it after they have committed and try to undo the damage by reversing the process.

    Although many public cloud providers offer pay-as-you-go pricing, companies often purchase more services than they use. When companies are unclear about their objectives, such expenditure is more extravagant than the utility. This awareness of cloud spending has encouraged companies to take stock of their needs and expenses, and when they do not add up, to consider partly shifting back to on-premises.

    But Isn’t On-Premises a Thing of the Past?

    Not yet, but gone are the days of mindlessly unloading entire infrastructures into the cloud. Increasingly, companies are aware of the costs of pulling their data from the cloud.

    For many, security and control concerns motivate them to keep data on-premises. The public cloud has its benefits, but private storage has its place, too, especially for some components of your software. All in all, on-premises is here to stay, as companies continue to acknowledge and appreciate its value despite their reliance on public cloud environments.

    Why Do Companies Choose Cloud Migration in 2022?

    Cloud technologies can potentially deliver faster and better-performing experiences to end users, while innovations are giving rise to new services that cloud vendors can capitalize on. Various use cases are surfacing as companies understand the cloud better and see its potential. They are pushing the boundaries on how they can use the cloud, and cloud service providers are keeping track of the changing nature of the demand.

    For example, cloud-based gaming platforms like Amazon Luna are seeing significant growth. The cloud is also of great value to artificial intelligence and machine learning efforts, and these are often intertwined.

    The Great Resignation phenomenon also drives some companies towards the cloud. Although some knowledge is needed when moving to the cloud, cloud services offer automation and require less employee supervision in the long term.

    The COVID-19 pandemic has also been a catalyst for accelerating cloud adoption, making resiliency a priority. Companies can stay afloat in an emergency if their workloads are moved to the cloud. It comes at a cost, sure, but allows companies to outsource time-consuming responsibilities and focus resources elsewhere. While this was most significant in 2020 and 2021, this logic remains relevant.

    Cloud computing is also helping companies improve performance, efficiency and scalability. In addition to AI, the cloud will be instrumental in the development of more powerful augmented and virtual reality apps.

    Approaches Defining Cloud Migration Today

    The possibility of these approaches or facilities also motivates companies to migrate to the cloud:

    Containerization: The process of compiling and storing a service, along with its dependencies, in a container. Due to their small size, multiple containerized applications can run on a server, allowing for easier and cheaper shipping of the service. According to CNCF’s 2021 Annual Survey, containerization is now a mainstream practice, and Kubernetes, for instance, is a heavyweight in this domain, with 96% of companies employing or evaluating it.

    Security: In Q1 of 2022 alone, 90% of data breaches had their origins in cyberattacks. Mindful of this alarming trend, companies are factoring security concerns into their cloud adoption strategies. We are also witnessing a shift in businesses’ approach to security via DevSecOps: Incorporating security as a continuous practice from the beginning of the software development life cycle instead of relegating it to the back-burner until the last moment. Additionally, frameworks like secure access service edge (SASE) and customized cloud disaster recovery strategies are becoming essential elements of cloud adoption.

    Sustainability: Sustainability is becoming a key component in most business models. Companies are eager to adopt practices and models that reduce their carbon footprint. This impetus is also emerging from business partners and investors who are beginning to employ sustainability as a critical factor when deciding which companies to work with. When a cloud migration is planned carefully, chances are that you will retire certain services in your infrastructure and make decisions about what to migrate and what to retain. By optimizing your migration strategy, you are likely to only run and pay for the services you need, ensuring that your energy expenditure is under control.

    Edge computing: Edge computing, a $176 billion market in 2022, emphasizes the importance of building data centers locally; i.e., closer to where they are needed for storage and computational purposes. It solves any bandwidth and latency issues. Improved security is another added benefit of this practice. Edge computing has a bright future as it attempts to compensate for some of the pitfalls of using centralized servers in the cloud.

    Cloud-native applications: Cloud-native applications are easier to work with on the cloud, as they are designed and built on the cloud itself, not rearchitected to ensure compatibility with cloud services. When compatibility is not a concern, they can leverage various benefits offered by cloud services. As a result, scalability, flexibility and resilience are built into these applications. With cloud adoption, companies are increasingly in favor of cloud-native applications. This subsequently informs their migration strategy as they decide which services to retire, rearchitect or build anew once the actual migration is complete.

    How Does the Hybrid Cloud Fit In?

    Hybrid computing is on the rise as it leverages the public and the private cloud to deliver maximum benefit to companies with little to no compromise. The public cloud offers scalability, flexibility and speed, while the private cloud provides cost-effectiveness, security and compliance with regulations. While sensitive data can rest on the private cloud, frequently accessed data can be stored in public servers like AWS and Google Cloud. Investing in a hybrid cloud strategy from the point of migration itself can go a long way in giving companies access to the best of both worlds.

    Similar to a hybrid cloud environment, a multi-cloud strategy where companies distribute workloads across multiple clouds put some control into the company’s hands and ensures additional flexibility. By working across cloud services, companies can pick and choose which services are most compatible with their application’s components. These can include services offered by different cloud providers.

    Cloud Providers Seeing Growth, and What They are Doing Differently

    Serverless cloud environments: Cloud vendors like Amazon, Microsoft and IBM are pushing serverless cloud services. Although these continue to rely on servers (despite the name), companies don’t rent servers or pay fixed charges. Instead, the storage and bandwidth infrastructure scales with the software as and when it requires it. Companies do not need to concern themselves with technicalities and can focus on innovation and improving their existing applications. An added advantage is the provision of safe testing environments that encourage developers to tinker with cloud services and develop solutions for potential failures or outages. Serverless cloud solutions are also beneficial for smaller companies that need not invest large amounts of capital to ensure growth.

    Partnerships: To meet the soaring demand for cloud services, partnerships among cloud providers are also on the rise. By partnering with other service providers, companies can offer more services by leveraging the resources and reputations of one another while strengthening their competitive edge in the cloud market.

    Industry optimization: Cloud adoption is not limited to software companies. Businesses across industries like healthcare, finance and retail are migrating their infrastructures to the cloud. This variety in demand has encouraged providers to customize their services to meet the specific requirements of each industry. Often, these include regulatory compliance needs in these sectors.

    The cloud is only getting bigger and better. You can develop a well-curated transition strategy with the knowledge of current cloud migration trends. Additionally, identifying your requirements at an early stage will go a long way in leveraging the cloud to your advantage.

  • API Gateway Vs. Service Mesh: What’s the Difference?

    API Gateway Vs. Service Mesh: What’s the Difference?

    In the average microservices architecture, app programs trade the stability and rigidity a call stack offers for the network’s flexibility. Consequently, latency, security and traceability issues that were previously absent become a service call concern. 

    Service mesh is the resulting solution to eliminate these issues and allow developers to focus on more important business solutions. The caveat is the functionality overlap that exists between a service mesh and an API gateway. But while the two are similar, there are also key differences between both options. Here’s what you need to know.

    Defining a Service Mesh

    A service mesh is a comprehensive architectural pattern set designed to deal with enterprise concerns like instability, debugging and security. These concerns arise when a team moves from a call stack to network calls. 

    For instance, a function call via a call stack recognizes the constant availability of the requested function. On the other end of the equation, a network call doesn’t offer this functionality. 

    A service mesh is the preferred option because it assists the system’s client endpoint in managing network instability. The primary method for solving this network instability is frontrunning retries transparent from the client app’s viewpoint. 

    The service mesh also assists the server’s endpoint by rerouting incoming requests to the server node that’s best able to deal with the incoming request. The choice of server node is a function of previously determined policies for routing incoming traffic.

    The ideal service mesh is installed with two layers: The control plane and the data plane. The latter is a proxy for any connection’s server and client endpoints. 

    The data plane also enforces policies from the former while running operation metrics to a monitoring tool. Conversely, the control plane layer manages all attending service policies alongside the data plane’s operation. 

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    Key Features of a Service Mesh

    The average service mesh has a number of primary features. 

    But there are a few things that bear mentioning. Say your app development team is working on a small business phone system. This particular service implementation doesn’t need every feature listed below. It depends on your team’s specific needs and traffic setup.

    Traffic Routing

    This feature enables the service mesh to route requests based on preset configuration or a predetermined policy. The system may prioritize traffic from specific client applications. Alternatively, a service mesh can re-route traffic to different service versions for:

    • A/B testing
    • Canary release
    • Service versioning

    Observability

    Service mesh implementations feature proxies that record service calls for individual clients and services. This feature takes the manual burden of the call-logging process away from the developers. 

    Plus, teams can leverage downstream analysis and monitoring tools to report on system availability and overall performance. These monitoring tools also provide a framework for basic tracing across all call chains. 

    The observability capabilities of a service mesh extend beyond the above. For example, some coding work can allow developers to improve call chain monitoring to allow for accurate tracing for enterprise business transactions.  

    Common service mesh observability features include: 

    • Latency, error rate and throughput alerts
    • On-demand service dashboards and graphs depicting the connection between individual services
    • Tracing the journey a business transaction or request makes through the service mesh

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    Security Policies

    An application with multiple independent services has a huge attack surface. Essentially, each independent service is a vulnerable entry point that requires protection. A service mesh has proxies on both the server and client endpoints to secure any communication between both terminals. 

    A service mesh also improves security by eliminating the dependence on developers to manually program security into every service. Instead, the server proxies will handle authorization, authentication and encryption for each independent service. The resultant effect is a zero-trust security framework.  

    Identity

    A service mesh manages who can access individual services. It also maintains a real-time log of stakeholders accessing each service and when they did so. The system validates identity through JSON Web Tokens (JWTs) while allowing authorization depending on the requesting service and the stakeholder. 

    Encryption

    Compliance, security and data protection are key considerations. Well, communication between all services in a service mesh is fully encrypted. The control plane is responsible for certificate management functionality including certificate rotation and generation. 

    Furthermore, a service mesh uses mutual TLS where both server and client endpoints whitelist the certificates that can operate on the other side of any connection. A service mesh provides robust mutual TLS authentication, allowing for seamless system authentication and encryption. 

    Resilience

    This feature defines the ability of a service mesh to insulate developers in scenarios where the principal service function goes offline. In some instances, the proxy may follow an alternative service path or switch to a backup framework. 

    For illustration, streaming giant Netflix has a personalized user recommendation feature that suggests titles to its users. If this service goes offline, a service mesh system can ensure a switch to a default recommendation feature with zero personalization. Only when this backup fails will the system return an error code. 

    A service mesh assures developers that when a service fails, the proxy has done all it can to manage communication errors. In some cases, the mesh can ensure optimal performance by switching to a different service with the lowest latency.

    Examples of configurable resilience patterns for a service mesh include 

    • Circuit breaker patterns
    • Rate limiting
    • Retry policies

    Another illustration of a service mesh’s resilience is the ability to build chaos engineering capabilities. The service mesh can inject itself into the endpoints within the network, providing the observability required to monitor a chaos test. 

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    What an API Gateway Entails

    Whether the team is working on contract management software for small business or an expense calculator app, the choice of API can determine the results of your team’s app development efforts. But more than the choice of API, proper management is another crucial talking point. The best results come with a system that identifies incoming calls, directing them to the appropriate resource. 

    API gateway is the future of DevOps and is the solution to that need. It’s the technology framework for routing API client calls to the ideal application. It also processes the call, forwarding resulting responses to the client’s network.

    At the technical level, an API gateway isn’t a must-have. Find a developer with a digital business card, hire them, and they can always program an embedded code responsible for call request routing. The only caveat to this alternative is the manpower and resources necessary to program and insert codes for individual call requests. As such, it’s always better to have an API gateway to handle this task. 

    An API gateway is a more innovative solution because extensive coding always holds the possibility of risks and errors. Furthermore, these errors can complicate app development stages like maintenance, bug fixing and application testing. 

    The API gateway ensures all API requests reach their intended destination with zero diversion. Plus, it also offers the following added functionalities:

    • API request monitoring
    • Shifting API requests between individual nodes
    • Request balancing for multiple instance situations

    The Major Differences Between API Gateway and Service Mesh

    An important digital workplace definition that differentiates both frameworks is the placement location. The API gateway is necessarily present on public-facing systems. Other key areas where an API gateway and service mesh differ include:

    Observability

    Observing the performance of the average API gateway is a more concrete task. The framework can provide actionable data insights like how long the API takes to respond to requests, problematic features, and their impact on network traffic. 

    With the service mesh, the ability to spot performance issues is a given. However, the system can’t ascertain any effects of performance lags on end-user response and experience. Therefore, while troubleshooting is easy, gauging the extent of the problem’s impact is more complicated. 

    The Communications They Handle

    Both frameworks manage different communication nodes—one handles internal discussions and the other external ones. The API gateway is the component responsible for routing external communications. For example, the API gateway handles chatbot connections, purchase orders and visits to specific pages. 

    Conversely, the service mesh is responsible for internal communications in the system. For illustration, a discussion between multiple microservices under the same app environment will fall under the service mesh’s purview. 

    In other words, the API gateway manages client-server discussions. Meanwhile, service-service communications are for the service mesh. 

    Management

    Both frameworks may have some areas of overlap, but the amount of management each one requires isn’t one of them. API gateways are simpler to manage and develop. As such, they’re easier for your app development team to manage. 

    The deployment for an API gateway happens just once, regardless of the service app. Plus, post-deployment centralization and monitoring are easy tasks. 

    In contrast, a service mesh is more complicated and tedious to manage. Your app development team must deploy the service mesh function for individual app systems. 

    Compatible Tools

    Most API gateway tools and software resources are highly expensive. Plus, they’re compatible with every popular app architecture. 

    The other end of the equation offers more accessibility. There are countless service mesh tools and resources for varied functionality. Plus, the majority of these support tools come at zero cost because they are open source.  For example, there’s Envoy and Istio, both of which are free and open source. 

    However, you shouldn’t expect to enjoy cross-platform compatibility across these service mesh tools. For example, Istio is only compatible with Google. On the other hand, AWS App Mesh—another service mesh tool—is compatible with AWS Cloud. 

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    Difference  API Gateway  Service Mesh
    Existing Functionalities
    • Useful for API requests between the client and server
    • Compatible with external API requests and internal calls. 
    • Handles internal communications 
    • Best for improving the portability of service-to-service calls 
    Digital Transformation Functionality
    • Longer app delivery cycles 
    • Zero security risks
    • Useful for effective microservices management 
    • Speeds up the network’s delivery cycle 
    • Security risks due to open loopholes when the program runs alone
    Operations Reroutes API calls present outside of the given application environment  Works within the application architecture 
    Maturity Level API gateway is mature technology. It has been a core developer tool for several years now Service mesh is still a relatively new technology and requires more research and development
    Security Processing Automated security protocol Manual security systems 
    Usage Complexity Application complexity remains constant with endpoints remaining unchanged  Business scalability can be complex as each update comes with new endpoints 

    Can Service Mesh and an API Gateway Be Used Together?

    The unlikeliest of brands often form marketing partnerships. In the same way, there are instances where using both a service mesh and an API gateway is a logical and feasible idea. 

    While differences exist between an API gateway and a service mesh, it’s not an “either/or” situation.

    Cloud contact center teams looking to streamline communications at both the internal and external levels are already leveraging both technologies. And you can also leverage both systems to achieve the following:

    Improved Innovation

    Effective API use, especially during external interactions, requires internal resource optimization, which is better when teams deploy a service mesh alongside an API gateway. 

     

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    Accelerated Digital Transformation

    Leveraging both resources improves enterprise digital transformation rates. Plus, any application with both technologies will enjoy a unified API gateway for internal service mesh management. If there are any linked applications, the unified API makes them simpler to handle.

    Increased Security Scalability

    Scalable container security is achievable when you deploy a service mesh and API gateway together. The service mech will boost the system’s interservice connectivity while the API gateway acts as a central point for all client-server requests. 

    The API gateway also provides the much-needed technical support for increased proxy security. It also fosters quick threat detection and resolution at the proxy level. 

    API Gateway and Service Mesh are Different

    Both the API gateway and service mesh are designed to simplify communication while also reducing any coding responsibility on the app development team. While both systems can ensure network calls through an enterprise contact center reach the target destination, they’re structurally different.

    Above, we’ve highlighted the key differences between both frameworks. But despite these differences, both systems are compatible and can work together for improved app development. We recommend leveraging a service mesh and an API gateway together for better security, innovation and app scalability.

  • Majority of Open Source Devs Open to Change Jobs in 2022

    Majority of Open Source Devs Open to Change Jobs in 2022

    Open source is the foundation of most modern software applications. Now more than ever, retaining developers with experience working on and with open source projects is critical to maintaining software development agility. Yet amid the Great Resignation, it can be challenging to keep talent happy—organizations must demonstrate a commitment to employee benefits, mentorship and cutting-edge technologies to remain competitive.

    The EDB Open Source Talent Survey 2022 surveyed over 1,400 developers and DevOps professionals working on open source projects. Below, we’ll highlight the key takeaways about their volume of work, the quality of on-the-job training and top motivators that could influence workers to take a job elsewhere.

    The Great Resignation Persists

    It’s not news that IT is under mounting pressure to deliver new features and applications to meet surging digital demands. As a result, the amount of work has increased across the board. The report found that 67% of respondents said their volume of assigned work has increased in the past 12 months.

    Employer favorability has also declined slightly—28% have a less favorable view of their employer over the last 12 months. Although 46% are satisfied with their current job, 44% are satisfied but would be open to new opportunities. And 10% said they were not satisfied with their current position. Taken together, that means 54% are open to or actively looking for new roles.

    Mentoring: An Enticing Benefit

    So, what are the main drivers that would influence IT workers to change jobs? It’s no surprise that increased pay and benefits are a top reason behind most resignations. However, salary is not the only factor—43% say improved career opportunities influence a job shift, a steep rise from 21% in 2021.

    On-the-job mentorship and training are also becoming more desirable. A little over a third (38%) said that better access to mentorship would make them consider another job opportunity. This was followed by working with more cutting-edge technologies (32%), greater access to training and certifications (30%) and allotting more time for open source projects (19%).

    Interestingly, one-fifth of responders who said they were satisfied with their current employer said their organization introduced a mentoring program in the past year. This indicates that investment in employee career advancement helps increase morale and retain skilled workers.

    Career Advancement And Training

    Out of the above reasons developers are open to switching jobs, one factor in particular is increasingly important: Working with cutting-edge technologies. This figure doubled in the last year, rising from 16% to 32%. Most engineers prefer not to work with cumbersome legacy technologies or manage rising technical debt. And if an organization isn’t evolving its software infrastructure, employee skillsets become stale.

    Working with new open source or cloud-native technologies can help retain workers, but will likely require training. In terms of specific categories, 41% said that training in emerging technologies, like cloud, is a favorable factor in considering another job opportunity.

    From a security perspective, ongoing training is also vital to staying on top of emerging exploits. As a result, 43% said security training is an interesting perk when considering a job shift. These areas are followed by training on core technologies like database and infrastructure (38%) and training on compliance (37%).

    Final Thoughts

    Open source software is pervasive and at the heart of today’s digital innovation. Nearly all applications depend on some form of open source software, but it’s no less prevalent in the DevOps sphere, where open source projects are used in continuous delivery, container orchestration, observability and a multitude of other stages of the software life cycle.

    Supporting the needs of skilled labor will be necessary to leverage the benefits of open source projects. As such, organizations will require not only competitive wages but also relevant training and investment into cutting-edge technologies to retain such developers.

    Another factor that could drive workers away is unnecessary toil, which can negatively affect morale. A company’s environmental, social and governance (ESG) initiatives can also affect its talent pool. Furthermore, a commitment to high-quality developer experience can ensure the needs of developers are heard and respected.

    The EDB Open Source Talent Survey 2022 surveyed over 1,400 application developers and IT professionals from around the world. It targeted open source technology users, specifically those holding application development, information technology and operations titles. For additional insights, you can read the full report here.

  • What You’re Getting Wrong About DevOps (and How To Make it Right)

    What You’re Getting Wrong About DevOps (and How To Make it Right)

    DevOps is now the top software deployment strategy by a mile — 77% of organizations say they use the approach to roll out new software. This is good news because a DevOps approach is an important marker of business maturity, and its benefits (more on that later) are so tangible that most organizations don’t need to be convinced that DevOps is the future. 

    The bad news is most organizations are getting DevOps wrong, which is probably why only 10% report that their organization is “very successful” at achieving rapid software development and deployment. My company started its DevOps journey in 2019 as we began to build out the next generation of our ERP software. We were committed to the strategy but made mistakes along the way. 

    Our experience taught me the typical approach to DevOps (the one we took at first and the one many organizations follow now) is an improvement, but that a mindset shift can truly unlock the DevOps strategy’s potential and enable companies to maximize benefits. Here’s a closer look at where companies can go wrong—and what they stand to gain when they get it right. 

    What Happens When Organizations Get DevOps Wrong

    At its core, the DevOps concept is simple: Operational considerations should be considered at the development stage. As SaaS solutions gained popularity, the idea that issues like downtime should be everyone’s problem gained currency. Many organizations interpreted this to mean there should be a DevOps team that acts as a bridge between the development and operations teams.

    It actually makes more sense to merge the teams entirely. If a DevOps team sits in the center, the silos on the development and operations sides still exist. There can be even more communication breakdowns if there are three teams instead of two, and user needs can still be overlooked. A merged operation addresses this by creating a “you build it, you run it” mindset, which eliminates the disconnect. 

    We’ve found that a merged DevOps group handles operational issues as part of the engineering process from the earliest stages. This approach simplifies the process of identifying and addressing operational issues so that the software is more resilient and does a better job of meeting user expectations. 

    What Happens When Companies Get DevOps Right

    Simply put, making this mindset shift to get DevOps right lets your organization build better products, expedite updates, create greater alignment between teams and execute tasks more efficiently. This improves the experience for everyone, including the organization, its customers and end users. 

    One of the key benefits is better uptime. If you incorporate operational elements from the beginning, you reduce the risk of failure. When the groups are separated, operations teams often find it difficult to pinpoint issues, and the larger the system, the longer it can take to locate the problem, which can result in unhappy customers and unacceptably long outages. 

    By merging engineering and operations, you eliminate the tendency of some engineers to regard operations as someone else’s problem. A DevOps strategy that merges the teams makes operations everyone’s problem. And when everyone is subject to late-night calls for help if a problem occurs, it brings home the need to be proactive about preventing issues in the first place. The result is greater stability and reliability—and happier customers. 

    Maximizing DevOps Benefits

    Our experience taught us that merging engineering and operations was the best way to maximize DevOps benefits. It was a mindset shift that didn’t occur immediately; once it did, we realized how powerful it could be. One key component of the shift is to enhance team effectiveness by hiring engineers who are operations experts and honing existing staff skills as necessary. 

    Another important element is to get software engineers on board with the new approach and make it clear that the change is a priority. It’s easier to coast along as if the present approach is working perfectly, but that prevents your DevOps strategy from reaching its full potential. If you prioritize change and acknowledge the challenges involved, you can overcome them and reap the full benefits. 

    The consensus is clear: DevOps is the logical next step toward business maturity. But how you implement your DevOps strategy determines the magnitude of the improvement. Merging your engineering and operations teams is a challenge, but the reward is better products, quicker updates, tighter team alignment and an improved experience across the board. Getting DevOps right is worth it. 

  • Simplifying DevOps and Infrastructure-as-Code (IaC)

    Simplifying DevOps and Infrastructure-as-Code (IaC)

    Let’s face it, tools for infrastructure-as-code (IaC) configuration management may be vital, but they’re difficult to use. But what if it’s not so much the tools themselves as it is our understanding of them? What if we looked at provisioning and configuration systems differently and did so using vocabulary that was less confusing and more common? 

    Simply put, we’d gain a greater understanding of these tools—and, therefore, they would be simpler to use—if the terminology was simpler. Reframing these complex tools would not only allow us to make greater sense of this realm, but we’d be more effective in addressing the DevOps and software issues we all experience. 

    Coming to Terms With Infrastructure-as-Code (IaC)

    Software engineering encompasses all the actions developers take to create software and technology products. This typically entails writing code, testing it and making sure it satisfies the original requirements for users. Operations is the practice of ensuring those software systems are ready to go and will be able to serve users on a day-to-day basis. Operations engineers aren’t as focused on writing new code as developers are; instead, they are focused on how existing software (mis-)behaves in existing systems.

    Now let’s cover some other key phrases we use and hear all the time in DevOps.

    Forward engineering means creating a system based on requirements that exist for that system. It entails first building a model, implementing it in software, then configuring and deploying it into operation. While the goal is for the new system to closely match the original requirements, the modeling process can be ambiguous, causing a system to perform in unanticipated ways. Furthermore, over time, the system itself may differ from the original model due to interaction with its environment. Consider that some parts can change the configuration of other parts; procedures performed by engineers shift the system state or configuration, too.

    Unfortunately, forward engineering offers no feedback with regard to the eventual success or failure of the product during the model creation process. These models and the subsequent software are created first and then deployed to test for success. Such a system can only provide feedback later based on its behavior, which can then be used to improve and build new models.

    Reverse engineering calls for understanding existing systems, usually by first creating a partial mental model by inspecting such a system and then using it to plan any modifications. Often, changes are made directly to the system without always fully understanding how it works. Feedback for changes is fast because putting the system through its paces and inspecting the results is relatively simple, easy and cheap to do. A good example of reverse engineering is software security professionals who scan network services or memory to identify anomalies and exploitable gaps and then try to manipulate their content for instant feedback.

    Then there’s infrastructure-as-code (IaC). This modeling technique began with declarations about how server operating system content should appear and was later adapted for the purpose of laying out and configuring cloud resources using API calls. While many vendors and products have come and gone, most tools have been declarative—with good reason.

    Anything to Declare?

    With declarative tools, you specify how things should appear without explicitly showing how you’ll get there. Why? It’s the easiest way to create models, templates and maps because engineers only have to declare how things should look, not get them into the correct form or place to actually do so. The emphasis is on evolving from iterative improvements that take place over time.

    Another feature of declarative IaC tools is idempotence, which means repeating an action multiple times with consistent results; the system only changes when it detects deviations from the model. This can be compared with the imperative approach, which requires developers to first find all the differences between a model and system and then write each step that’s necessary for the system to match the model.

    Declarative IaC tools eliminate the need for developers to write an implementation for these changes manually. These tools save thousands of labor hours by publishing templates that can be used repeatedly to build identical systems with less time and effort. The problem is that declarative tools rely on a forward engineering mindset—meaning there’s no way to get feedback from a live system—leading to many complaints from users.

    The Rift With Drift

    Configuration drift is what happens when a declarative forward engineering model no longer matches the state of a system. It can happen when a developer changes a model’s code without updating the systems built using that model. It could also be the result of an engineer that does exploratory ad-hoc operations and changes a system but fails to go back into the template and update its code. 

    Is it realistic to ban operators from ad-hoc exploration? Actually, some companies have policies forbidding any operator or developer to touch a live production environment. Ironically, when a production system breaks, it’s the first rule overridden: Ad-hoc exploration is welcomed by anyone able to get to the bottom of the issue. 

    Engineers who adopt IaC usually don’t like the work that comes with remodeling an existing system. Still, because of high demand fueled by user frustration, there’s a tool for every configuration management language—they just fail to live up to engineer expectations. The best-case scenario is that an engineer can use a reverse-engineered template to copy and paste segments into one but will need to manually write it elsewhere.

    Bridging Gaps

    What is needed is a new type of IaC tool. These must integrate reverse engineering at their core and allow feedback from living systems to update the very models that created them. In doing so, operators can adopt an ad-hoc change or simply revert back to the system as it was declared in the original model.

    Yet, today’s standard tools fall short of this goal, and aren’t much good for helping engineers solve system operations issues. True, there are those capable of auditing templates and singling out misconfigurations and mistakes. Still, while helpful, the greater value would be the ability to review a living system, not just the templates that engineers initially used to create it. 

    We have monitoring and observability tools and IaC tools, but in between them is a wide chasm. For effective DevOps, what’s required is a new breed of IaC tool, one that integrates reverse engineering practices that supports operations engineers and enables companies to bridge any development gaps.  

  • Future of DevOps: Trends to Watch

    Future of DevOps: Trends to Watch

    Technology is transforming every aspect of industry, and digitalization and automation have flourished in the past few years. DevOps has established itself as an indispensable software development methodology for successful digital transformation.

    According to a recent market survey, the DevOps market will exceed $20 billion by 2026, growing at a CAGR of 24.7% from 2019 to 2026. DevOps has facilitated rapid and dependable software development, delivery, improved quality and higher customer satisfaction.

    DevOps, too, is continuously evolving to meet the changing demands of the corporate environment. Let’s look at the future of DevOps and the trends you should expect to see.

    Serverless Computing

    DevOps teams will see a rise in the adoption of serverless architecture, mainly because of the massive capital and operational costs of server infrastructure. Numerous DevOps groups are working with modular components offering an overview of the pipeline. Moving these modular components to serverless architecture will empower groups to mitigate a few pieces of errands identified with pipeline architecture and focus on deployment and development of the product.

    Serverless computing streamlines overall DevOps operations, including the complete software life cycle, from development to deployment, testing and maintenance in one place. Also, serverless computing reduces the developer’s workload by taking care of server maintenance, such as cloud monitoring and system updates. Other prime USPs include flexibility, reliability, speed and cost-efficiency.

    Microservices Architecture

    Microservices architecture has been gaining popularity as an alternative to traditional monolithic architecture in the IT industry, and this trend is expected to continue. Microservices architecture is a pattern of organizing computer systems into segments that can scale on demand. A microservices architecture pattern is the next evolution of service-oriented architecture (SOA) that structures an application as a collection of coupled services. If one piece of the application fails, microservices make it easy to identify and contain the issue without disrupting the whole application.

    DevOps and microservices architecture enable decentralized teams to innovate rapidly, maintain control over their tech stack and standards, track performance metrics, supervise the development and release cycles and reduce overall consumption time by achieving shorter software release intervals.

    Low-Code/No-Code Applications and Platforms

    To keep up with the demand for new applications and features, many organizations have turned to low-code/no-code development to deploy applications swiftly. Adopting a low-code approach with DevOps is a game-changer. It enables developers to experience higher agility and gives the organization a competitive advantage in the fast-paced, demanding software market.

    Low-code applications allow non-technical ‘citizen developers’ to contribute to software development with the help of visual interface integration. In addition to end users, developers and DevOps engineers are set to play a crucial role in this trend, using low-code platforms and tools that offer drag-and-drop elements and other extensions. These low-code application development tools support every aspect of software development, including app ideation, analysis, software design, code development, quality assurance, deployment and documentation.

    DevSecOps

    Many large enterprises are integrating and automating security into DevOps processes. The transformation from DevOps to DevSecOps is expected to gain even more momentum in the future. According to a forecast, the DevOps market size is expected to reach $5.9 billion by 2023 at a CAGR of 31.2% during the forecast period.

    More companies will embed security controls earlier in the software development cycle to ensure application security. Tools and frameworks will simply integrate with CI/CD pipelines and testing tools will see a rise in 2022. DevSecOps empowers developers to consistently track, monitor and remediate security defects during the various stages of development while improving the speed of delivery and quality.

    Kubernetes and GitOps

    Kubernetes is an open source container orchestration platform emerging as an infrastructure trend on the operations side. Kubernetes will expand further into the software development space in 2022 and beyond. It will ease the task of starting a local Kubernetes cluster on a developer’s machine. Kubernetes security is expected to take center stage as well, through infrastructure-as-code (IaC) scanning.

    GitOps is a Kubernetes-related infrastructure trend that allows developers and IT operators to use Git to manage clusters and deliver efficient, secure applications without misconfigurations. It automates releasing incremental updates, and consistent delivery will enable developers to build, test and deploy software rapidly.

    AI and ML

    Artificial intelligence and machine learning have already transformed the way businesses operate nowadays. In 2022, it is anticipated that AI/ML will further streamline and accelerate every phase of the SDLC. It enables DevOps teams to enhance their performance through intelligent automation, improving feedback and managing alerts.

    Advanced analytical methods like predictive analytics are becoming increasingly important as the market evolves. AI can enable proactive investigation of the DevOps pipeline to identify problems and offer possible solutions. AI and ML integration also enable deep learning, data mining, statistics and modeling, becoming an integral part of your business’ workflow.

    Conclusion

    DevOps continues to evolve and pivot to drive significant IT transformations that directly bolster business goals and objectives. It will assist in enhancing the company’s ability to design, produce, launch and maintain high-quality software solutions. The above trends will help organizations move rapidly beyond automation, focusing on continuously improving outcomes. These trends are catalysts for creating a regular and dependable release pipeline and building better communication between development, IT and business teams.