Tag: reporting

  • VMware Advances Hybrid Cloud Ambitions

    VMware Advances Hybrid Cloud Ambitions

    VMware today advanced its hybrid cloud strategy by making a series of updates to existing offerings, including the CloudHealth by VMware software-as-a-service (SaaS) application for managing and optimizing cloud applications, which VMware acquired last summer.

    In addition, VMware Cloud on AWS is now available in data centers operated by Amazon Web Services in Canada, Paris and Singapore, the company noted. VMware also announced that the latest version of VMware Cloud Foundation—which provides and automatically updates all the VMware compute, storage and networking software required to build a cloud—has been optimized to deploy VMware Horizon virtual desktop infrastructure (VDI) software, and will available on Dell EMC VxRail hyperconverged infrastructure in April.

    New capabilities being added to CloudHealth by VMware include enhanced multi-cloud reporting, multidimensional reporting and workspaces reporting.

    The CloudHealth platform also is now integrated with the Wavefront by VMware cloud analytics and monitoring platform, enabling users of CloudHealth to employ Wavefront metrics to rightsize and optimize applications running on multiple cloud applications as part of their DevOps practices.

    A future update to the CloudHealth platform will add enhanced amortization as well as support for convertible Reserved Instance (RI) exchanger automation, container cluster visibility and cross-family rightsizing for Amazon EC2, according to the company.

    Finally, VMware announced it is making available version 9.7 of VMware vCloud Director to cloud service providers. This offering provides a single pane of glass through which multiple VMware environments can be managed regardless of whether they are deployed on-premises or in the cloud. The latest version of VMware vCloud Director adds support for version 3.0 of VMware Cloud Availability, which unifies onboarding, migration and disaster recovery services between multi-tenant clouds. VMware vCloud Director is only available to cloud service providers, but Ajay Patel, senior vice president and general manager for the cloud provider software business unit for VMware, said VMware and its cloud service provider partners plan to make available a software-defined data center (SDDC)-as-a-service offering through which they will manage deployments of VMware from the edge to the cloud on behalf of IT organizations.

    VMware also revealed there are now more than 60 partners building or delivering managed service offerings for VMware Cloud on AWS, and more than 280 partners have achieved the VMware Cloud on AWS Solution Competency. VMware also now has 35 VMware Cloud Provider Program partners that have achieved Cloud Verified status, which is required to be eligible to deliver the forthcoming SDDC-as-a-service offering. Patel also revealed the number of organizations running instances of VMware on public clouds now exceeds 1,000, in the wake of the launch of VMware Cloud on AWS last year.

    Patel said the shift to hybrid clouds is more about transforming IT operational models than it is about deploying workloads at new physical locations. The challenge many IT organizations will need to come to terms with going forward is deciding what portions of that IT operational model they want to run themselves versus opting to rely on VMware and its partners.

    — Mike Vizard

  • 5 Ways KPI Analysis Helps DevOps

    5 Ways KPI Analysis Helps DevOps

    Four Vs drive big data solutions: volume, velocity, variety and veracity. Volume and velocity are technical considerations usually receiving a healthy dose of attention among architects and coders. However, variety and veracity often determine success or failure, and they tend to sneak up on teams who haven’t fully considered them. Key performance indicator, or KPI, analysis can head off variety and veracity problems before they escalate.

    When some folks say “KPI analysis,” they mean KPI monitoring and evaluation—the business intelligence perspective. I’m talking about a DevOps analysis discovering which KPIs should be monitored and what data is needed. One step in this analysis traces each desired KPI back through necessary transforms to its required data sources, often termed as facts. Such an analysis clarifies variety and quickly establishes a path to veracity.

    From a strategy perspective, KPI analysis can also mitigate the symptoms of “big bad data” that devour an organization, consuming people and resources rapidly. Here are five ways KPI analysis helps DevOps teams get a handle on big data.

    1. It prevents over-measuring. If you don’t know what you’re looking for, more data probably won’t help you find it. Teams experiencing FOMO (fear of missing out) expand their data gathering, hoping more variety uncovers something interesting. Once your KPIs have been discovered and fully analyzed, stop exploring and start exploiting. Stick to monitoring KPIs directly driving the most impact for your business.
    2. It minimizes bloating. In any computational system, the most expensive operation is moving data. Simply moving stuff around eats processor, network and storage bandwidth. Even with cost-effective storage available, unnecessary data means a bloated infrastructure and less efficiency. KPI analysis identifies a minimum spanning set of data sources. A workload-optimized solution then concentrates on efficient algorithm implementations, yielding results aligned with your KPIs.
    3. It stops wallowing. Allowing people too close to raw big data is extremely dangerous. Off-the-shelf analytics tools often carry a lot of non-KPI variables with them. Cloud-based martech tools are some of the biggest offenders; they are essentially showing off how many ways they can expose their underlying data. Avoiding the temptation to wallow through intriguing but ultimately irrelevant data saves precious brain cells. Customize reports and optimize dashboards around your KPIs.
    4. It de-obfuscates conversations. Nope, not talking about JavaScript here. We want everyone in the organization talking about the same KPIs and only those KPIs. You’ve seen those presentations where someone tries to dazzle an audience with mostly meaningless charts and graphs. Obfuscating your KPIs underneath a pile of other observations wastes time and risks defeating the purpose. Trust your KPIs and focus all organizational conversation on those.
    5. It thwarts non-causality. This is one place where data scientists and analysts really earn their money. Part of a solid KPI analysis determines causality—if your data sources change by some amount, the KPI needle responds by a quantified amount. Understanding those relationships is critical in avoiding false conclusions from data. If your KPI isn’t moving despite changes at the data sources, or your KPI is changing and the obvious data sources aren’t, step back and look at causality and transforms again.

    If you’re seeing these symptoms developing in your organization, it might be a sign automation was rushed in before KPIs were completely analyzed. At that point the toughest symptom to deal with is bloating. You may be stuck with a sized and funded infrastructure, and you really shouldn’t throw out good data already captured. Ongoing KPI analysis can lead your team to optimizations moving forward.

    Ideally, KPI analysis is performed upfront and these symptoms never develop. Realistically, complex systems have cycles of learning before their behavior is fully understood. Even a good upfront analysis may benefit from a revisit as trends start becoming apparent. When KPIs are rolled up periodically, you’ll need a minimum of six periods of results before expected causality is confirmed, maybe more. If that period is monthly, you’ll need ample patience.

    You’re spending too much on a big data solution to afford a big bad data outcome. KPI analysis is an important step, one DevOps teams with both technical and business expertise can excel at with some practice.

    — Don Dingee

  • Dashboards from Hell

    Dashboards from Hell

    Even organizations with a mature DevOps services model inevitably will hear from executive sponsors about the “need” for a dashboard for DevOps information. Organizations just starting to embrace DevOps won’t have the time to construct one yet, but over time will reach the same conclusion. The need is real, and the value is significant—it is how you get there that makes the difference. Vendors are keen to promote the idea that they already have a dashboard built into their respective products. And this is partially true.

    But the problem with dashboards is not a matter of information, it is a matter of perspective and scope. Ultimately, all of the information an audience wants can be found in the various DevOps tooling used in the continuum of services your organization created. However, locating some of it takes profound knowledge of how each tool operates, adequate security profiles and an elephant’s memory as you click on multiple windows to try to correlate the data you ultimately are seeking across the various tools.

    Before you know it, executives and perhaps even practitioners are calling for a consolidated dashboard that would present this information cleanly. The danger here, of course, is an attempt to consolidate every perspective from every individual audience into a single dashboard for your company. Instead, it is wiser to begin such an effort by breaking down the number of audiences into categories (by need, perhaps) and examining what is already available and what is missing to make a dashboard project effective, and not one originating from hell.

    Dashboards: The Practitioner Perspective

    There is a keen difference in what a typical engineer wants to see as it relates to DevOps from what the other audiences will be looking for: namely, detail. If you consider this part of a dashboard effort the foundational section (i.e. providing the right detail information to the engineers who ask), the remaining higher-level presentations can be built on top of this data. Most engineers are content to just “look into the given tool” to get what they want. But sometimes consolidating this information into a dashboard begins to make more sense.

    Ideally, your company has only one build tool and, again ideally, only one deploy tool. If you have multiple types of both kinds of tooling in these disciplines, you would be better served to pick a winner there than try to use a dashboard effort to consolidate build and deployment data across multiple build and deployment tools. Allowing multiple kinds of build and deployment tools across an enterprise usually winds up in the demand for multiple dashboards from multiple audiences, and before you know it, the dashboard project from Hades once again has infested your organization. A clear winner in build, deployment and release orchestration cuts out this kind of problem.

    Most of the time the need for a dashboard emerges when users are trying to see information in a way the native tooling did not plan for. For example, I want to look at a given server, or class of servers, and a list of everything that is on them (by version). Or as an example, I want to look at these technology components of this type and version and find out every server in my entire data center that they may have been deployed to.

    Some vendors offer internal reporting capability intended to meet these needs. However, using those reporting features should be labeled with “caveat emptor” signage. Typically, vendors apply their reporting directly against online or production data within their respective tools. So an engineer presses the report button on his build tool, and the build reporting function begins to look at its own data from a production viewpoint, instead of from an offline reporting copy of the data. When you start running reports against “live” tools, you get slowdowns, sometimes significant slowdowns.

    To avoid this issue, start with determining what data the engineers need, how often they need it and to where you can copy this information (to form the basis of your dashboard system). You also may wish to examine how the base data is inherently constructed and join some of it where it makes sense, so you are not issuing very large queries joining key data from many tiny tables together before anything sensible emerges for your new dashboard system. Speed of presentation will matter still, even though the data becomes only near-real-time instead of real-time.

    Dashboards: The Business Perspective

    Once you have ascertained the detailed information to satisfy your engineering or practitioner community, it usually needs to be married to your business segmentation to meet the needs of this audience perspective. For example, I may have 30 servers that I use regularly in my e-commerce products or line of business division. My engineers may care less about this designation, as they only care which servers are content servers or database servers or application servers. But my e-commerce business teams look at this information differently. They only want to see “their” servers, not every server in my company. So to accommodate the next perspective audience, I need a way to marry servers and technologies to the product lines or lines of business my company understands.

    This may require an outside or additional database that tracks the raw server name and marries it with the business unit that cares about it. It may also track the application names the same ways, marrying them with the appropriate business unit. But this marriage now offers the ability to pull reports (or dashboard screens) by line of business—it is nearly certain your basic build and deploy tools weren’t designed to show information in this way. This kind of business segmentation, or filtering (in some tools), provides an entirely new way to examine the data. The need to drill down to the details may still exist, but a picture of change relative to a product line or business line becomes a high value-add for the business community who looks at DevOps data.

    The business community looks at DevOps information to help it make decisions based on the state of change at any point in time. E-commerce customers, for example, might get special company notifications of upcoming changes or improvements in their capabilities, as the business community is made aware, and stays current with the planned release events and quality of testing underway. Or those same notifications might be delayed based on a quick look at the same information presented the same way.

    The point is, business users are an important community or audience of DevOps information, and the lens they use to examine data is from a product or line of business construct rather than the raw data aggregated by a practitioner.

    Dashboards: The Executive Perspective

    Executives, no matter the level, usually are more concerned about trends in the information than point-in-time data. For example, a given executive may want to know how many deployments per month are happening out of the development class environment for a given line of business. If you have constructed your dashboard with the architecture laid out above, you should be able to provide this kind of information. The discreet data is stored offline (copied from the build and deploy and other DevOps tools themselves), then married with the business segmentation data. Now it can be presented in trend-over-time manners that executives invariably want.

    It is much more likely that a business team member will use the drill-down features of your dashboard than your typical exec. But having it available up to the executive layer is a huge win when demonstrating the detail of what is going on where and potentially framing the why. The other significant win of having a single dashboard for presenting trending data is the consistency in the presentation across the enterprise. Consider for a moment how many Excel spreadsheets are sure to spring up around the company when execs task their people to produce reports that aren’t available in a dashboard at their fingertips. Each minion of the exec likely will grasp certain data in unique ways, often resulting in bar charts that never seem to line up.

    Having one dashboard—or, rather, one method of presentation of the same data—eliminates having multiple unique minion versions that lack context and often misrepresent the real story. In addition, without having a central dashboard you construct and control, minions will pull data requests against the production copies of build and deploy and release tooling, perhaps slowing these tools down to a crawl while data is gathered. The act of looking at a thing will certainly change the speed of that thing, in this instance.

    Impact to the Bottom Line

    Meeting the 80/20 needs of your primary audiences of DevOps data can be done without tying up programming resources forever. Once you have delivered a core dashboard system with mainline capabilities, your executive or business teams can decide how much more resources they are willing to fund to continue to update and enhance the dashboard. As long as they fund it, you can dedicate the resources you need to keep making it better (see agile). But for most shops, this kind of dashboard is a three-to-six-month effort from end to end.

    Once the dashboard is up and running, it becomes “the” tool in the arsenal of transparency for DevOps services. Granting access to this kind of tool (i.e., setting up user IDs) is significantly simpler than doing it in the multiple of underlying tools you have to administer without it. The cost of administration in a large firm can be substantial if a dashboard is not present. Imagine having to set up a typical user ID in your build tool, deploy tool, configuration management database, testing tool(s), release orchestration tool and monitoring tool—and that’s just to start. Within these types of tools that provide a real service or function are secondary concerns such as security or permission profiles, group memberships, etc. A dashboard only needs a valid company ID, and you are ready to “see” change in motion via DevOps.

    The work effort typical executives “task” their minions to produce reports is usually no small effort, either. While this work effort is rarely captured or understood by the executives who request it, it definitively puts a drain on company resources (both man and machine). A functional dashboard can eliminate this work nearly altogether, and produce a consistent work-product output. Your DevOps strategies should be created to reduce costs and facilitate innovation at every turn. If you need help feel free to contact me, I am still looking at the moment 🙂 .

    To continue the conversation, feel free to contact me.