Category: DevOps Practice

  • Overcoming the Awkward Teen Years of DevOps Adoption with Process-Centric IT

    Overcoming the Awkward Teen Years of DevOps Adoption with Process-Centric IT

    DevOps adoption is all over the map. Just last year, xMatters and Atlassian surveyed more than 1,000 companies on their DevOps maturity and found only 65 percent were getting the benefits from their DevOps initiative that they expected. Sixty-six percent described their maturity level at or below intermediate. And another 1,000 organizations were excluded from the survey because they didn’t have a DevOps initiative at all!

    With so much misalignment and insecurity, you might say that organizations are going through their awkward teen years. While many businesses recognize the importance of DevOps and agile methodologies, most are still struggling to truly embrace them, and therefore reap the real benefits. So what’s holding us up? The answer is people issues. Gartner recently surveyed organizations about their biggest barriers to DevOps adoption, and resistance to change was No. 1.

    Becoming Process-Centric

    It’s a complex challenge, but it does have a solution. The best way to enable continuous delivery while supporting legacy initiatives such as major incident and IT service management is to ensure your IT teams are process-centric. This entails mapping out the business processes required to achieve IT objectives and identifying the data, tools and people needed to optimize existing workflows while driving real change. If you’re not sure where to start, here’s how you can get started.

    Map Out Essential Processes

    IT teams frequently make the mistake of buying a checklist of tools they believe are necessary, but this can make it difficult to know where to start when evolving IT to support continuous delivery. Without a business process strategy in place, it’s virtually impossible to integrate your tools and optimize collaboration between teams.

    Enable IT to be a driver in achieving business goals by clearly defining your business objectives and the activities that directly support them, as well as managed entities and available response types. For instance, common business objectives include increasing the frequency of code releases, reducing the number and severity of major incidents, minimizing lead time for product updates and improving SLA standards of uptime.

    Identify and Connect Your Process Ecosystem

    After mapping out your business processes, you’ll need a connected ecosystem of data, tools, people and teams to drive them forward. This ecosystem is possible only if you understand the different types of contextual data you need for each managed process, from monitoring insights to system logs to stakeholder communications.

    Next, choose the tools to manage the processes and data flow. For instance, application performance monitoring and management, ChatOps and issue tracking are all common tool types. Then, pinpoint the people (or teams) that need to be involved in each type of process so that you can keep the right people informed—no more, no less.

    Finally, you should ensure that your teams within development, operations and support have an efficient and streamlined way to collaborate so that processes always advance to completion.

    Operationalize Steps for Collaboration and Resolution

    Now let’s get more specific. What are the steps required to facilitate collaboration and drive resolution? By defining these steps, you can optimize and effectively templatize processes that can be reused, thereby improving both consistency and efficiency each time a recognizable  incident comes into play.

    For each process, start by outlining the tasks necessary to resolve the issue, then detail the tools needed and the personnel or teams that should be involved. Repeat this for all of your processes to define the necessary technology investments.

    Automate and Curate Manual Work

    Operationalizing processes improves efficiency to a certain degree, but you can go a step further to really maximize the benefits. This is where you can leverage both automation and curation of manual work. For each managed process, drill down at the task level to determine whether you can either fully automate or, if human intervention is needed, efficiently curate work.

    For instance, routine tasks that have a defined set of triggers and can use software-driven logic to determine and execute on next steps can be fully automated, whereas tasks that have a defined set of triggers but require that human intervention determine which next steps should be curated. There are, of course, different levels of curation and automation based on associated triggers and response types, so you’ll need to gauge these and plan accordingly.

    Extract and Archive Contextual Information

    It’s important to maintain a repository of information on your business processes and how they are handled. This kind of data gives you complete visibility into issues as they progress, and ensure you’re taking consistent, proven responses to similar situations when they arise in the future. Some stakeholders will, of course, also need access to this information.

    The way to maintain this repository is to understand the unstructured data (such as FYI communications) versus structured data types (such as ChatOps) managed within each process. Once these are identified, you can map the unstructured data into structured data. The rule of thumb for aligning stakeholders is that the business and customers require data from structured sources; they’re looking for the information concerned with status and real business impact.

    In contrast, subject matter experts will often rely on unstructured data. They benefit more from the in-depth insights from system and application logs, performance metrics, and other essential data points to help them assess, resolve and manage situations.

    In Conclusion

    The journey to becoming process-centric may seem daunting, but there is a proven approach that can guide you toward the dream of becoming a mature DevOps organization. Figure out which processes you can automate, which require human intervention, and be sure to let your teams work within the systems they know and trust to ease the resistance to change.

    One persistent danger is that multiple systems can create silos of data that stand in the way of collaborative work. A process-centric IT organization with integrated systems can help move data to enable collaboration, more informed decisions and better outcomes. And nothing takes the awkwardness out of a teenager like better outcomes.

    — Kristin Apgar

  • Is DevOps Really Necessary?

    Is DevOps Really Necessary?

    I spend my days discussing DevOps and digital transformations with companies of all sizes, from startups to some of the world’s largest enterprises. I’ve found that many teams are still at a very early stage in modernizing their technology, culture and practices. However, I’m surprised at the number of the organizations that don’t believe in change because they “have been doing just fine up until now.”

    This is a recipe for failure in the long term. Their competitors will likely be working in loosely coupled teams with shared responsibilities and increased collaboration. They will have adopted continuous integration, continuous deployments/delivery and automation, to name a few processes. In time, these competitors will begin to consume the laggards’ market share. To put it into perspective, you might still be talking about your next deployment coming up in six weeks time, while they are talking about deploying into production 12 times today alone.

    With their increased agility, stability and reliability, these more successful teams will be faster. They will not be afraid of failure and will have built fault-tolerant systems that can rapidly and automatically repair. These self-healing, well-tested systems and processes will free time for developers and other team members to focus on developing newer and better applications in response to customer feedback—all while the your team might continue “doing fine.”

    We aren’t just talking about buzzwords here; the data shows that those who are playing catchup are also potentially causing themselves more harm than good. Puppet’s “2017 State of DevOps Report” shows that the gap between higher- and lower-performing teams has narrowed as throughput has increased, meaning lower-performing teams are working faster, shipping more code to production and responding to changes quicker than they were the previous year. This is great in principle, but in 2017, change failure rate had also increased, showing that low performers were actually more likely to fail when making changes than they were in 2016. The data shows that low performers had a 3x change failure rate in 2016, which increased to 5x in the 2017 Puppet study. From a business standpoint, this means that those higher-performing teams are able to deliver more value to their customers by responding faster and more reliably to feedback and market demands.

    Companies should certainly be paying attention to what their competitors are doing, no matter what area or sector in which they operate. However, they also must not lose sight of the importance of quality, security or feedback during this journey. If you know you are at a much earlier stage in your transformation, focus on getting it right. There will always be setbacks and challenges, but as the old saying goes: This is a marathon, not a sprint.

    DevOps methodologies, when applied effectively, will make your organization more successful.

    — Conor Delanbanque

  • Enthusiasm for DevOps Greater Than Actual Adoption, Survey Shows

    Enthusiasm for DevOps Greater Than Actual Adoption, Survey Shows

    When it comes to DevOps practices, adoption continues to lag intention by quite a bit.

    GitLab recently surveyed some 5,300 software developers and engineers from mostly small and medium businesses around the world. The survey examined overall attitudes and perceptions around the state of DevOps, with a particular focus on uncovering any disparities that might exist between developers and IT management.

    The responses showed that many organizations are sold on the idea of DevOps, especially those who have implemented it. Sixty-five percent of the developers polled felt that using a DevOps workflow would save development time. An even higher 81 percent of managers shared that same sentiment.

    Organizations with an integrated DevOps strategy reported a high correlation between DevOps practices and overall team productivity. Development teams that deploy code on demand and spent more than half their time on new projects, for instance, had twice the rate of DevOps adoption compared to lower-performing teams. Higher-performing teams also generally considered automation of the software development life cycle a top priority and had access to better tools.

    Yet, the number of organizations in the survey that have actually implemented DevOps practices was far smaller than the otherwise enthusiastic support for it would suggest. In fact, only 23 percent of the respondents in the survey identified their team’s methodology as following a pure DevOps model. Thirty-five percent described their DevOps culture as being somewhat established.

    The low adoption rates are somewhat at odds with Forrester Research’s prediction about 2018 being the “Year of Enterprise DevOps” and may reflect the global nature of the survey base and the fact that many respondents were SMBs. Forrester found DevOps adoption to be around 50 percent when it conducted a survey of its own of 237 DevOps pros last year.

    GitLab’s survey found that while developers view DevOps as critical to software development life cycle, many have not implemented a DevOps workflow. Similarly, while over 95 percent of the survey respondents placed a high value on collaboration and 81 percent felt that such collaboration was easy to achieve, 25 percent said they did not have enough visibility into operations, security and production. And 42 percent reported unclear directions as one of their biggest challenges to getting work done.

    Culture continues to be a big roadblock. “It’s hard to get Dev and Ops to work together in a cross-functional way,” said Mark Pundsack, head of product at GitLab. “That challenge can be exacerbated by tooling. Disjointed toolchains lead to disconnected processes and barriers to communication that get in the way of working together.” Many developers in the survey reported using at least five tools to get their job done.

    Nearly 6 in 10 of the survey respondents (58 percent) pointed to ingrained practices as their biggest roadblock to adopting new tools and practices, while 50 percent blamed an inherent resistance to change.

    Interestingly, remote employees tended to have a more favorable outlook on how things were going with DevOps, with 67 percent saying they had good visibility into what others in the organization were doing compared to 57 percent of on-premises teams.

    “DevOps culture is clearly important to achieving high performance,” Pundsack said. “For managers … the survey identified the top challenge in getting work done was unclear direction and managers should be able to play a role in fixing that.”

    The apparent gap that exists between intent and adoption was not the only surprise in the survey, Pundsack said. Also unexpected was the extent of the discrepancy between how executives think their teams are operating, and what developers are actually saying. When asked how often they deployed code, 47 percent of managers were under the impression their organizations were deploying multiple times a day in contrast to the substantially smaller 39 percent of developers who said the same thing.

    That one area aside, though, developers and IT manager seemed better aligned on other issues in the Gitab survey. For example, more than three-quarters of both developers and managers feel they are set up to succeed and about one-third of them felt that they deadlines they have to deal with are fair.

    GitLab’s survey showed that most organizations expect to invest in continuous integration and continuous deployment tools in 2018, with nearly half strongly agreeing that CI can help speed up the development process.

    “CI can help alleviate blockers in the development cycle such as removing the manual process to verify code before deploying,” thereby allowing development teams to have a faster and more effective feedback cycle, Pundsack said.

    — Jai Vijayan

  • Sneckdowns In IT Operations

    Sneckdowns In IT Operations

    What IT operations professionals can learn from the actual paths people take through tools and processes, and the differences from the expected usage model.

    It has been a snowy winter across much of Europe, and although the snow is now beginning to melt in most places, there are still substantial sneckdowns to be seen across our cities.

    If you’re not familiar with the term “sneckdown,” it describes those areas of streets that are not actually used, as revealed by the unmarked snow that settles on them.

    Sneckdown example

    Transportation planners have even been known to use sneckdowns successfully as planning aids.

    The world of IT should learn from this example. We may not have anything as whimsical as snow to help us, but on the other hand, we do have enormous amounts of data. Let’s investigate some examples, starting with IT operations.

    Restate My Assumptions

    IT operations has a bunch of assumptions baked in, but just like with street layouts, some of them may be obsolete, leftovers from a previous generation of technology. My own hometown dates its founding back to Roman times-218 B.C., to be precise-but the sorts of design choices that Roman colonists might make to facilitate chariot and ox cart traffic have had to be updated significantly-first to accommodate motor vehicles, and then again to ensure that the motor vehicles did not crowd out other forms of transportation such as bicycles.

    Similarly, much of IT operations is based around the notion of a ticket, which describes and encompasses an atomic problem. This ticket has a single owner at any one time, and is processed sequentially through a series of steps. This is the operations equivalent to the waterfall model of development.

    These assumptions used to be reasonable in previous eras of IT technology, when a simple failure of a single piece of equipment was enough to trigger an incident. That incident ticket could easily be paired with the specific piece of equipment that was impacted, and from there a simple lookup would return the responsible group, who could then be assigned the ownership of the ticket.

    These days, that model is largely obsolete. Hardware, especially virtual hardware, is cheap enough to be made multiply redundant. Automated functions such as load balancing ensure that single failures are rarely enough to cause an actual incident. Instead, multiple failures have to occur in the right sequence—or the wrong one, depending on your point of view. Meanwhile, operators are drowning in a sea of red alerts, unable to find and focus on the few actionable ones because processes require them to respond to everything. Finally, when a real ticket does make it through the noise, it gets bounced around from team to team, as harried operators try to work out whether it’s their problem or somebody else’s.

    Operators are human, after all, and over time they develop their own paths through and around this rigid process, just as people walk and drive their own paths through the snow.

    Examples of IT Operations Sneckdowns

    Users Working Together

    One big IT operations sneckdown is collaboration. Instead of using the ticketing system and its rigid assumptions, operators work around it, using alternative methods to work together and share information with each other. The goal is to deliver full-stack operations for the entire complex business service that is being supported by the team.

    The problem with sneckdowns is that if there is no city planner watching and measuring, eventually the snow will melt and leave no visible trace of the paths that people take around their city. In the same way, the risk to the wider organization is that as people work and collaborate in alternative, unofficial tools—whether IRC or Slack—information gets stuck there and does not make it back into the official knowledge base, where it is available to be consulted and reused over time.

    Sneckdown Recommendation: Recognize that the nature of modern IT and network architectures means that there may be no single owner of a problem. The rapid rate of change, too, makes it almost certain that any owner will not be documented in a static database somewhere. Instead, modify processes to recognize this multiowner approach, and enable users to work easily with each other without having to play ticket ping-pong.

    The Right Tool for the Job

    This overlaps with another sneckdown: unofficial tool adoption. In the same way that operators might start using an unofficial collaboration tool to route around limitations in the official service desk process, they might look at Kanban to share task status, or start building libraries of automated diagnostic and remediation actions. The problem is that the rigid official policy often does not accommodate these extensions, and indeed has no mechanism to do so.

    Sneckdown Recommendation: Modern operations tools need to offer flexible interfaces to enable integrations and extensions to be done in a safe way. This way, if a particular integration does not work out or needs to be upgraded, that can be done without disruption to the wider process and to any other integrations that are working well.

    Look into the Shadows

    The final sneckdown—at least for this snow storm—is shadow IT. In the same way that much of our universe is theorized to be made out of dark matter, not visible to our current tools, a growing proportion of IT spending is happening outside the “official” IT budget. This also means that whatever is bought with that budget is outside the official IT operations and support process. That’s all very well on paper, but if the tool Sales or Marketing were relying on to do their jobs goes down, somehow, IT operations is still going to get the call to fix it.

    Sneckdown Recommendation: There isn’t an easy short-term fix for this one—no way to shovel it out of existence. Instead, IT operations professionals need to work on this issue from two angles. The first is to address the causes of shadow IT, those rigidities and frictions that make people look for outside solutions rather than engage with their in-house IT departments. When everything is properly aligned, the IT department has a lot to offer in terms of understanding of How Things Are Done inside the organization. The other angle is—yes—to make it easier to accommodate solutions that might have been procured originally as shadow IT. Figure out how to engage with support processes for SaaS solutions that people adopted on their own; if it’s working for them, it’s up to you to keep it working! The benefit is that this alignment will reduce the need for constant injections of energy by sysadmins and operators to keep things running: the Second Law of IT Ops.

    Conclusion

    Compared to city planners and transportation activists, we IT professionals actually have it pretty easy. We’re not reliant on particular weather conditions for our data, but rather, we have enormous amounts of data at our fingertips all the time. It’s up to us to look at the data with open minds and read the conclusions that are already there. The hard part, of course, is putting those conclusions into action, especially when that requires changes to widely accepted processes, but that is also an opportunity to revisit those processes and make sure they are still as fit for purpose as when they were first adopted.

     

    Game Day Chicago

    If you’re interested in what that process looks like, there is an interesting event series: Game Day ITSM & Incident Management Workshop. The next Game Day is in Chicago on March 21, but there is a whole series running, so there is probably an event near you soon. It’s a fun time, and also a great opportunity to rethink some assumptions and look at where some sneckdowns might be that could be incorporated into your planning.

    — Dominic Wellington

  • Defects: Pay More Attention to What, Less to Who

    Defects: Pay More Attention to What, Less to Who

    Software defects have been with us forever, and while DevOps and agile can reduce the number of defects generated, it is unlikely this number will ever get to zero for a given organization—and even if it does, it won’t likely stay there. Simply put, we’re developing increasingly complex systems on increasingly short timelines. The reduction possibilities that DevOps offers cannot completely combat this combination.

    But the quest to keep defects down is necessary. The goal should always be “Less, not more.”

    One of the things we have historically done is associated defects with developers. It is natural to want to know “who broke the entire payroll system,” but quite often “who” is the wrong question.

    A huge benefit of both agile and DevOps is that it allows us to look closely at where the process fails and determine why. While every developer in a shop knows “you should avoid modifying that module,”—and those are the easy ones to target for early bug reduction—there are other modules and source files that might be a hidden source of a certain percentage of defects. Using the defect tracking toolset to keep tabs on where bugs are occurring might show that the problem is a given developer, but more likely it will show that the problem is a given set of source.

    So if you are not already, it is time to start tracking defects closely and looking for patterns. There are some great documents out there about relatively common break points that might be useful. However, reviewing your own data is more useful, as it isn’t about common—it’s about actual problems in your source pool.

    This is all part of the process for agile and DevOps, I’m just saying it a different way than the usual “blameless” discussions because I think they fail to connect for some shops. Of course blameless is great, the point here is focus on problems—whether that is blameless or not depends upon which problem you find you have.

    Meanwhile, keep cranking it out. If you’re working on a viable product, then defects are an annoyance, but not something to gnash your teeth over. You are developing a viable product, after all.

    — Don Macvittie

  • Datical Helps Pull DBAs into Modern DevOps Age

    Datical Helps Pull DBAs into Modern DevOps Age

    As organizations move to embrace DevOps, some have inadvertently left database administrators (DBAs) behind. As a result, developers increasingly find themselves managing databases, as DBAs can’t keep up with the rate of change introduced by DevOps processes. To help narrow that growing gap between developers and DBAs, Datical created a namesake platform that applies continuous deployment/continuous integration (CI/CD) processes the management of databases.

    An update to the Datical platform now adds real-time views and dashboards that illustrate the status of database deployments. DevOps teams now can simulate the impact of changes before they are deployed in a production environment and enforce rules to meet audit and compliance requirements. A new management tool also makes it easy to track database changes across the pipeline as well.

    Pete Pickerill, vice president of product strategy for Datical, said Datical 5 now makes it possible to automatically apply the updated changes to a database without many of the steps that slow down application development on top of databases.

    Finally, with this release, Dacital also provides a mechanism for centrally managing all the credentials attached to database.

    While agile development methodologies have lead to an increase in the rate at which code is being written, database management continues to act as a drag on the rate at which applications are being deployed and updated. Datical decided to address that problem by creating a platform that specifically applies DevOps processes to the management of databases, said Pickerill.

    Organizations that still rely largely on manual database processes often find the level of tension between developers and database administrators to be high. Developers often will simply opt for a database they can manage themselves rather than engage a DBA. That can result in suboptimal choices being made for different classes of application workloads that become apparent once the application starts to scale. The challenge and opportunity is to put tools in the hands of DBAs that enable them to respond rapidly to changing application dynamics, said Pickerill.

    Of course, in many instances developers have simply taken over the database administration task. But every minute spent on those tasks is one less minute a developer is writing code. The more efficient option is to find a way to integrate DBAs into the overall DevOps process. The effort requires both providing DBAs with access to new tools as well as changing an IT culture where many DBAs assumed the application development strategy revolved around their ability to find time to manage a database. Today, DBAs are now being asked to manage a multitude of types of databases that are being deployed in unprecedented numbers. Clearly, processes rooted in approaches to database management developed a decade ago or longer no longer suffice.

    Not every DBA will make that transition. But at this juncture it’s clear that fundamental change to the way databases are managed is now a matter of when rather than if.

    — Mike Vizard

  • Sauce Labs Adds Analytics to Continuous Testing Platform

    Sauce Labs Adds Analytics to Continuous Testing Platform

    Sauce Labs has added additional debugging and analytics capabilities to its testing platform to make it easier to manage continuous testing across multiple application development projects.

    Lubos Parobek, vice president of product at Sauce Labs, said DevOps teams need to prioritize which bugs to address first across multiple development projects occurring simultaneously. In fact, according to the latest annual survey of 1,091 technology professionals conducted by Dimensional Research on behalf of Sauce Labs, DevOps teams on average are now taking longer to address bugs. These days there are many more issues that require debugging across multiple development projects, which is overwhelming DevOps teams, who must then prioritize some debugging requirements over others.

    Specifically, the survey found only 21 percent of respondents report they fix bugs immediately, down from 23 percent in 2017.

    Analytics capabilities included in Sauce Labs’ rechristened Continuous Testing Cloud include the ability to view test results by browser and operating system, visibility into pass/fail and error trends and the ability to see how a single test behaves over time so DevOps teams can more easily identify tests inconsistent with overall test run time and pass/fail rates.

    Scheduled to be available in the second quarter, the Continuous Testing Cloud makes it possible to more easily reconcile advances in the rate at which applications are being developed and constrained testing and debugging tools, Parobek said.

    The Sauce Labs survey results also show progress being made in the realm of mobile application testing. A full 77 percent of respondents said they now use a mix of both real devices and simulators or emulators for mobile testing, up from 34 percent in 2015. That approach results in higher fidelity to the actual experience of an end user on each device, said Parobek.

    That shift, however, may also explain why there is more manual testing. More than half (56 percent) said they do as much or more automated as manual testing, a minor decrease from 59 percent (2017) and 60 percent (2016), the report found. Just under half (45 percent) said they do, however, expect to increase spending on test automation in 2018.

    Parobek said organizations are moving into what he describes as the third era of testing. The first two eras were manual testing followed by a shift to automated testing. The third era is marked by continuous testing that leverages automation to make it possible to test code as it’s being developed rather than after, when the application is complete.

    Naturally, each organization is not as far down the path toward continuous testing as every other. Parobek said for the most part, early adopters of DevOps practices employ continuous testing more widely than traditional enterprise IT organizations that are still in the early stages of embracing DevOps. The good news is the latter have an opportunity to leverage a much larger body of DevOps best practices than the organizations that originally pioneered the concepts.

    — Mike Vizard

  • Service Mesh: The Best Way to Scale Enterprise Apps

    Service Mesh: The Best Way to Scale Enterprise Apps

    Microservices are great for DevOps, but the service-to-service communication these architectures depend on are complex to run and manage at production scale. Enter service mesh: the best way for enterprises to scale, secure and monitor apps. A service mesh is a dedicated infrastructure layer enabling service-to-service communication to be quick, secure and reliable. If you’re building cloud-native applications, you need a service mesh.

    After talking to development and operations teams it became clear that microservices are great for development velocity, but the complexity and risk in these architectures lies in the service-to-service communication that microservices depend on. Service mesh is an application-first approach to provide a communication fabric for microservices, giving DevOps teams the flexibility and autonomy they desire while providing the policy, visibility and insights into their microservice environment that operations teams demand for production-grade applications.

    A service mesh provides a robust microservice communication fabric to enable the best possible path to scaling containerized apps whether in the data center or in the cloud (or both). There has also been a recent emergence of enterprise-grade meshes to address the needs and complexity of enterprise production environments. A service mesh needs to do more than just scale apps; it also needs to monitor and secure them. A supported infrastructure allows DevOps teams the flexibility and autonomy they desire while providing the policy, visibility and insights into microservices that operations teams demand for production-grade applications.

    Advantages of a Service Mesh

    Think about your plans for microservices. Maybe you plan to have 10, 50, 100 or 1000’s of services running in your Kubernetes cluster. How do you get all of those services in your new microservice and container environments in an efficient, uniform way?

    Do you know who is talking to whom and if they are allowed to? Is that communication secure? How do you debug something when it goes down? How do you add tracing or logging without touching all of your applications? Do you know what the performance or quality impacts of releasing a new version of one of those services is on the upstream and downstream services?

    A service mesh helps answer those questions. As a transparent infrastructure layer that is inserted between your microservice and the network, it gives you a single point in the communication path of your applications to insert services and gather telemetry. You can do this without requiring changes to your applications.

    2018: The Year of the Service Mesh

    The concept of service mesh is relatively new. In fact, until 2018 was declared “The Year of the Service Mesh” at KubeCon in December 2017, most people had never heard of a service mesh. There are now several open source offerings such as Istio, and several companies building out supported meshes to take the burden out of managing microservices. It will be interesting to see how a service mesh enables organizations to further capitalize on containers and microservices.

    — Zach Jory

  • DevOps: Bringing People, Process and Technology Together for Digital Transformation

    DevOps: Bringing People, Process and Technology Together for Digital Transformation

    DevOps principles are built on an effective, continuous cycle of improvements—key factors that help drive digital transformation for companies large and small. Inserting this agile mindset into culture and operations, however, can be challenging, as the approach sometimes conflicts with the more the methodical ways of IT. So how can people, process and technology be brought together for business success in this new paradigm?

    People and Process

    Digital transformation first must be defined. It’s a ubiquitous app or service that can be consumed from anywhere on any device at any time. Digital transformation aims to reduce the consumption barrier, which enables businesses to profit from every possible engagement while leveraging data-driven models.

    A DevOps culture supporting such digital transformation is about agility, availability and scalability with continuous integration and continuous delivery of services. These strategies require new technology skillsets, which begs the age-old question: build or buy?

    This question leads to more questions, which IT organizations struggle to answer. Should they “buy” new talent and build the capabilities with the existing team? Should they buy new talent and implode the existing team? Or, should they build up the existing team and enable them to bridge the gap from traditional ops into a DevOps culture?

    Organizations often overlook one of the most important considerations. Putting top engineers with topnotch development skills together does not always produce a successful team. DevOps calls for collaboration and cooperation, so organizations must pay attention to soft skills. People collaborating across traditional boundaries and silos are at the center of digital transformation. Progress only happens when the organization is moving forward as a whole with the same end goal and execution in mind.

    Digital transformation is a function of speed to market, and that’s where traditional IT and DevOps processes clash. IT follows the doctrine of trust but verify, a methodical process filled with rigor and discipline. On the other hand, DevOps’s modus operandi is continuous integration and continuous delivery while failing fast if needed, trying anything and everything to iterate and get as many data points as possible, as fast as possible.

    Blending these cultures and processes is a matter of stepping back and realizing they are actually more similar than different. Striking a process balance between IT Ops and DevOps empowers organizations to deal with the volume, variety, and velocity of change involved in digital transformation.

    Normalize the Right Technology

    Making the right technology choices means finding the right signal in the noise of all the possibilities. From programming languages to the automation engine, infrastructure services (virtual machines, cloud instances, containers and microservices, serverless) and more, when creating a DevOps culture, people will gravitate to solutions that are biased toward what they know best.

    The number of choices turns into an endless buffet with a tendency to overindulge, which creates bloat and starts accruing tech debt. Shouldering too much technology and being half-baked into “best-in-breed” solutions are not enough.

    Disruptive technologies surpass the bounds of “good enough.” The wrong technology choice will put an organization behind in a world that’s disrupted constantly. The organization may get disrupted into nonexistence. Choose the technology that will best enable normal consumption of services while you scale services, become more agile, and expand availability. That’s what digital transformation done right accomplishes.

    Customer-centric

    A transformative DevOps culture keeps the customer as the focal point. Some organizations view this as “the customer is always right.” That’s one way to look at it, but sometimes the customer doesn’t know what “right” is. It’s up to the organizations to demonstrate good and great.

    That means being less feature-centric and more solutions-centric—actually solving the customers’ pain points. Don’t get stuck in the “easy” to implement. Don’t assume your use case is the right use case without being certain of how customers would leverage and utilize the end results of the transformation. Remember, the goal is to reduce the barriers to consume for consumers.

    One of those barriers is having a bad experience with an application or a service. Users want it now and are willing to pay if the service is excellent, always available as needed, and easy. We’ve seen this with rideshare applications, Netflix and Amazon.

    Yet, the SolarWinds Digital Experience Monitoring Survey shows that 40 percent of respondents are suffering digital experience issues that negatively affect their businesses. They also report a lack of visibility into customer experience. Forty-four percent say end users are first to discover an issue, and 25 percent of those surveyed say they are the last to know.

    Best Practices

    Digital experience monitoring (DEM) tools help solve these problems. For example, when implemented as part of a rigor and discipline approach, the survey shows that 59 percent received fewer support tickets, 44 percent improved brand image, and 33 percent gained a better ability to retain customers. Among those who reported that DEM has driven increased revenue, 15 percent of revenue increase can be attributed to DEM.

    Observability and Control

    At the same time, a certain level of observability and control must be integrated into a successful DevOps strategy for digital transformation efforts. Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.

    Depending on who you ask, observability can mean logging for one IT professional, metrics for another, or tracing for a DevOps engineer. Despite these shades of grey, the reality is that application stacks are evolving with so much velocity, variety and volume that such that a combination of all three (metrics, logs, and traces) are needed to build a viable observability protocol.

    To achieve this level of observability—and, as a result, control—you must have a powerful integrated full-stack monitoring platform that enables analysis of key performance metrics, events and logs,and application code traces. This approach to monitoring as a discipline enables developers, DevOps engineers and IT professionals to better manage, optimize and troubleshoot the full stack at every layer—from the infrastructure services and application to the end-user experience—from a single dashboard.

    With observability and controllability enabled, you can successfully and continually integrate and deliver the applications and services enabled by your digital transformation.

    — Kong Yang

  • CA Technologies Advances DevOps Automation Agenda

    CA Technologies Advances DevOps Automation Agenda

    CA Technologies further advanced its staged goal of unifying its disparate portfolio of DevOps automation tools by announcing today a new release of CA Workload Automation AE (AutoSys) software now directly integrated with the CA Automic One Automation platform the company acquired early last year.

    CA AutoSys is the older of two offerings and is positioned by CA Technologies as being able to support more complex workloads running on a larger number of platforms, including mainframes. CA Automic One Automation was acquired to bolster the automation capabilities CA can offer optimized for distributed computing environments.

    Gwyn Clay, vice president of product management for CA Automic, said it’s no secret CA Technologies has multiple automation engines. The long-term goal is to enable organizations to employ any mix of automation engines and associated DevOps tools as they see fit to create what the company describes a software factory.

    Other new capabilities being added to CA AutoSys include the ability to centrally manage all the various agents the company provides, support for connecting to Amazon Web Services (AWS) and Azure Database Services, improved load balancing and the ability to set up more custom email notifications.

    Clay noted the rate at which IT organizations are embracing DevOps varies widely. Just about every organization has at least one DevOps project or pilot in place, but most of them are still relatively nascent compared to the level of sophistication being applied by web-scale companies, he said.

    CA Technologies is betting that, rather than stitching together components themselves, enterprise IT organizations will prefer a more modular platform that requires less effort to set up without locking them into a specific tool.

    DevOps is coming to the enterprise in one form or another. But unlike web-scale companies, where developers and engineers have tended to work closely together from the inception of the company, enterprise IT organizations will navigate myriad culture issues. There has been a historic predisposition in enterprise IT organizations for developers to “throw code over the wall” to IT operations, with little regard for how that code might run in the actual production environment. IT operations teams, meanwhile, have not always been as forthcoming about the state of that production environment as they might be. That lack of trust and visibility means both sides often make decisions that are not always in the best interest of the overall organization. For example, an application workload might wind up being deployed on a public cloud, even though that platform may be both more expensive and less secure.

    At the same time, it’s still not clear to what degree warring cultures inside the enterprise will be able to put aside their historic animosities to create a coordinated approach to automation and DevOps. Mainframe and distributed computing teams to this day still battle for control over workloads inside the enterprise. Whatever the outcome, there’s no doubt various teams will eventually be forced to work more closely together to achieve DevOps goals regardless of whether they like it.

    — Mike Vizard