devopsy.kinsta.cloud

Category: Features

  • 3 Reasons Your Digital Transformation Stalled

    3 Reasons Your Digital Transformation Stalled

    The more a company modernizes, the more value it gets from the cloud, but not all digital transformation efforts are created—and executed—equally. According to Gartner, “87% of senior business leaders say digitalization is a company priority, yet only 40% of organizations have brought digital initiatives to scale … [and] without a shared vision across the organization, digital transformation efforts will continue to stall.”

    CIOs and other senior technology leaders are constantly looking for the best way to lead and scale modernization efforts that positively impact the business and generate the most value from the cloud. Here are three factors to consider to make sure digital transformation efforts don’t start to slow or stall out entirely.

    The Price of Ignoring Technical Debt

    Any CIO should be able to say how much of their development efforts are invested in innovation and how much is going into maintenance. A harder number to take into account is how much their innovation development effort is slowed down by technical debt.

    A good way to gauge this for CIOs—if you can say that your development organization dedicates more than 50% of its effort to innovation and that your development velocity is exactly where it was a couple of years back, then technical debt is not dragging you down. However, chances are that’s not the case.

    Why is that? A major reason is that the vast majority of business applications and business logic still reside on-premises in legacy applications and infrastructure with a once-solid software architecture that is now showing signs of wear.

    The technical debt these applications accumulated takes a toll. It is a ‘tax’ that many CIOs know they need to pay; know what is required, but choose to ignore until it’s too late.

    Many organizations delay modernization because they believe that it reduces the capacity for effort that can be spent on innovation, assuming that legacy maintenance can just carry on as usual. With the potential for a lengthy and risky modernization project, delaying innovation for that length of time is not even an option. The only way to stop ignoring technical debt is to find a way to reduce the risk of modernization and accelerate the modernization project so you can go back to doing what you love about tech: innovating.

    Managing App Modernization as a Tactical Project

    It’s amazing that application modernization is so often viewed as a tactical project—time- and materials-based, cost-focused—versus a strategic imperative driven by business goals. This approach also explains why many projects fail to meet expectations, are half-finished or abandoned or never even get started in the first place.

    The business logic and processes embedded in your legacy application estate are your core intellectual property and competitive advantage, and they need to be treated as such. These applications, and the business processes they encapsulate, require a strategy that continually modernizes them, starting now and continuing into the future. A one-and-done approach is a recipe for disaster and disappointment.

    So be strategic, make sure your modernization plan is aimed at reducing technical debt, restoring velocity and focusing on innovation. Once you get there, make sure you continuously manage your technical debt and keep your velocity high.

    Reliance on Lift and Shift for Cloud Migrations

    Cloud migration projects are most often critical parts of digital transformation initiatives, and yet, many outside advisors or quick-win focused insiders will push for the “easiest” approach.

    A rational plan to transform and modernize your applications for the cloud requires a critical assessment of your application estate to determine which applications should be refactored, lifted and shifted (rehosted or platformed), rewritten or just retired. Skipping this critical step and categorizing the majority of your apps into the “lift and shift” category is a common mistake that too many enterprises later regret. A lift and shift strategy may be fine for a subset of your applications, but for the most important ones it will result in a transfer of your technical debt to the cloud, increase your maintenance costs and take another bite out of your organization’s ability to innovate.

    Digital transformations are getting bogged down by technical debt, tactical decisions and modernization shortcuts. Organizations need to directly address these issues and reduce the significant drag they are incurring on modern transformation initiatives. By attacking them head-on, enterprises can accelerate their cloud migrations, application modernizations and business agility goals.

  • NS1 Touts a Common Delivery Platform for Devs and Ops

    NS1 Touts a Common Delivery Platform for Devs and Ops

    NS1 has extended the capabilities of its DNS and network management platform with the release of NS1 Connect. The platform is designed to serve as a delivery platform that integrates developer and network operations teams’ tasks for the creation and management of applications across highly distributed cloud-native and on-premises environments.

    While DevOps teams typically rely on dozens of tools for the deployment of software and the management of the network connections on which the software runs, NS1 was created to—among other things—reduce the number of different tools DevOps teams require by improving visibility, automation and management for these processes.

    In this way, NS1 Connect provides a unified API framework to automate DNS, traffic optimization and network services management across heterogeneous networks, Sanjay Ramnath, vice president of products at NS1, told DevOps.com. NS1 Connect’s reach covers internal and external applications deployed via on-premises, cloud or hybrid infrastructure. This enables NetOps and DevOps teams to integrate NS1 solutions into their infrastructure and CI/CD pipelines with lower complexity and enables NetOps teams to accelerate management and minimize the risk of misconfiguration, Ramnath said. “Prior to this, teams had to deal with complex manual processes or disparate API frameworks that slowed down their ability to develop, deploy and deliver applications.”

    In his talk, Your Business Depends on Eliminating Boundaries Between Applications, Users, Infrastructure, and Data during INS1GHTS 2021, NS1’s annual user conference, Ramnath described how a number of NS1 products and services are integrated into a single platform under the NS1 Connect umbrella. They include cloud-native network services for DNS, DHCP and IPAM (DDI), edge-to-cloud networking for NS1’s managed DNS, dedicated DNS, private DNS and DNS for China and application traffic automation for VPN traffic steering, pulsar active traffic steering and general traffic management. Other commonly used network services are provided as well, including IP address management that can be centralized across the network, said Ramnath.

    The idea was to remove many of the roadblocks DevOps teams face when seeking to deploy on highly distributed infrastructures. “The goal here is to remove some of those boundaries and barriers that are talked about on the infrastructure side,” says Ramnath. “[This helps to make sure] that our control plane is centrally provided and centrally consumable as a cloud solution and that our products can be deployed wherever they are needed in a zero-touch way, in a lightweight fashion, making sure that we are aligned with cloud-native operations.”

    The need to maintain low latency across distributed infrastructures is also key. As David Coffey explained during the conference in The Future of Application Traffic Intelligence and Automation session, low latency is essential for creating “that great experience for users.”

    “Low latency is not an attribute of this feature, it is a top, tier-one priority for people to accomplish, but it’s an incredibly complicated aspect, given all of the frameworks that are out there—the tools, the DevOps capabilities and the components required to support and deploy these modern-day applications,” said Coffey. “That’s what enables application companies to deliver that low latency and treat it as an attribute in the system; not having to go out of their way to create this. We want to enable all developers to embed this into their system—and we can provide them with a degree of success for delivering that great experience to their customers and to their networks.”

  • Why You Should Embrace Incidents and Ditch MTTR

    Why You Should Embrace Incidents and Ditch MTTR

    The cliché is that everyone in IT hates incidents, and the natural reaction when assembling incident response metrics is to look for numbers that you can lower over time. Fewer incidents and shorter incident response times must be better, we think. You might already be familiar with the common metrics associated with these goals, including mean time-to-resolution (MTTR) and total incident count.

    As it turns out, however, these metrics actually work against your company’s resilience goals. To understand why, let’s look at the industry that birthed most of our modern incident management practices and has learned the most about how to properly apply them: the airline industry.

    What the Airlines Can Teach Us

    From the 1950s to the 1990s, the airline industry built and refined best practices for incident management that we have since simplified and that have been widely adopted across the tech industry. What’s often termed “modern incident management” simply means the predictable and repeatable approach to handling unexpected disasters and the learnings-driven remediation steps that are prized when building a DevOps or SRE practice.

    One of the most important findings to come out of this research is the somewhat surprising fact that incidents weren’t being declared often enough. Instead, incident reports were created and the carefully assembled response and cataloging process was only followed after catastrophic events, which meant earlier opportunities to catch the underlying problem during a less severe incident were being missed.

    So why did an industry with such incredibly high stakes take so long to realize that more incidents were actually a good thing? You guessed it: a misguided desire to decrease the incident count. In chasing this goal of “lowering the number,” pilots and mechanics were often reluctant to file incident reports for anything less than the most critical situations, which meant the powerful, learnings-focused response and remediation process was only triggered in response to relatively rare and critical situations.

    This is a textbook example of Goodhart’s Law in action. To paraphrase, Charles Goodhart posited that any metric created will ultimately become the goal. In other words, if the incentives are aligned to reduce incidents, no matter how good your response and learning process is, it will only be executed in the most dire and unavoidable circumstances. After discovering this, the industry rapidly moved their focus towards expanding the use of the incident process by working to expand the spectrum of situations that could be classified as incidents with the goal of triggering the formal incident response process more often, distributing learnings for more incidents of lower relative severity and, yes, ultimately resetting the goal to increase the number of incidents.

    This approach effectively turned Goodhart’s Law to the airlines’ advantage—any effort to game the metric(s) meant more learning, not less. Filing incidents became directly and heavily incentivized and these metrics were never used as a justification for punitive action. This resulted in the safest decade of commercial aviation on record, with over 12 billion passengers traveling and no fatal accidents.

    What IT Can Learn About Successful Incident Response

    As technology companies, there is much for us to learn from this exercise in achieving resilience versus chasing “lower numbers.” The primary learning is this: increasing your incident count by incentivizing the use of your incident management process for more non-critical incidents—likely your SEV2s and below—and focusing your metrics and incentives on that goal, is likely the best way to reduce your overall count of catastrophic failures.

    For most companies, this means putting MTTR on the back burner and instead focusing on making the incident process itself more accessible throughout your company and across your organizations. It means leaders should talk about impactful and recent postmortems publicly and that postmortem distribution should be a priority; it should also be something that’s measured. Most importantly, it means opening up the incident management process more widely. Ideally, everyone at your company should have access to systems that allow them to file incidents, read incidents in progress and review postmortems from previous incidents.

    I like to reference the “big red button” philosophy of factory floors, where the decision-making power to declare an incident is in the hands of every employee. An unsafe situation shouldn’t be up for discussion, debate or approval before being acted on, even if the situation is later determined to be less severe than originally thought. The context of why that incident was filed—why that big red button was pressed—may prevent larger, more catastrophic failures in the future.

    So if you’re just getting started building out an incident management practice at your company, or you’ve been chasing MTTR metrics and focusing on pushing your incident count down as a primary goal, consider taking these learnings to heart and creating a positive incident culture with positive metrics meant to encourage and reward incident responders and reporters. Your company’s future depends on it.

  • Engineering Applications for DevOps (Part 6)

    Engineering Applications for DevOps (Part 6)

    This blog series explains how to engineer applications for DevOps.

    Topics covered in this blog series include:

    • Factors used to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covers five levels of application maturity. You can read part one here, part two here, part three here, part four here and part five here.

    Five Levels of Application Maturity

    Why is it that younger applications are more mature than older applications? DevOps works best with incremental changes rather than large complex changes that are time-consuming to change, build, test and deploy. DevOps also works best in ephemeral cloud computing infrastructures that can be scaled up and down quickly depending on short-term workload demands of supporting build, test and deployment requirements.

    While existing applications will eventually be modernized to take advantage of the cloud, greenfield applications should be designed to exploit recent advancements in cloud-based platforms and application delivery.

    The following paragraphs describe the characteristics of five levels of application maturity.

    Spaghetti applications are older, monolithic legacy application designs that tend to be large and complex, like a big messy bowl of spaghetti. This complexity occurs partly because these older apps accumulate a massive amount of code and patches over time and partly because the architectural approaches at the time they were conceived did not have modular, cloud-oriented requirements in mind. Spaghetti applications are the least capable of taking advantage of cloud infrastructures.

    Modular applications emphasize architecture designs that separate application components into functionally independent, interchangeable modules, such that each contains everything necessary to execute only one aspect of the desired functionality.

    A module interface expresses the elements that are provided and required by the module. The elements defined in the interface are detectable by other modules. Modules often use a design style known as service-oriented architecture (SOA), where services are self-contained black boxes that provide capabilities to the other application modules, as a service, through a network protocol. SOA may be confused with microservices architecture.

    There are key differences between SOA and microservices architecture that are important to DevOps. Microservices are preferred over SOA architecture because microservices are more fine-grained and restricted in what they can do. SOA uses an enterprise service bus for communication, whereas microservices use more lightweight stateless protocols and APIs managed as a product.

    Changes to SOA modules usually require a new application release, while a change to a microservice usually only requires the microservice to be replaced. Migration of modular applications to the cloud may require rework to take advantage of cloud features such as autoscaling.

    Cloud-ready applications that use the twelve-factor application modular design principles are more advanced. These modular designs emphasize declarative formats for automation, clean contracts with the underlying operating system and portability between execution environments. Deployment on modern cloud platforms obviates the need for servers and systems administration, minimizing divergence between development and production and enabling continuous deployment for maximum agility.

    These characteristics and capabilities enable the application to scale up without significant changes to tooling, architecture or development practices. Cloud-ready applications can be easily mapped to virtual machines (VMs) available through cloud infrastructure services (IaaS) for VMs based on VMware vSphere or Microsoft Hyper-V.

    Cloud-ready apps do not require rearchitecting or refactoring. They offer least-effort migration to the cloud with a lower availability rate. However, workloads such as legacy databases, third-party applications and homegrown business applications that are typically not designed for clustering or high availability suffer from a single point of failure.

    Cloud-ready apps are expensive to maintain. This is primarily due to the way they consume computing, storage and network resources, even when idle. Each component of an app runs in a dedicated VM with no scope for optimization. Upgrading or patching results in downtime and makes maintenance complex and expensive. Ultimately, cloud-ready apps derive less value from the cloud than cloud-optimized or cloud-native applications.

    Cloud-optimized applications take advantage of managed services offered by cloud providers. For example, instead of running a database in a VM, they consume database-as-a-service (DBaaS). Cloud-optimized apps typically target the platform-as-a-service (PaaS) delivery model of the cloud. They are portable and can be deployed in IaaS or PaaS. This reduces the deployment footprint, offering flexibility to IT teams. These apps are elastic, with the ability to easily scale in and scale out which results in optimal utilization of resources. Upgrading, patching and maintaining cloud-optimized apps is more manageable when compared to cloud-ready apps.

    The downside of cloud-optimized apps is the learning curve involved in migrating to the cloud. Developers, operators and system administrators will have to learn new ways of deploying and managing workloads. Line-of-business applications with access to source code are great candidates for being cloud-optimized apps. They can be redesigned and rearchitected to take advantage of the cloud. Cloud-optimized apps can integrate and interoperate with cloud-ready applications.

    Cloud-native applications are the most recent iteration of applications that are born in the cloud. They take full advantage of the cloud by exploiting capabilities such as elasticity, event-driven resource optimization and faster release cycles. Apps that are in the early stages of design and architecture are perfect candidates for cloud-native.

    Cloud-native applications are designed as federated microservices, packaged and deployed as containers and managed through modern DevOps processes. When your organization needs to scale to thousands of services in the cloud, microservices application architectures offer major benefits over monolithic modular architectures, including the following:

    • Separate microservices are better suited to different technology stacks.
    • Federated microservices enable independent deployment of different parts of the system.
    • Isolated deployments add to the resilience and fault-tolerance of the system.
    • Scaling characteristics may be different for each microservice.
    • APIs are used as connectors.
    • Stateless protocols help assure seamless recovery.
    • Event-driven programming reduces overhead and process latency.
    • Antifragile practices inoculate against unanticipated failure modes.

    The target deployment environment for cloud-native applications includes containers-as-a-service (CaaS) and functions-as-a-service (FaaS). This breed of apps delivers ultimate scalability and availability. Combined with continuous integration and continuous deployment, software is delivered rapidly, which accelerates time-to-market. It brings closed-loop feedback among developers, testers, operators and end users.

    Cloud-native apps demand new skills of containerization, microservices and build and release management. However, the ROI is higher when compared to other app models. Greenfield applications that are still in the design phase are ideal for adopting the cloud-native pattern.

    Diverse Applications for Cloud Migrations

    Large enterprises moving to the cloud will deal with a diverse set of applications. Through hybrid connectivity and integration, spaghetti, modular, cloud-ready, cloud-optimized and cloud-native applications will co-exist and interoperate with each other. If you are after the benefits of modularity, make sure you do not trick yourself into a microservices-only mindset.

    Explore the in-process modularity features or frameworks of your favorite technology stack. You will get support to enforce modular design instead of having to just rely on conventions to avoid spaghetti code. Then, make a deliberate choice whether you want to incur the complexity penalty of microservices. Sometimes you have no choice, but often, you can find a better way forward.

    What This Means

    This blog is one of a series that explained how to engineer applications to be most suitable for DevOps. Topics covered in this blog series include:

    • Factors used to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covers five levels of application maturity. You can read part one here, part two here, part three here, part four here and part five here.

    For more information regarding how to engineer applications for DevOps refer to my book Engineering DevOps.

  • Engineering Applications for DevOps (Part 5)

    Engineering Applications for DevOps (Part 5)

    This blog series explains how to engineer applications for DevOps.

    Topics covered in this blog series:

    • Factors to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covers DevOps applied to manufactured (embedded) systems software. You can read part one here, part two here, part three here and part four here.

    DevOps Applied to Manufactured (Embedded) Systems Software

    In the late 1970s (yes, forty years ago!) as a young engineer, I was given an assignment to figure out how to accelerate the software development and delivery cycles for an innovative packet switch called SL-10, (predecessor to the more recent model called Data Packet Node [DPN]) manufactured by Northern Telecom. Each SL-10 “node” consisted of millions of lines of original embedded software source code and custom hardware configured with many interconnected processors and protocols interfacing with other nodes, host devices, and terminal devices.

    Given the complexity of the system and lack of automated build, test and deployment tools, it was taking more than twelve months to generate each release of the system, and there typically were lots of bugs even after the release. Without knowing much about DevOps (the word “DevOps” was not invented for another twenty-nine years), we proceeded to implement a continuous delivery system that included automated build, test and delivery tools and workflows.

    The result was remarkable. Lead times for releases to manufacturing were reduced more than tenfold, from twelve months to one month, and quality, measured as defects found by customers per release, also reduced tenfold. This dramatic shift in performance and capabilities made it possible for the engineering and manufacturing teams to accelerate the rate of innovation and trample the competition. The SL-10 product, and its rebranded DPN version, quickly dominated the connection-oriented packet switch market, and it became the pride of the Nortel corporation and the Canadian nation. Even cooler, I got a nifty president’s pin and promotion which paved the way for my career that led to writing a book and this series of blogs! If this real-life example is not sufficient to demonstrate that DevOps can be applied to manufactured embedded software products, then I don’t know what is.

    There have been many other examples published that show how DevOps practices applied to embedded manufactured systems have reaped similar benefits. A notable example was published in 2013 by Gary Gruver in A Practical Approach to Large-Scale Agile Development: How HP Transformed LaserJet FutureSmart Firmware.

    That is not to say that DevOps applied to manufactured software embedded systems is the same as applying DevOps to enterprise applications or COTS systems. A paper published on the CloudBees blog, Five Aspects that Make Continuous Delivery for Embedded Different, explained some of the key DevOps practices used with embedded systems.

    DevOps Benefits for Embedded Systems

    Benefits of DevOps for embedded systems are summarized as follows:

    • Retrofitting the huge code base typically found in legacy embedded software systems into a DevOps model is not easy. It is likely to be expensive, but it is almost guaranteed to be a worthwhile effort, especially if you have a longer-term vision and intend for your product to stay competitive in the future.
    • Developers of embedded software need access to massive amounts of computer resources for testing their real-time embedded code on powerful hardware simulators.
    • Developers and testers need access to physical product hardware to run their binaries for system tests that cannot be run on simulators alone. Lab-as-a-service (LaaS) systems that orchestrate testing topologies of real devices can improve sharing of critical resources. LaaS systems provide a test framework that can be integrated through APIs into a continuous delivery pipeline.
    • Long build and test processes may need to be reengineered to reduce serialized lead times into quicker build times made possible by scaling build and test resources.
    • Long compliance and conformance test processes for embedded systems need to be accelerated with automation and resource scaling.
    • Focus on continuous delivery rather than continuous deployments in which shippable product versions are frequently delivered to manufacturing for verification, but the frequency of deployments to end customers is determined by other business factors.

    What This Means

    This blog is one of a series explaining how to engineer applications to be most suitable for DevOps. This blog series explains how to engineer applications for DevOps including:

    • Factors to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covers DevOps applied to manufactured (embedded) systems software. You can read part one here, part two here, part three here and part four here.

    For more information regarding how to engineer applications for DevOps refer to my book Engineering DevOps.

  • Engineering Applications for DevOps (Part 4)

    Engineering Applications for DevOps (Part 4)

    This blog series explains how to engineer applications for DevOps.

    Topics covered in this blog series:
    • Factors to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covers DevOps applied to commercial off-the-shelf (COTS) systems. You can read part one here, part two here and part three here.

    DevOps Applied to COTS Systems

    I often hear that DevOps does not apply to systems that use commercial off-the-shelf (COTS) software. The idea that DevOps cannot be used with COTS systems is, frankly, a cop-out. Just because someone else designed and developed an application’s source code is not a sufficient reason to avoid engineering a DevOps solution.

    Consider the value stream chain of activities from the source vendor through integration and deployment in your environment. There are limitations and constraints to the level of automation allowed, the return on investment in automation and influence over the development life cycle that DevOps practices can introduce when dealing with COTS applications.

    COTS applications will typically have predefined and supported interfaces for managing configuration or accessing data. These endpoints may not be accessible or compatible with your existing toolchain or automated change management practices.

    Types of COTS Applications

    COTS applications typically come in one of three flavors; namely, closed, open and platform, and each should be evaluated differently.

    Closed COTS applications allow little to no customization to their functionality and/or interfaces. They typically have predefined management consoles and a published list of commands or APIs that will allow external applications to interact with the COTS applications. A good example of a closed COTS application is Microsoft Exchange.

    Key considerations for DevOps with closed COTS applications are as follows:

    • Can configurations be scripted and integrated with existing automation and orchestration toolchains?
    • Can configuration scripts be managed and maintained in external version control systems?
    • How are updates, patches and service packs applied and managed? Can these practices be automated?
    • What developer tools (APIs, software development kits [SDKs], etc.) are available to extend and/or interact with the application and/or data?
    • Are these tools compatible with existing tools and expertise in the enterprise?
    • Can these developer tools be integrated within the existing toolchain?
    • How frequently are changes made to the base product?

    If these applications can be managed by external systems and code developed to automate deployment and configuration and/or integration can be managed through a version-controlled pipeline, closed COTS applications make good candidates for DevOps and continuous delivery (CD). By applying CD principles, COTS binaries are versioned and stored in an artifact repository; installation and configuration scripts are managed through version control and IaaS workflows and applications and integration endpoints are auto-tested through deployment pipelines.

    This is only made possible when consumers of the COTS application collaborate with operators of the COTS platform to ensure that the solution is aligned with enterprise goals, objectives and standards related to portfolio management.

    Open COTS applications allow significant modifications to functionality, data and/or interfaces. These platforms typically have rich SDKs, APIs and embedded developer utilities that enable users and developers to modify all layers of the applications; namely presentation, business logic and data. Open COTS applications typically have a large, complex footprint consisting of core services, data and/or GUI customizations, embedded applications, etc. Classic examples of open COTS solutions include ERP/ CRM platforms (like SAP or Oracle) or portal platforms (like SharePoint, ServiceNow or WebSphere).

    Key questions to ask when considering whether DevOps can be applied to an open COTS application:
    • How are customizations (created through internal design editors, logic builders or data schema extensions) version controlled? Are these configurations stored in a database or code?
    • How are customizations created through internal tools and packaged, installed and configured in higher-level environments like staging or production?
    • What does the development life cycle for customization created with internal tools look like? What quality gates are required? How about architectural standards?
    • How are custom, embedded applications/applets packaged, installed and configured?
    • Can these custom applications/applets be run and tested separately from the underlying COTS application?
    • What does the development life cycle for customizations created with external tools look like? What quality gates are required? How about architectural standards?
    • Does the COTS application provide testing frameworks, mocks and/or stubs for testing external dependencies?
    • What automated test suites are supported by the platform (unit, functional, regression, capacity tests, etc.)?
    • What is the upgrade path for customizations (historically)? As you can imagine, open COTS applications add layers of complexity.

    When considering DevOps and CD with open COTS solutions, it is recommended that you build multiple pipelines to support each layer of the application in the dev or continuous integration (CI) stage of the value stream. Subsequent stages, like staging and production, will use a converged pipeline built from known good artifacts of the dev and CI stages.

    Before implementing a solution, it is important to study the development life cycle and practices of the target COTS application before building your toolchain. Understanding how the end-state platform is created and how change is introduced will strongly influence your toolchain and pipeline design.

    Platform COTS Applications provide a set of services and tools, as well as proprietary runtimes, that enable users to build and run custom applications on the platform. These are the most difficult type of applications to integrate into existing DevOps practices and CD toolchains because common practices like version control, testing and build are done through the platform rather than using external tools.

    Platform COTS are packaged applications that enable customers to build applications on top of the base platform. As a blend of closed and open systems, all the previous considerations are valid. In most cases, the base platform acts as a closed system, whereas the custom-developed applications on top act like open systems.

    For this type of application, it is critical to understand what hooks and services are available to support DevOps practices and CD. Many of these platforms are not going to fit within your existing toolchain and may not be able to employ the workflows, dashboards and reporting you have grown accustomed to from your current toolchain.

    What This Means

    This blog one of a series that explains how to engineer applications to be most suitable for DevOps. This blog series explains how to engineer applications for DevOps including:

    • Factors used to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covered DevOps applied to COTS systems. You can read part one here, part two here and part three here.

    For more information regarding how to engineer applications for DevOps refer to my book Engineering DevOps.

  • Rocky Linux Emerges as a CentOS Replacement

    Rocky Linux Emerges as a CentOS Replacement

    Rocky Linux was created to offer the community an alternative following Red Hat’s recent decision to shift its focus away from CentOS—the open source version of Red Hat Enterprise Linux (RHEL). However, the Linux distro’s popularly can also be attributed to how its creator sought to offer a superior alternative based on lessons learned during CentOS’ development—and less as a perceived backlash to Red Hat’s controversial move, said Gregory Kurtzer, Rocky Linux’s creator, who is also founder and CEO of Ctrl IQ.

    “As one can probably imagine, there were a lot of lessons learned from the founding of CentOS—we did a lot of things correctly, but we made some mistakes, as well, that set into motion some of the difficulties the project went through,” said Kurtzer. “I was not—nor am I now—upset at Red Hat or anyone else. It’s important to understand that companies have a legal responsibility to do what they need to do. But I will say, this is why open source communities should not be controlled by a single company, especially if that company has a competing product to the open source project.”

    In addition to applying experience gained from the CentOS project to help “recreate the initial goals of CentOS in a new project,” Kurtzer said he was prompted to create Rocky Linux following Red Hat’s decision to end-of-life (EOL) CentOS. The move “affected my company, our customers, as well as the entire industry,” said Kurtzer.

    “To be blunt, my knowledge—in terms of doing it myself is severely dated. Luckily, after I made the announcement that I was considering recreating CentOS, thousands of people reached out to me wanting to be part of the project and contribute,” said Kurtzer. “From that, our development team and open source community was born.”

    Additionally, CentOS Stream offers value to the community by allowing “us and others” to take part in developing the upstream Enterprise Linux standard, said Kurtzer, who added that his team will continue to debug Rocky Linux so that solutions are properly integrated into the Enterprise Linux Standard.

    “The necessity was absolutely clear, so I founded Rocky Linux and the Rocky Enterprise Software Foundation (RESF),” Kurtzer explained. “Together with our contributors, sponsors and partners, we all have united for a common goal: to create an open source community that will provide us all with an enterprise operating system to stand the test of time, attrition and commercial agendas.”

    Interest in the development of a CentOS alternative was strong when Kurtzer first floated the idea of the project to the community. Since its release in June, the total number of ISO downloads is 56,000, according to Rocky Linux Tier0 mirror data.

    “We have zero visibility as to any other ISO downloads from any of our other mirrors, but if I were to guess, I’d say it would be about two to three times more, in total,” said Kurtzer. “When we all get a moment to breathe, it will be fun to see how many people are using Rocky Linux, and take in all the feedback the community has provided,” he said.

    With this team of developers,  Kurtzer said it took about seven months to build the general availability  (GA) release. “We didn’t just jump right into building the operating system because the goal was to create an infrastructure where many contributors and developers can take part without putting the security of the product at risk. Building that infrastructure took about four months,” Kurtzer said. “Once the infrastructure was completed, we spent about two months building the first-release beta candidate and then spent the following month ensuring that we did everything correctly.”

     

  • Engineering Applications for DevOps (Part 3)

    Engineering Applications for DevOps (Part 3)

    This blog series explains how to engineer applications for DevOps.

    Topics covered in this blog series:

    • Factors used to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • DevOps applied to software services.
    • Five levels of application maturity.

    This blog covers the topic of DevOps applied to enterprise apps and software services; part three in the series. You can read part one here and part two here.

    DevOps Applied to Enterprise Apps

    An enterprise application assists an organization in solving business problems. Enterprise applications are typically designed to interface or integrate with other enterprise applications used within the organization and to be deployed across a variety of networks (internet, intranet and corporate networks) while meeting strict requirements for security and administration management.

    Proprietary enterprise applications are usually designed and deployed in-house by specialized IT development teams within the organization. However, an enterprise may outsource some or all the development of the application and bring it back in-house for deployment.

    The size and complexity of large enterprise applications, especially older monolithic-architected applications that are not designed in modules, are a challenge when it comes to implementing DevOps. Given the large scale and distributed nature of the people, process and technology components of enterprise applications, it is recommended that enterprises engage very experienced DevOps consultants to determine strategic requirements, roadmaps and plans for DevOps that fully consider all three dimensions (people, processes and technologies) and nine pillars of DevOps before embarking on a major DevOps transformation journey.

    Despite the complexity, with the right strategy and plan, DevOps can deliver enormous ROI for enterprise applications. Amazon and Adobe are just two of many examples of large corporations that have benefited enormously from applying DevOps across the enterprise.

    DevOps Applied to Software Services

    I often hear people ask, “Does DevOps apply to software services as well as it does products?” These are usually people who have only worked in product-oriented organizations, such as companies that manufacture things that have embedded software.

    Ironically, I often hear people ask the opposite question, “Does DevOps apply to products with embedded software as well as it does to services?” These are usually people who have only worked organizations in which software is consumed as a service over an intranet or internet for a price or for free. Examples of these organizations are IT departments (even the IT departments of manufacturers!), government agencies and enterprises such as banks, insurance companies, hospitals and financial trading companies.

    You already know the answer to these two questions if you read the earlier parts of this blog series. The answer to both is clearly ‘Yes!’ Cloud services are doubly interesting to DevOps because not only are cloud services used by DevOps as a preferred infrastructure, as described in the next chapter, but cloud services also use DevOps in the process of creating and deploying software for the implementation of cloud components.

    While we are on the topic of DevOps and services, I want to help clarify some confusion regarding whether “newer” DevOps renders “older” IT service management (ITSM) obsolete or less important. This confusion exists because ITSM has often been regarded as a gatekeeper run by Ops to prevent Dev from getting out of control. While some organizations may have, in fact, implemented ITSM that way, this is not the purpose of ITSM.

    “DevOps tends to focus on the software delivery life cycle and on building and delivering software,” said Jayne Groll, president of the ITSM Academy. For its part, ITSM tends to have a broader focus; it is really looking at all aspects of customer engagement, from fulfilling standard services to ongoing support and operations activities. So, ITSM is “almost like the left and right ends of DevOps.”

    Using ITSM With DevOps

    Here are some recommended engineering practices for using ITSM with DevOps:

    • Training to help DevOps professionals gain an understanding of ITSM, and vice versa, will help both teams find points of integration—not just in technology, but in process, as well.
    • Find ways to automate service change approvals implementation of known, good changes to allow for agility, iteration and innovation. At the same time, hold back high-risk, high-impact changes that might need more than just an individual contributor to sign off on before they make it in front of a customer.
    • Self-service requests, automation flows and IT orchestration are now the front door to IT service management actions. These can and should be happening in the background, automagically created, maintained and measured by our DevOps tools.
    • Embrace Agile service management to take Agile values and apply Scrum-like development practices to process design and improvement. Aim to make more frequent incremental changes rather than design and implement a total end-to-end process in the traditional way.

    What This Means

    This blog is part three of a series to explain how to engineer applications to be most suitable for DevOps. This blog series explains how to engineer applications for DevOps including:

    • Factors to decide whether an application is a good candidate for DevOps.
    • Practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services.
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity.

    This blog covered how DevOps can be applied to enterprise apps and software services. You can read part one here and part two here.

    For more information regarding how to engineer applications for DevOps refer to my book Engineering DevOps.

  • Aqua Security Acquires tfsec to Advance DevSecOps

    Aqua Security Acquires tfsec to Advance DevSecOps

    Aqua Security today announced it has acquired tfsec, an open source project that provides a static analysis scanner for infrastructure-as-code (IaC) that is designed to be integrated within a DevOps workflow.

    Amer Jerbi, Aqua Security CTO, said tfsec extends the security platform the company already provides in a way that advances adoption of DevSecOps best practices. In addition, to prevent developers from misconfiguring cloud infrastructure using tools such as Terraform, the scanner surfaces examples of code that provide developers with insights into how to properly configure infrastructure.

    Jerbi noted that tfsec is already integrated with Aqua Trivy, an open source tool that scans container images for vulnerabilities. Integrations with the rest of the Aqua Security portfolio will follow, added Jerbi.

    Tfsec co-founders Liam Galvin and Owen Rumney will join Aqua Security as cloud engineers. The challenge Aqua Security is trying to address is the need to enable organizations to proactively prevent cybersecurity issues from arising in production environments rather than only identifying vulnerabilities that need to be fixed, said Jerbi.

    Aqua Security tfsec

    The decision to acquire tfsec comes in the wake of a series of high-profile breaches when many organizations are scrambling to make sure software supply chains are secure. Cloud platforms are rife with misconfigurations mainly because developers often lack the expertise needed to ensure infrastructure is secure after they have provisioned it. Organizations are embracing DevSecOps best practices in the hopes of educating developers to assume more responsibility for application security. However, developers are not going to acquire that expertise overnight. The tfsec scanner provides a way to enforce security policies today while at the same time affording the time to teach developers how to avoid misconfigurations.

    It’s unclear how many cybersecurity events arise because of cloud misconfiguration issues. The cloud service providers have adopted a shared responsibility model that makes the organization employing the cloud responsible for both the applications deployed on those clouds and for how that infrastructure is provisioned. Many developers that provision cloud infrastructure themselves within the context of a DevOps workflow often assume the cloud service provider is doing more than it is to prevent misconfigurations. Cybercriminals, meanwhile, have become very adept at scanning for those misconfigurations.

    There are, of course, a lot of layers and nuances when it comes to DevSecOps. Arguably, Jerbi said, the most critical thing for most organizations is to simply get started. Tools that can be quickly and easily integrated within an existing DevOps workflow with minimal disruption are critical, Jerbi added.

    In the meantime, DevOps teams should expect cybersecurity teams to become more involved in their workflows, especially as government mandates aimed at securing software supply chains become stricter. Eventually, as organizations gain confidence in the ability of their DevOps teams to secure their application environments, those cybersecurity teams will refocus their efforts on hunting for threats. In the meantime, DevOps teams would be well-advised to make those cybersecurity professionals feel as welcome as possible, given the application security mandate most of them have been given.

  • Engineering Applications for DevOps (Part 1)

    Engineering Applications for DevOps (Part 1)

    This blog series explains how to engineer applications for DevOps.

    Topics covered in this blog series:
    • Factors to consider when deciding whether an application is a good candidate for DevOps.
    • Best practices for engineering designs for DevOps
    • DevOps applied to enterprise apps and software services
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity

    This blog covers factors to consider when deciding whether an application is a good candidate for DevOps.

    Factors to Consider for Engineering Applications

    The extreme worldwide hype around DevOps products and services seems to suggest that DevOps is good for everybody and applies equally well to every software-based product or service in every organization. Here is my experience with that assumption: It is not true!

    On the other hand, I have heard people say that DevOps does not apply to applications for which it absolutely does! Before you go a step further in your DevOps journey—or decide that your application does not qualify for DevOps—you should consider carefully how well your application is engineered to become a model for DevOps implementations.

    Best Practices for DevOps Determination

    The following are important factors you can use to decide whether an application is a good candidate for DevOps:
    • The application will benefit from faster lead times.
    • Leaders with responsibility for this application are open to collaboration and will be sponsors of change.
    • Team players that are associated with the application team (product owners, Dev, QA, Ops, infrastructure, security, PM) are open to collaboration and change.
    • The application is currently using or planning to use service-oriented, modular architectures or microservices.
    • There are at least ten people associated with the application including product owners, Dev, QA, Ops, infrastructure, security, PM. Smaller projects with smaller teams may not show sufficient impact or justify substantive DevOps investment.
    • The application is expected to undergo changes for more than a year. Less time would not likely yield return on the investment required for DevOps.
    • The application represents a good level of business impact and visibility but does not involve an extreme amount of risk for the business.
    • The application experiences frequent demands for changes from the business.
    • Not all tools used with the application need to be replaced to implement a DevOps toolchain. The costs of changing out all tools can negatively affect the ROI and business case for DevOps transformations.
    • Efforts to build, test or deploy releases of the application are substantial and could be reduced significantly by automation.

    It is not necessary for an application to perfectly fit all the factors, but the more closely it fits the more likely your application will benefit from using DevOps and will, therefore, be worth the cost to implement a well-engineered DevOps practice.

    DevOps can be used with all types of software applications and services. However, not all applications and services should be engineered to use DevOps. The factors that determine whether to engineer DevOps for an application have more to do with business factors than technology differences between different types of applications. Applications must be engineered in specific ways for DevOps to operate effectively.

    What This Means

    This blog is part one of a series explaining how to engineer applications to be most suitable for DevOps. This blog series explains how to engineer applications for DevOps including:

    • Factors to consider when deciding whether an application is a good candidate for DevOps.
    • Best practices to engineer designs for DevOps.
    • DevOps applied to enterprise apps and software services
    • DevOps applied to COTS systems.
    • DevOps applied to manufactured (embedded) systems software.
    • Five levels of application maturity

    This blog covers the first two topics: factors to decide whether an application is a good candidate for DevOps and best practices to use when engineering designs for DevOps.

    For more information on engineering applications for DevOps, refer to my book Engineering DevOps.