Category: DevOps Practice

  • DevOps Myths Debunked

    DevOps Myths Debunked

    Leaders need not be DevOps experts, but they need to distinguish DevOps myths and realities to lead their digital transformation projects.

    As I indicated in a recent article, Engineering Practices Can Overcome DevOps Challenges, leaders need to set an inspiring DevOps directional vision for the organization, and proactively stimulate and sponsor team activities toward goals. To be credible, communication from leaders need to distinguish between myth and reality. It is easy for leaders to get confused, because there are many popular myths surrounding DevOps. It’s important to remember that, in DevOps, definitions and engineering best practices for DevOps have not entirely been nailed down.

    As Doug Miller, senior director strategic partnerships, Perforce Software, said “Working in an industry that has yet to settle on an accepted ‘definition’ for DevOps makes each day a challenge. The ever-morphing landscape makes it difficult for technology vendors and consumers alike to ensure we’re all talking about the same thing in the same way.”

    Here are some of the most important myths that leaders need to be aware of.

    Debunking DevOps Myths

    Myth: “DevOps is a cultural movement.”

    The truth is that implementing DevOps requires a careful and balanced engineering approach that considers a wide array of people, process and technology aspects. In my article, The Time Has Come for an Engineering Approach to DevOps, I explained that while culture is a critical success factor, DevOps is much more than just culture. Furthermore, DevOps is not just a movement. In the larger context of software engineering history, the word “DevOps” is new, and perhaps it will be replaced by another word someday, but the underlying engineering principles and practices that constitute DevOps could apply to almost any engineering project throughout the ages.

    Myth: “DevOps requires continuous deployments.”

    The truth is that DevOps enables continuous delivery of release artifacts to a staging environment to be ready to deploy safely, but frequent deployments to production is not always a goal. There are valid customer use cases that do not desire frequent deployments to production. For example, customers may not want to receive a new version of a software platform product frequently, because each new version may be disruptive and risky for reasons beyond the scope of the product itself. The engineering benefits of delivering complete releases to staging without deployment to production are still considerable. Each release is produced and validated in a pre-production environment, and made ready for deployment on demand without disrupting customers that do not want to take a new release.

    Myth: “DevOps applies to any software product, service or application.”

    The truth is that DevOps engineering yields high ROI in many circumstances, but some cases do not warrant investment in the people, process, and technology changes needed for DevOps. For example, applications that rarely change do not warrant the cost of DevOps changes. Organizations that are heavily siloed will find it too frustrating and counterproductive to implement DevOps. The application of DevOps to some commercial off-the-shelf (COTS) products may not yield sufficient ROI, because business-to-business barriers may behave as impenetrable organizational silos. Applications that have few tools in their toolchains suitable for DevOps may find the transition to DevOps too expensive.

    Myth: “DevOps requires Agile.”

    The truth is that DevOps and Agile can be either complementary or competitive. The notions of working with small, incremental changes is common to both Agile and DevOps. However, organizations that embrace Agile are focused on optimization of the development end of the pipeline, and may struggle with DevOps, which emphasizes engineering of end-to-end process optimizations.

    Myth: “The scope of DevOps is the same as CI/CD.”

    The truth is that a well-engineered DevOps implementation encompasses continuous integration/continuous delivery (CI/CD), but also extend beyond integration and delivery into other practice areas such as planning, operations, governance, disaster recovery and other practices that must work together with CI/CD to accomplish business goals.

    Myth: “DevOps does not apply to platform products such as software code embedded in manufactured products.”

    The truth is that DevOps does apply to embedded code products, as well as web services, business applications and other types of products. It even applies to development of software and services for DevOps itself! In the case of platform products, typically, deployments are planned. They are not continuous, but, instead, continuous deliveries of candidate releases are delivered to a staging environment to make sure the changes implemented for the platform are fully developed and verified as the platform is built, instead of waiting for a big-bang release.

    Myth: “DevOps is an elimination of IT operations.”

    Jeremy Bosch, in his article, Top 9 Debunked DevOps Myths, “As per the NoOps concept, the IT sector will become so automated that it will not require any in-house team to manage the software. However, no matter how automated the services may become, there will always be a need for operations. Although there may be changes in operations, they will still hold significance in DevOps.”

    There are many persistent myths about DevOps, such as the examples in this article, that can confuse and misdirect uninformed leaders. Leaders that learn to distinguish between DevOps myths and realities are better able to communicate realistic goals to lead their digital transformation projects.

  • How to Build Actionable Logging Systems

    How to Build Actionable Logging Systems

    Logging is often considered an automatic process that doesn’t require much thought. We see logs, in general, from any system and take for granted that those logs have the information that’s needed. However, thinking through a logging strategy and architecture is extremely important for troubleshooting and performance efficiency. Regardless of how you log, the most important part of logging, after simply having logs in the first place, is having logs you can actually use. Actionable logs provide enough information, enough detail and enough history to ensure you can use the logs to get something done.

    Let’s examine an example system. This example system is an application, with a microservices architecture, running on a Kubernetes cluster that lives on top of an RHEL box. By default, you get logs from the operating system and Kubernetes. But to get logs for the different microservices, you have to program those logs into your application while building the codebase. So how do you set up useful, actionable logs successfully?

    Three Main Considerations

    Consider the following elements when you think about building actionable logs.

    Enough Information

    Your logs should provide enough information to answer ‘when,’ ‘who’ and ‘where’ for any event. As an example, let’s say you want access logs. If there was an access event for your system, the log line for that access event should tell you when that access occurred; this is known as the timestamp. The log line should also include information about who accessed the system. Often, this detail comes in the form of an IP address or other identifier, and the ‘who’ should include not just human interaction, but also actions by other systems. Always log every access! Finally, the log line should include the location from where the system was accessed, meaning which service or which part of the platform was accessed.

    Enough Details

    Your logs should provide enough details to tell you exactly what happened during any single event. Simply answering the who, when and where doesn’t actually tell you what happened, and a basic answer such as “This pod started,” doesn’t provide enough detail to differentiate a code change triggering a pod restart from a system crash triggering the orchestrator to restart pods.

    Enough History

    Finally, your logs should provide enough history to understand why something happened. Logs, overall, help you understand the context of the action that was taken. Writing log lines to a terminal somewhere without storing them is pointless. A single log line doesn’t provide you with enough context to understand the “why” for any action or event. How could you tell if an event was normal for a system without an easily accessible history?

    All three of these components are necessary to create a plan that will lead to actionable logs—logs you can actually use to understand what happened, why it happened, and how you can act to handle the situation. If you can answer who, what, where, when and why from your logs, you probably have actionable logs, and are well on your way to meeting or exceeding logging best practices.

    Next-Level Considerations

    Once you’ve thought through those higher-level considerations, you can think about the nitty-gritty of your logs themselves. When I think about the practicalities of logging, I think about these main considerations: log levels and data needs, format and structure and security and compliance

    Log Levels and Data Needs

    Using log levels properly ensures that you collect and store the data that you need, and only that data. Logging can get very noisy, very fast in modern environments. Log levels provide the ability to fine-tune your logs to store only what you need; this can help you find information quickly in an emergency, rather than digging through the haystack for the proverbial needle. In addition, someone is more likely to keep logging capabilities turned on when it gets noisy if they have the ability to reduce the noise and fine-tune the information, versus having only an on-off switch.

    Different programming languages use different combinations of log levels, but all of them have general categories of major, minor and debug logs. Major logs, as you might guess, should be on for all environments. Minor logs, generally, are turned on in environments for which you need to be able to understand the data flow without necessarily knowing the minute details. Finally, debug logs are generally turned on in dev or QA environments, and only when you need to know every detail – such as when you’re looking for issues that could turn into problems later on. You will need to ensure the necessary log levels are set in your code, as they aren’t necessarily automatic, but taking this extra step is worthwhile.

    Format and Structure

    You need to think about who or what is the primary audience of your logs. If you anticipate sifting through the logs yourself, you’ll likely not mind a text-based format. However, most modern logging uses machines to parse the logs and make the mounds of data more useful. In that case, you should use structured logging; JSON is the standard for structured logs due to the plethora of tools available for working with the standard.

    Security and Compliance

    Logging for security and compliance could be (and has been!) a full article, on its own. The short version, however, is that logging is a key pillar of a compliant system that is aware of, and actively monitoring, its security. If your stack doesn’t log everything to one or more append-only log files, stored in secure location(s) with a read-only archive stored in a separate location, then you’re at risk of an attacker coming in and modifying or deleting logs so you can’t track their actions across your system.

    Your logging processes shouldn’t store personally identifiable information (PII) in clear text or behind an easily broken cypher, either. It’s better to keep that PII in a secured and hashed database, with a unique identifier (UID) that correlates to the database, if you definitively need to keep that PII in your logs in some form, or strip it out if you don’t need it.

    Working Together

    All of these tips are fine and dandy, until you realize that you cannot get enough buy-in from others to ensure everyone follows the same best practices. It’s generally best to come to an agreement as to logging standards and practices across an organization. That will also help, as anyone working on a different system will start from the same baseline, and not have to understand another team’s standard, if they come in to help in an emergency. Ops teams working across many systems will appreciate the standards, as well. To get everyone to the table in the first place, start by appealing to each of their needs. Ask about people’s problems with logging and listen to responses. You likely will need to address assumptions folks make, misconceptions, complacency and resentment. The key is listening; you won’t get buy-in until everyone feels heard and validated.

    To get everyone on the same page, you should first identify, as an entire organization, the needs you have from your logs based on the components of actionable logs. Ensure everyone has a seat at the table and the ability to discuss and unearth all needs, so no one is tempted to go around the team’s decision. One team’s needs will not match another’s, so you need to ensure that all viewpoints are heard and accounted for. Then, organize related needs and set standards for those needs. Once you’ve agreed to your new standards, work together to update your systems. This will take time, just like it does with resolving any technical debt! Start with smaller systems, or less-critical systems, so you can monitor how the standards are surfacing in real life, and how well everyone’s needs are met. Adjust, if you need to, with everyone’s agreement. Needs and standards will change, just like your systems will, and they need to adapt to fulfill the needs of your teams. If you take the time to get everyone to the table and make sure everyone is on board, you’ll be better off.

    What You Need to Succeed

    Logging may seem like simple magic that ‘just works,’ but there’s a lot to think about before you can have a successful, useful system. You need actionable logs that have enough information, details and history to be useful. You need to think about the data you need for all of your various systems, how you’ll structure that data and how you’ll ensure you stay secure and compliant. Finally, you need to ensure your team is on board with standards so logging can be as useful as possible. If you do all of that, though, you’ll find logging becomes a great way to communicate across teams and organizations to ensure you deliver the best product possible.

  • Languages and DevOps: Web Development

    Languages and DevOps: Web Development

    Web development has changed a lot since DevOps first hit the scene. The languages used have shifted, where languages are used has shifted, the likely back-end hosting environment has changed – but DevOps has folded these issues in and just kept plugging along.

    We’re going to approach this from two angles – what languages you’re likely to use, and what DevOps offers. This is not a “Best Billion Web Development Languages for 2021!” article; there are plenty of those out there, and you are more than welcome to read them. This is more about using these languages in a DevOps environment, which has become standard for web apps more than any other type of development.

    We’ll start by observing that your web app is, more than likely, going to have at least three parts – HTML, CSS and JavaScript. This is not news to those of you doing it daily, but we’re just setting the bar, here. Other languages may be used to generate or populate any/all of the above, but, in the end, these three languages get sent to the browser, so will be part of the equation.

    Backends add an incredible amount of complexity to this equation. I’ve done Ruby backends that did most of their work in C++ and Java; I’ve done pure Java backends that spit out the required three components; I’ve done Node backends that had very little actual HTML/JavaScript source code, but generated a ton of both – it’s all there. And the hosting environments for backends have changed. When DevOps started, most hosts were virtual machines (VMs) or hardware, whereas now we’re mostly running cloud instances or containers. Those backends have to be configured and managed, too, along with all of the dependencies that your application requires. When you think about it, we really have created quite a messy environment.

    But DevOps has risen to this challenge.

    I’ve long been an Atlassian fan, and find them to be a great tool for managing the agile side of DevOps. Add in Ansible, for the operations side, and you have a good thing going. I recently looked into GitLab, and GitOps – through GitLab – offers a compelling alternative to Atlassian’s offerings; more so since Atlassian made some rather unpopular changes right about the time I was wrapping up my look at GitLab.

    The key is that Git holds all the code. By adding in the scripts to spin up those increasingly complex backends and secure the entire infrastructure, you get an “App in a (virtual) box.” No matter what DevOps toolchain you use, tying this in is simple, because it already works well with Git and associated tools.

    Developers tend to care more about the development side of the equation (imagine that!), so let’s talk a bit about what’s going on out there.

    PHP is hanging on solidly, with a bulk of applications and (perhaps more importantly) libraries to keep it in the running. PHP is one of the original “generate an HTML page” languages, and is slowly being displaced. It isn’t a bad choice for your web projects, but it isn’t the only – or at this point, the most popular – choice.

    Java is still somewhat popular, and while it offers good support for web development, it is … heavy? Is that the word? It’s something for this type of development, and has ceded ground to other languages when it comes to pure-play web development. The skills are transferrable to other targets, so it is a good broad-use language. Like the other serious players in this space, there are web-centric libraries and frameworks that make Java hum. I’m a fan of Spring, but the options are pretty broad.

    JavaScript wins this race today. It is used on both client and server, there are a ton of developers and DevOps people available who can be productive in JavaScript from day one, and it is increasingly used in other contexts – meaning skills could transfer to other projects. Like any language, there are trade-offs, but the portability aspect makes the trade-offs things that many are willing to work to overcome. Libraries and frameworks abound to make JS development faster and more dynamic. Personally, I like Node for server-side development, and Angular for clients that need more than straight HTML/CSS offers (probably because it’s the one I’ve used the most).

    Python is definitely a contender in this space, as it is in most others today. With Django (or whatever your framework of choice), it functions well for web development, and its popularity has given it a host of support libraries to get things done. Developers are easy to find, and skills are broadly applicable, as Python is used in most areas of Dev and Ops at this point.

    Windows aficionados are still using C#, and it does have good hooks into Microsoft DevOps tools, so any Microsoft shop should include it in evaluations. I’ve done C# on Azure for web apps, and for me it was … different, but not broken. In a Microsoft shop, C# will be familiar, and at this point, most developers are familiar with web clients, so merging the two is only a tiny bit of a learning curve, but not a major one.

    Ruby does seem to have ceded its spot to Python. It is unlikely you’ll find a steady flow of ruby developers to fill those positions, but it is a highly intuitive language that you can train Java, and even Python developers in, if necessary. Ruby is well-supported for web development; indeed, it feels more natural using Ruby for web work than command line tools (at least to me, YMMV). There are web frameworks to make Ruby more intuitive in web development, and jRuby offers deep integration with Java to make use of already existing Java classes.

    All in all, JavaScript should be the strong contender for a new project today, but any of these tools will serve the need. They are all well-supported, and DevOps tools start with integration to most of these languages. Do what’s best for your environment, and keep rocking it.

  • Game On: Secure Coding Virtual Summit 2021

    Game On: Secure Coding Virtual Summit 2021

    Agile organizations are looking for ways to win at security without slowing down their release cycles. That’s where DevSecOps comes in. The days of a hands-off security approach for developers are over. Organizations implementing DevSecOps to integrate security into their DevOps framework are able to release secure software faster. Developers test code for potential security vulnerabilities earlier in the pipeline to fix security issues in real time to avoid costly delays.

    At Secure Coding Virtual Summit 2021, industry-leading AppSec and DevSecOps practitioners, analysts and visionaries will come together to share their best pro tips and power ups to level up your code security. The agenda for the virtual event, taking place March 24 from 10 a.m. to 6 p.m. ET, features a mix of topics including AppSec, threat modeling, API security, container security, case studies and more.

    The most brilliant security luminaries from OWASP, Okta, Microsoft, Secure Code Warrior, WhiteSource and many other well-known IT companies, will discuss:

    • How to level up your secure coding strategies
    • How to run an AppSec program with open source projects
    • The best practices for securing your APIs, the attack surface of an API and common authorization problems
    • The concept of threat modeling, its objectives and its role in the SDLC
    • How to transform developers into security people
    • How easy (or not) it is to get a container or Kubernetes cluster hacked on purpose
    • How to create an AppSec pipeline with containers in a short period of time (demo)

    This year’s Secure Coding features outstanding industry leaders, such as:

    • Vandana Verma Sehgal, global board of directors at OWASP
    • Simone Curzi, cybersecurity principal consultant at Microsoft
    • Keith Casey, API problem solver at Okta
    • Matias Madou, CTO and co-founder at Secure Code Warrior
    • Matthew Coles, senior principal product security engineer at Dell
    • Sarah Young, security architect at Microsoft
    • Chris Romeo, CEO and co-founder at Security Journey
    • Dr. Phillipe de Ryck, web security expert and founder at Pragmatic Web Security
    • Richard Greenberg, founder and CEO at Security Advisors LLC
    • Izar Tarandach, principal security engineer at Squarespace
    • Jeroen Willemsen, principal security architect at Xebia
    • Shiri Ivtsan, director of product at WhiteSource
    • Justin Hutchings, director of product management at GitHub
    • Matt Tesauro, senior partner at 10 Security
    • Matthew Butler, principle engineer at Laurel Lye

    Attendees will have access to useful resources and will be able to interact with sponsors to power up their secure coding strategies. There will be live Q&A chats with keynote speakers to get your burning questions answered. Attendees will also have the opportunity to hunt for Easter eggs to boost their player score for a chance to win a PlayStation 5.

    To see the full agenda and to register, please visit the Secure Coding website.

  • Engineering Practices Can Overcome DevOps Challenges

    Engineering Practices Can Overcome DevOps Challenges

    When I think about the challenges of DevOps transformations, I love the viewpoint expressed by Ryan Harmon, strategic accounts manager at Trace3: “DevOps is a complex maze that has many leaders frustrated. In many large enterprises, ‘DevOps’ is simply a buzzword they are striving to achieve but struggle with what the end state looks like or even where to start. There are many layers across the organization that are instrumental to truly executing on DevOps. DevOps is not something you go get a quote for and simply buy. It’s an evolving journey.”

    In my previous article, The Time Has Come for an Engineering Approach to DevOps, I agree with Ryan.

    Too many organizations launch into a DevOps transformation initiative without considering the full scope of what it takes to engineer a successful DevOps solution. DevOps is more than “just” a CI/CD pipeline or toolchain, as complex and challenging as this alone may be to accomplish, for even one application. DevOps is more than simply a culture change, as difficult as that always is.

    There are 24 DevOps practice categories that need to be mastered to realize and evolve well-engineered, high-performance enterprise DevOps solutions. Not all 24 need to be mastered to begin the DevOps transformation, but, ultimately, all of them do need to be mastered before the solution will achieve the high performance levels to which many enterprises aspire. The good news is, successful enterprises have demonstrated that the investment in people and technologies required for the journey is worthwhile, and a strategic approach will get you there systematically if you follow a disciplined engineering implementation roadmap.

    As explained in my book, Engineering DevOps, the core of every successful DevOps practice includes nine core pillars. These are the pillars to focus on as you start on your DevOps journey. The following paragraphs illustrate requirements for each of the nine pillars.

    Leadership – Leaders set an inspiring DevOps directional vision for the organization, and proactively stimulate and sponsor team activities that help progress toward goals.
    Collaborative culture – The organization encourages cross-functional collaboration, shared responsibilities and works to reduce restrictive boundaries between organizational functions.
    Design for DevOps – Products are architected to support service-oriented, modular, independent packaging, testing and releases, in accordance with the principles of twelve-factor apps.
    Continuous integration – Changes to software code and artifacts needed for releases are frequently merged, built, tested and packaged into executable artifacts for application releases.
    Continuous testing – Software tests that are most important to verify the results of pipeline stages including pre-flight, integration, regression, performance and release acceptance tests are automated as much as possible.
    Elastic infrastructure – Infrastructure-as-a-service (IaaS) resources (i.e., software, computing machines, storage and networks) are integrated into a toolchain for automating builds and testing environments to efficiently support variable workload demands.
    Continuous monitoring – Applications, pipeline tools and infrastructure components are instrumented with metrics, and the collected data is analyzed to manage health and performance.
    Continuous security (DevSecOps) – Security practices, processes and technologies for applications, databases, pipelines and infrastructures are integrated into the culture and pipelines to assure security.
    Continuous delivery – Release artifacts are validated, prepared for deployment to production and may be automatically deployed to production once acceptable deployment measures are achieved.

    A Comprehensive Engineering Blueprint

    While the above nine practice categories are the core of every successful DevOps solution, 14 additional practice categories are essential parts of a comprehensive engineering blueprint for accomplishing high-performance DevOps.

    DevOps infrastructure practices are used continuously by the nine core practice categories as follows:

    Version management systems – Every stage in the end-to-end DevOps value stream depends on keeping track of versions of software source code, configuration data and executable images using source code management systems, artifact repositories and configuration management systems.

    Infrastructure-as-a-Service – Virtualized cloud and dedicated resources must be served up ephemerally to meet variable workload and storage demands, which requires mastering engineering practices for orchestrating dedicated and cloud, hybrid cloud and multicloud services, and deployment of applications to the infrastructure.

    DevOps pipeline practices, which constitute a layer just above the nine core practices, include two additional categories that make use of the nine core DevOps practices.

    Application release automation – Directing and monitoring software changes through the CI/CD pipeline requires process orchestration and data analysis working at a level above the CI/CD pipeline itself.

    Value stream management – A deployed service, composed of multiple application microservices and data services, requires orchestration and analysis of a federated set of services, end-to-end, across the value stream from planning through to operations.

    Management Practices

    DevOps management practices include four categories that make use of the DevOps Pipeline practices, as follows:

    Service catalogs – Organized collections of software stacks, tools and pre-configured pipelines, made available through portals, enable DevOps-as-a-Service operations and simplify access, configuration, control and evolution of DevOps resources needed by enterprise application teams.

    Disaster prevention and recovery – Proactive disaster monitoring coupled with high-availability configurations and disaster mitigation practices, such as automated processes, work together to minimize the blast radius of disastrous events, and orchestrate rapid restoration of essential DevOps services, when needed.

    Site reliability engineering (SRE) – A combination of practices which include service level objectives (SLOs), error budgets, monitoring, work-sharing policies, toil reduction, proactive anti-fragility practices and blameless post-mortems help to “shift left” production wisdom from operations into development, thus improving the efficiency, readiness, security and resiliency of applications and infrastructures in production.

    Governance – The scale and scope of enterprise DevOps solutions are controlled using governance policies-as-code implemented through service catalogs, management dashboards and error budget policies.

    Project Practices

    DevOps project practices include four categories that guide enterprise adoption and evolution of DevOps, as follows:

    Adoption – Enterprise-wide DevOps adoption strategies that engineer and build DevOps capabilities in logical phases. This begins with a small set of representative applications that serve as a model for other applications; this method has been shown to be more successful than “boil the ocean” strategies that attempt to transform all applications at the same time.

    Model applications – Applications – that can serve as a model for greater enterprise adoption – are selected and monitored according to criterion that spotlight the value of DevOps for other applications.

    Return on investment (ROI) – DevOps investments must compete with other IT projects for resources. An ROI model that demonstrates measurable cost savings is an approach that has proved to achieve greater numbers of DevOps project approvals compared to other methods.
    Continuous improvement – Competitive forces and industry trends require that DevOps solutions continue to evolve to keep up with enterprise requirements.

    The Human Element

    Human development practices include two categories, as follows:

    DevOps engineering – The development of DevOps engineer roles, skills and the structure of DevOps engineering within an organization is critical to the success of designing, building, maintaining and evolving DevOps solutions.

    Continuous learning – A DevOps continuous learning strategy is critical for the development and transfer of DevOps knowledge needed to develop and continuously improve DevOps for the enterprise.

    In summary, the importance of practice categories – and specific practices within each of the categories – will evolve as each application does, alongside the changing needs of the enterprise. DevOps is complex, but a comprehensive engineering strategy that considers the 24 DevOps practice categories described in this article can help enterprises achieve the high-performance DevOps solutions they need to justify the investment and to meet business goals.

  • 3 Common App Modernization Pitfalls to Avoid

    3 Common App Modernization Pitfalls to Avoid

    Application modernization has been a consistent theme for organizations year after year. Businesses today still rely on their legacy applications for the majority of their revenue generation, from mainframes to Java EE. New digital transformation initiatives and mandates have pushed application modernization back to the forefront, as a new generation of automation platforms and best practices are helping these organizations avoid the pitfalls of the past and accelerate their journey to cloud-native architectures.

    Here are the three most common application modernization pitfalls and how to avoid them.

    Lack of a Clear Organizational Destination

    Application modernization journeys need a north star – a destination point that development, infrastructure and DevOps teams can rally around. In order to establish a clear, unified direction, everyone must share a precise understanding of that destination. That journey starts by defining a set of business goals you are trying to accomplish (such as cost savings, business agility, resiliency and/or competitive advantage) that can guide and prioritize the technical decisions.

    Next, you need to decide which application modernization technologies, platforms, and infrastructure will help you deliver on those business goals (e.g., Red Hat OpenShift, Kubernetes, serverless, hybrid, private or public cloud, etc.). Setting common architectural standards will help avoid disparate future stacks and ensure a common shared platform for microservices architectures. For example, if an application starts with Java WebSphere, should the goal be to move to a modern container framework like Spring Boot, or a specific microservices Java framework like Quarkus? To avoid analysis paralysis and internal drag, set tangible objectives and targets that prescribe how teams are expected to prioritize, select and modernize these applications and how their progress should be measured, communicated and shared.

    Taking Shortcuts – Don’t Fall For the Old ‘Lift and Shift’

    The more one modernizes, the more value one gets from the cloud – but not all modernization efforts are created equal. For example, ‘lift and shift,’ putting a monolith into a container and application refactoring into microservices are all called “modernization.” But only refactoring allows enterprises to realize the true benefits of the cloud, including elasticity, regained engineering velocity and cost savings. Don’t fall into the quick-and-easy modernization temptation with lift and shift or brute force containerization. You will still have a monolith, in the end; you’ll simply end up paying more and trading one set of monolithic problems for another.

    Not Bringing Everyone Along – No Developer Left Behind

    Modernization is never just about technology. Modernization requires that your team’s processes (for example, agile practices), structure (vertical domain teams as opposed to horizontal teams – UI team, for instance) and skills must all adapt. The organizational culture needs to grow to adopt DevOps and cloud-native best practices to take advantage of the deep development, build, deployment and operational benefits that are available. Take the time to train and educate so everyone is brought along. Modernizing legacy applications can introduce a whole new set of technologists, responsible for those applications and the supporting infrastructure, into the cloud-native world. This is a win-win for everyone.

    In other words, the organization needs to have crystal clear answers to the where (destination), why (path) and how (team) to ensure success and avoid the three most common pitfalls of application modernization.

  • Languages and DevOps: Embedded

    Languages and DevOps: Embedded

    Every once in a while, it is a good idea to take stock of the environment and the market around us to determine if what we are doing is what we should be doing. I’m a developer, not a prognosticator, so these observations are simply based on my experience in the development world, along with a frank assessment of what’s changing, to try and offer some advice to my fellow developers. This week I’ll talk about embedded, but we’ll hit a bunch of application targets over the coming weeks.

    Embedded development is, generally speaking, more low-level than most other modern programming practices. Considering the goal of embedded is to interface with the hardware, this is no surprise.

    C/C++

    And that brings us to the king(s) of the embedded hill. C, and to a lesser extent, C++ rule this roost. You will see a lot of talk about Python, and we’ll get to that in a minute, but for historical and performance reasons, C is still your best bet. If your DevOps tools support your Make variant – Ant, Maven, or whatever – it can be plugged directly into the DevOps process. Testing will still take extra steps. Since deployment is most often handled directly, that does not generally need extra steps, but, in some cases, it might. A lot of general guides claim C/C++ is “easy” to learn. I disagree with this assessment; newer languages tend to be more intuitive while retaining some of C’s better features, but it is, at this time, necessary to embedded development.

    There’s a Reason (or Many) for That …

    There are a lot of conflicting factors going into where embedded is headed in the future, so we’ll try to address them here.

    Historically speaking, C/C++ have had better performance and smaller footprints than interpreted (even bytecode) languages. They are easier to code in than Assembly and supported by a ton of libraries to get work done; they struck the right balance for embedded. The weight of these developers and the legacy source code they created are part of the reason C/C++ are still on top.

    But three things have caused a shift that will open up room for other languages to make headway into this space. Embedded devices are getting faster, and storage is getting both smaller and cheaper. That means that a less-efficient means is more viable. Arduino and other hobbyist embedded platforms have also come of age. It is now viable to pick up an embedded board and start prototyping over the weekend. These embedded environments are well-supported by newer languages, and, historically speaking, today’s hobby builders are tomorrow’s startups. This is pulling other languages into the mix far more frequently, particularly our next entry.

    Python

    Python is popular in almost every category of programming. It has been around long enough to lose the mantle of “language du jour,” and should be considered based on its strengths and weaknesses. Given that the newer wave of prototype systems is well-supported by Python, it is easy to maintain, and has a broad selection of libraries (often, ironically, written in C), it has gained clout in the embedded space. It doesn’t perform as well as C/C++, but modern embedded devices are of a class that this may not be as important (early embedded devices were high-performance tools, while IoT often has devices that don’t require anything like near-real-time), and hardware improvements make this less of a concern. Python’s popularity makes it relatively easy to work in to your DevOps toolchain, with targeting of embedded devices making deploy and test require extra steps … but that’s true for all of these languages, as it is a targeting feature, not a language issue.

    Assembler/Assembly

    In the end, we need machine code. Even bytecode (or p-code, if you prefer) interpreted languages execute their instructions in hardware, eventually. Because performance and space have long been issues in embedded development, assembly language has been a preferred tool for pieces of the development pie. While few developers use assembly language for everything, most embedded developers use it, at some point, to modify libraries or create targeted subroutines. Certainly not a “popular” language among developers, most of us buckle down and just do what we need to. Like C, assembly is normally built using a Make variant. These are easily integrated into DevOps toolchains to get reporting and results up to those dashboards that run a company’s agile efforts.

    Others

    There are a wealth of other languages in use on embedded platforms, most notably C#, but also newer languages like Rust. The environment is changing, and as the ramifications of terabyte SD cards and multi-core embedded boards start to sink in, they are worth watching. We are not that far off from having a desktop-quality computer in a board only a little larger than a credit card. That changes a lot of things. More room for data storage and processing, and the need for tools that are easier to use in these contexts.

    Libraries

    All languages use libraries to make development easier. We are past the days when coders wrote all of the functionality of their project. The tools that bring libraries into a project need to be incorporated into the DevOps toolchain. For C/C++, the mechanism is largely manual, and can be automated with scripts or with the build tool. For newer languages, tools like NPM will need to be hooked into the toolchain. The ease of library dependency management is one of the appealing aspects of newer languages, and making it easier for a developer to hook in libraries also makes it easier for DevOps tools to make use of library management functionality.

    You. Rock.

    Whatever your language of choice, in the end it will likely be your build tool that links it to DevOps. Maybe we should do a series on those, too; comparing when to use Gradle versus Ant, for example. Until then, keep rocking it. All development depends upon embedded in the end, so you’re holding up the foundation.

  • Survey: The State of Database DevOps

    Survey: The State of Database DevOps

    Enterprises are undergoing rapid changes. Throughout the past year, companies have been pressed to learn a diversified set of technologies and methods to remain agile in a changing economy. In this digital-first world, data plays a heightened role; in tandem, so does the delivery of data and the persistence of the database layer itself.

    Applying DevOps principles and automation throughout the software life cycle is critical to maintaining a high level of efficiency. As with application development, databases are not immune to the DevOps takeover. Database DevOps is a discipline that considers the processes used to automate database changes and deployments — a field growing in mindshare as databases diversify.

    Seventy percent of companies now use more than one database in their stack. Many organizations now use a blend of on-premises and cloud databases. Styles are diverging, too, as non-relational databases like NoSQL gain favor, and new open-source databases emerge to fit niche circumstances. All of this means database operators may require an increased emphasis on database DevOps to handle additional complexity.

    The 2021 State of Database DevOps report, conducted by Redgate, surveyed 3,200 database professionals on the general state of their DevOps adoption. The survey also inquired about database change automation and deployment and underlying database technologies. Now, five years in, the study continues to reveal shifts in practices across industries.

    Below, we’ll review the report’s main takeaways to see how companies implement database DevOps in practice.

    DevOps Adoption Increases Across Projects

    To meet pressing digital innovation needs, DevOps has surged in importance across all industries — 74% of organizations have adopted some form of DevOps, and 26% of respondents reported applying DevOps practices across all projects — a significant uptick from previous years. The report acknowledges a slight increase in DevOps adoption among larger company sizes.

    So, which DevOps practices are companies employing the most? Well, in terms of DevOps for application development, the majority of organizations (50% or above) have adopted version control, automated builds, monitoring, automated unit tests, static code analysis and semi-automated deployments in production. Only 41% of organizations surveyed have fully automated deployments to production. These methods can be viewed as a basic benchmark for modern DevOps.

    The study demonstrated that in most organizations, DevOps is a communal undertaking. More respondents reported adopting a community of practice, rather than relying on individual decision makers, to disseminate DevOps strategies and direct planning. According to the report, a community of practice is a group of people that “makes decisions, provides recommendations, or is consulted.”

    Lack of Skills Tops DevOps Obstacles

    The pressure is on engineers to produce to meet digital transformation demands. However, organizations experiencing a developer drought may not possess the talent to introduce DevOps practices. It’s no surprise that the common obstacle hindering DevOps adoption was a “lack of appropriate skills in the team.”

    Other cultural barriers to DevOps adoption include disruption of existing workflows and a lack of alignment between teams. Interestingly, a lack of executive support was cited as the lowest hurdle to DevOps adoption, indicating universal consensus on DevOps goals. If we consider what technical barriers prevent improved software delivery and operations, 41% noted insufficient automation in release processes and 39% expressed hindrances due to legacy code.

    Continuous Delivery For Databases Increases

    Database DevOps appears to be growing in production use too. For the purpose of this study, Database DevOps is defined as “automating the delivery of database changes.” According to the report, 51% of respondents are already automating some parts of their database deployment process.

    Though growing year after year, database DevOps is nowhere near ubiquitous. Compared to DevOps in application development, database DevOps has lower overall use. For example, only 36% are implementing automated builds. And, only 22% adopt continuous delivery (CD) for all databases. Nevertheless, this could quickly change, though, as 80% of respondents are working to implement CD for database changes or have already implemented it.

    Setting up a CD process for database changes could increase overall software delivery speed. The top two drivers for adopting database automation are increasing the speed of database changes and freeing up time for other work. The third top reason, minimizing application downtime, rose in rank this year — which makes sense, as the desire to create more reliable systems is an ever-present concern.

    However, to reap these rewards, organizations must overcome some significant challenges, like synchronizing application and database changes and maintaining high speeds for database automation, among other concerns.

    Organizations Adopt Hybridized Database Approaches

    Companies increasingly use a mix of different database technologies and styles. We also see an increase in cloud and hybrid database estates.

    In terms of database styles, relational databases still make up the majority of market share — 98% of respondents have at least one relational database. However, non-relational databases, like NoSQL, are gaining popularity. Forty-seven percent of database professionals have a NoSQL database in their enterprise. Interestingly, only 2% use non-relational databases exclusively, indicating it’s a niche use case.

    Enterprises are now using more databases than ever before. Seventy percent of database professionals reported having more than one database in their stack. This figure rose nearly 10% since last year. Database types are multiplying too — 45% have more than one type of database. In terms of database management systems, Microsoft SQL Server holds the most use, at an 82% usage rate. Others like MySQL, Oracle, PostgreSQL, and MongoDB trail behind.

    Enterprises are also increasing their use of cloud and hybrid database deployment models. Eighty percent of respondents host a cloud database, and 65% report a mix of cloud and on-premises estate.

    One possible explanation for the diversified database effect is an increase in public cloud provider usage, combined with the rise of multicloud strategies. Database options are increasing as well — many free, open-source databases can cater to unique project needs.

    This “polygot persistence” could also simply be a natural occurrence within large enterprises supporting many application nuances. As Martin Fowler describes, “… any decent sized enterprise will have a variety of different data storage technologies for different kinds of data.”

    Quality Database Ops = Quality Software Performance

    In summary, non-relational databases are gaining steam, cloud and hybrid database models are becoming more common, and large organizations are increasingly using multiple database types. The landscape is forecasted to diversify further, meaning database DevOps is stepping up to manage this complexity.

    The report indicates a correlation between quality database Ops and overall software quality, noting that organizations that release more frequently typically deploy database changes in much less time. To achieve these goals, “… the next area for database DevOps is in the data engineering and analytics space,” writes Pramod Sadalage, director, data & DevOps, ThoughtWorks.

    While it’s important to take a vendor-endorsed study with a grain of salt, it’s safe to say we will likely see more interest in database DevOps, especially as DevOps themes continue to permeate through the entire spectrum of software development and delivery.

    For more information, you can download The State of Database DevOps report behind an email gate here.

  • Protecting Data from Insider Threats

    Protecting Data from Insider Threats

    “Insider threat” can be defined as the threat an employee or a contractor will use their authorized access to, wittingly or unwittingly, do harm to an organization’s security. Certainly, as organizations become data-driven, embrace data democratization and release new applications at breakneck speed, the probability of an insider threat is rising sharply.

    A common example of an insider threat is granting permissions to on-call SRE engineers who may need to access system tables across multiple databases, inspect contents of S3 buckets and make changes to the production environment to fix availability issues. Every two weeks, the composition of the team may change, and the composition of services and databases may vary dramatically.

    While it is impossible to completely safeguard against insider threats, organizations can better protect their data by investing in best practices that (a) reduce the likelihood of such a threat playing out, (b) limit the blast radius from an insider attack and (c) help detect the attack as quickly as possible. The third point is especially important – the Ponemon Institute’s recent Cost of Insider Threats 2020 Global Report claims that it takes, on average, 77 days to detect and contain an insider attack.

    A DevOps Framework for Protecting Data From Insider Threats

    For security measures to be effective, they must be non-invasive, especially in agile organizations where DevOps teams are expected to push releases several times a day.

    Implementation of Access Policies-as-Code

    Implementing access policies-as-code automates workflows for user and service access authorization. Using this approach, manual policy practices are replaced with “code” (policy files readable by your team and by the systems that enforce the policies) so that the right people on your team can request, approve and publish policy changes instantly.

    As an example, here’s how the above scenario may be implemented as code:

    Engineers can add themselves to on-call status with a code push in GitHub to a Terraform script, with only the team lead authorized to approve this action. (See example 1, below.)

    insider threats
    Example 1

    Using frameworks like Sentinel, changes to production environments can be limited to only on-call engineers. (See example 2, below.)

    insider threats

    Example 2

    Similar policies can limit only those engineers who are able to read unencrypted content of S3 buckets by fetching keys from Vault. (See example 3, below.)

    insider threats

    Example 3

    The above examples demonstrate guardrails that allow engineers to perform their duties, while drastically reducing the administrative burden on leads, resulting in fewer errors. An additional benefit of this approach is that it forces a public review of all changes to policies, which makes the security model more robust.

    Zero Standing Permissions

    Zero Standing Permissions (ZSP) is a DevOps-friendly security tenet that requires any administrative privilege to be available to users only when needed, and only for as long as needed. It enables teams to configure access, via administrative privileges, to highly critical components of their infrastructure for a limited duration, with restrictions on the scope of those privileges.

    Here are a few examples of how teams can implement ZSP for their databases:

    • Create an SRE role on the database that can update and delete database tables and a private Slack channel of db-admins that can allow on-call engineers to assume this role.
    • Implement a service that can be used to request and grant access via this SRE role for a default of 60 minutes. After that time, the access is automatically revoked and needs to be granted again.
    • Further implement policies in BrewOPA that restrict an SRE from dropping tables and databases, which avoids any disasters.

    Audit Trail for Privileged Activity

    SIEMs are increasingly the nerve center of every security organization. While effective threat-hunting and incident response requires several years of expertise, DevOps teams can significantly bolster the security of their cloud platforms by investing in log collection and basic analytics for all privileged activity in their environment.

    These tools include services such as the following:

    • Session recording for all Linux boxes using Cmd
    • Logging activity across databases with Data Cloud services
    • Centralizing and collecting all environment logs using services like CloudTrail.

    These logs can be visualized in tools like Kibana, where teams can look at activity pattern anomalies. These could include:

    • Someone opening up an FTP connection to outside the organization
    • Creating a manual copy of tables outside of the database
    • An increased number of authentication failures across resources

    While the concept of “least privilege” as a safeguard against insider threats to data has existed for some time, the advent of cloud-native and DevOps methodology has exacerbated the challenge of implementation. In recent years, security-as-code and infrastructure-as-code have emerged as good models for implementing security solutions, and are rooted in the above framework. The future encourages collaboration across various stakeholders, documents security policies for easy review and builds on existing tools and workflows. It will help limit insider threats, limit privilege sprawl and prevent “user fatigue.”

  • Amex, Adobe Join GitHub Intern Program

    Amex, Adobe Join GitHub Intern Program

    GitHub revealed today that Adobe and American Express have joined its Summer 2021 Fellowship program, a 12-week, hands-on curriculum where interns work alongside developers to contribute to open source projects hosted on the GitHub repository.

    Vanessa Gennarelli, senior director of education at GitHub, said the overall goal of the program is increase the pool of developer talent with experience working remotely on an open source project that requires a fair amount of collaboration with other developers.

    The program was developed in collaboration with Major League Hacking (MLH), which organizes hackathons for students. Each intern participates in a series of short hackathon sprints involving multiple open source projects over the 12-week period. GitHub originally created the fellowship program as a way to address the demand for online internships during the COVID-19 pandemic.

    Those internships have provided open source maintainers with access to a pool of talent that can reside anywhere versus, for example, always trying to recruit individuals located in a specific geographic area, Gennarelli said. GitHub is also trying to increase diversity within the open source community, Gennarelli added.

    Three quarters of open source maintainers (76%) that have worked with MLH interns reported their open source project was in a better state after working with MLH Fellows. Of eligible fellows who were matched for jobs with an MLH Fellowship partner company, more than 20% ended up receiving full-time offers at that company.

    Mike Swift, MLH CEO, said the internships provide a way to partially close a gap in developer expertise that only continues to widen. Organizations are especially keen to find developers that have open source expertise, Swift added.

    A survey of 3,400 developers and technology managers conducted O’Reilly Media on behalf of IBM finds 65% of respondents preferred skills related to the underlying open source technologies. The same percentage agreed that contributions to open source projects impress potential employers and result in better professional opportunities. In many cases, open source projects are among some of the most innovative development initiatives in the IT industry, Swift noted.

    Of course, as open source becomes a primary driver for building software, more contributors are looking to get paid for their efforts. Much of that compensation comes in the form of organizations that hire developers for the express purpose of contributing to open source projects that they deem critical to their operations.

    Regardless of how developers are compensated for their time and effort, it is apparent open source projects are the fountainhead from which many opportunities spring. Future success in any endeavor has as much to do with who someone knows as it does actual talent. The best place for any developer to expand their base of industry contact is to collaborate on an open source project. The more projects they contribute to, the greater the reach of their personal network and, by extension, their net worth, will become.