devops
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In the world of software development, one term you’re sure to hear a lot of is full-stack development. Job recruiters are constantly posting open positions for full-stack developers and the industry is abuzz with this in-demand title.
But what does full-stack actually mean?
Simply put, it’s the development on the client-side (front end) and the server-side (back end) of software. Full-stack developers are jacks of all trades as they work with the design aspect of software the client interacts with as well as the coding and structuring of the server end.
In a time when technological requirements are rapidly evolving and companies may not be able to afford a full team of developers, software developers that know both the front end and back end are essential.
In response to the coronavirus pandemic, the ability to do full-stack development can make engineers extremely marketable as companies across all industries migrate their businesses to a virtual world. Those who can quickly develop and deliver software projects thanks to full-stack methods have the best shot to be at the top of a company’s or client’s wish list.
So how can you become a full-stack engineer and what are the expectations? In most working environments, you won’t be expected to have absolute expertise on every single platform or language. However, it will be presumed that you know enough to understand and can solve problems on both ends of software development.
Most commonly, full-stack developers are familiar with HTML, CSS, JavaScript and back-end languages like Ruby, PHP or Python. This matches up with the expectations of new hires as well, as you’ll notice a lot of openings for full-stack developer jobs require specialization in more than one back-end program.
Full-stack is becoming the default way to develop, so much so that some in the software engineering community argue whether or not the term is redundant. As the lines between the front end and back end blur with evolving tech, developers are now expected to work more frequently on all aspects of the software. However, developers will likely have one specialty where they excel while being good in other areas and a novice at some things… and that’s OK.
Getting into full-stack, though, means you should concentrate on finding your niche within the particular front-end and back-end programs you want to work with. One practical and common approach is to learn JavaScript because it covers both front and back-end capabilities. You’ll also want to get comfortable with databases, version control and security. In addition, it’s smart to prioritize design, as you’ll be working on the client-facing side of things.
Because full-stack developers can communicate with each side of a development team, they’re invaluable to saving time and avoiding confusion on a project.
One common argument against full stack is that, in theory, developers who can do everything may not do one thing at an expert level. But there’s no hard or fast rule saying you can’t be a master at coding and also learn front-end techniques, or vice versa.
One hold up you may have before diving into full-stack is you’re also mulling over the option to become a DevOps engineer. There are certainly similarities among both professions, including good salaries and the ultimate goal of producing software as quickly as possible without errors. As with full-stack developers, DevOps engineers are also becoming more in demand because of the flexibility they offer a company.
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Across the board, industries need to embrace modern workflows to keep up with the speed of startups. And out of all the various methodologies, I find the “lean methodology” to be the most intriguing of them all. It’s a unique combination of pragmatism and a higher purpose.
Lean methodology descends directly from the Toyota Production Systems (TPS), which is based on a philosophy of eliminating waste to achieve efficiency in processes. It relies heavily on the mindset of “just-in-time,” making only “what is needed when needed, and in the amount needed.” In software development, this means only developing the features your clients need, and only when they need them.
To emphasize the point and stir some creative juices, let’s look at the Japanese concepts of muda, mura and muri, and how this applies to being lean when we are building and shipping software.
Muda is the “waste” we are working to remove that is directly hurting efficiency. Waste is any activity that doesn’t create value, in the form of the products and services we offer. As every engineer knows, spending half the day in meetings is a painful waste of time.
Mura is “unevenness,” referring to any variance in the process itself or the output generated. In software development, “mura” causes unpredictability that makes it impossible to embrace a “just-in-time” mindset. If the quality of a new upcoming feature is uncertain, then additional time and resources will have to be reserved for quality assurance and bug-fixing efforts. It’s better to know upfront what you are going to get, how long it will take and what the cost will be.
Muri is “overburden,” which happens when we demand the unreasonable from our team, tools and processes. If we want to deliver a specific feature just-in-time, then we must allocate the appropriate time and resources. Giving our engineering teams too many simultaneous tasks, or failing to give them the tools necessary to succeed, will only lead to disappointment in time, quantity, quality or cost.
Diving deeper into muda — what I consider the cardinal sin of lean methodology — here are the forms of waste we should always be on the lookout for:
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The outages at RBS, TSB and Visa left millions of people unable to deposit their paychecks, pay their bills, acquire new loans and more. As a result, the House of Commons’ Treasury Select Committee (TSC) began an investigation of the U.K. finance industry and found the “current level of financial services IT failures is unacceptable.” Following this, the Bank of England (BoE), Prudential Regulation Authority (PRA) and Financial Conduct Authority (FCA) decided to take action and set a standard for operational resiliency.
While policies can often feel burdensome and detached from reality, these guidelines are reasonable steps that any company across any industry can exercise to improve the resilience of their software systems.
The BoE standard breaks down to these five steps:
Following this process aligns with best practices in architecting resilient systems. Let’s break each of these steps down and discuss how chaos engineering can help.
The operational resilience framework recommends focusing on the services that serve external customers. While internal applications are important for productivity, this customer-first mentality is sound advice for determining a starting place for reliability efforts. While it’s ultimately up to the business to weigh the criticality of the different services they offer, the ones necessary to make payments, retrieve payments, investing or insuring against risks are all recommended priorities.
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LogDNA, a startup that helps DevOps teams dig through their log data to find issues, announced a $25 million Series C investment today along with the promotion of industry vet Tucker Callaway to CEO.
Let’s start with the funding. Emergence Capital led the round with participation from previous investors Initialized Capital and Providence Equity. New investors TI Platform Management, Radianx Capital, Top Tier Capital and Trend Forward Capital also joined the round. Today’s investment brings the total raised to $60 million, according to the company.
Current CEO and co-founder Chris Nguyen says the company provides a centralized way to manage log data for DevOps teams with an eye toward troubleshooting issues and getting applications out faster.
New CEO Callaway, whose background includes executive stints at Chef and Sauce Labs, came on board in January as president and CRO with an eye toward moving him into the top spot when the time was right. Nguyen, who will move to the role of chief strategy officer, says everyone was on board with the move, and he was ready to step back into a more technical role.
“When we closed the latest round of funding and looked at what the journey forward looks like, there was just a lot of trust and confidence from my co-founder, the board of directors, all of the investors on the team that Tucker is the right leader,” Nguyen said.
As Callaway takes over in the midst of the pandemic, the company is in reasonably good shape, with 3,000 customers using the product and a strategic partnership with IBM to provide logging services for IBM Cloud. Having $25 million in additional capital certainly helps, but he sees a company that’s still growing and intends to keep hiring.
As he brings more people on board to lead the company of approximately 100 employees, he says that diversity and inclusion is something he is passionate about and takes very seriously. For starters, he plans to put the entire company through unconscious bias training. They have also hired someone to review their hiring practices to date and they are bringing in a consultant to help them design more diverse and inclusive hiring practices and hold them accountable to that.
The company was a member of the same Y Combinator winter 2015 cohort as GitLab. It actually started out building a marketing technology product, only to realize they had built a powerful logging tool on the back end. That logging tool became the basis for LogDNA .
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It’s the summer of 1858. London. The River Thames is overflowing with the smell of human and industrial waste. The exceptionally hot summer months have exacerbated the problem. But this did not just happen overnight. Failure to upkeep an aging sewer system and a growing population that used it contributed to a powder keg of effluent, bringing about cholera outbreaks and shrouding the city in a smell that would not go away.
To this day, Londoners still speak of the Great Stink. Recurring cholera infections led to the dawn of the field of epidemiology, a subject in which we have all recently become amateur enthusiasts.
Fast forward to 2020 and you’ll see that modern software pipelines face a similar “Great Stink” due, in no small part, to the vast adoption of continuous integration (CI), the practice of merging all developers’ working copies into a shared mainline several times a day, and continuous delivery (CD), the ability to get changes of all types — including new features, configuration changes, bug fixes and experiments — into production, or into the hands of users, safely and quickly in a sustainable way.
While contemporary software failures won’t spread disease or emit the rancid smells of the past, they certainly reek of devastation, rendering billions of dollars lost and millions of developer hours wasted each year.
This kind of waste is antithetical to the intent of CI/CD. Everyone is employing CI/CD to accelerate software delivery; yet the ever-growing backlog of intermittent and sporadic test failures is doing the exact opposite. It’s become a growing sludge that is constantly being fed with failures faster than can be resolved. This backlog must be cleared to get CI/CD pipelines back to their full capabilities.
What value is there in a system that, in an effort to accelerate software delivery, knowingly leaves a backlog of bugs that does the exact opposite? We did not arrive at these practices by accident, and its practitioners are neither lazy nor incompetent so; how did we get here and what can we do to temper modern software development’s Great Stink?
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Technology has dramatically changed over the last decade, and so has how we build and deliver enterprise software.
Ten years ago, “modern computing” was to rely on teams of network admins managing data centers, running one application per server, deploying monolithic services, through waterfall, manual releases managed by QA and release managers.
Today, we have multi and hybrid clouds, serverless services, in continuous integration, running infrastructure-as-code.
SaaS has grown from a nascent 2% of the $450B enterprise software market in 2009, to 23% in 2020 and crossed $100B in revenue. PaaS and IaaS revenue represent another $50B in revenue, expecting to double to $100B by 2022.
With 77% of the enterprise software market — over $350B in annual revenue — still on legacy and on-premise systems, modern SaaS, PaaS and IaaS eating at the legacy market alone can grow the market 3x-4x over the next decade.
As the shift to cloud accelerates across the platform and infrastructure layers, here are four trends starting to emerge that will change how we develop and deliver enterprise software for the next decade.
Companies are building more dynamic, multiplatform, complex infrastructures than ever. We see the “-aaS” of the application, data, runtime and virtualization layers. Modern architectures are forcing extensibility to work with any number of mixed and matched services.
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Axiom, a startup that helps companies deal with their internal data, has secured a new $4 million seed round led by U.K.-based Crane Venture Partners, with participation from LocalGlobe, Fly VC and Mango Capital. Notable angel investors include former Xamarin founder and current GitHub CEO Nat Friedman and Heroku co-founder Adam Wiggins. The company is also emerging from a relative stealth mode to reveal that is has now raised $7 million in funding since it was founded in 2017.
The company says it is also launching with an enterprise-grade solution to manage and analyze machine data “at any scale, across any type of infrastructure.” Axiom gives DevOps teams a cloud-native, enterprise-grade solution to store and query their data all the time in one interface — without the overhead of maintaining and scaling data infrastructure.
DevOps teams have spent a great deal of time and money managing their infrastructure, but often without being able to own and analyze their machine data. Despite all the tools at hand, managing and analyzing critical data has been difficult, slow and resource-intensive, taking up far too much money and time for organizations. This is what Axiom is addressing with its platform to manage machine data and surface insights, more cheaply, they say, than other solutions.
Co-founder and CEO Neil Jagdish Patel told TechCrunch: “DevOps teams are stuck under the pressure of that, because it’s up to them to deliver a solution to that problem. And the solutions that existed are quite, well, they’re very complex. They’re very expensive to run and time-consuming. So with Axiom, our goal is to try and reduce the time to solve data problems, but also allow businesses to store more data to query at whenever they want.”
Why did they work with Crane? “We needed to figure out how enterprise sales work and how to take this product to market in a way that makes sense for the people who need it. We spoke to different investors, but when I sat down with Crane they just understood where we were. They have this razor-sharp focus on how they get you to market and how you make sure your sales process and marketing is a success. It’s been beneficial to us as were three engineers, so you need that,” said Patel.
Commenting, Scott Sage, founder and partner at Crane Venture Partners added: “Neil, Seif and Gord are a proven team that have created successful products that millions of developers use. We are proud to invest in Axiom to allow them to build a business helping DevOps teams turn logging challenges from a resource-intense problem to a business advantage.”
Axiom co-founders Neil Jagdish Patel, Seif Lotfy and Gord Allott previously created Xamarin Insights that enabled developers to monitor and analyse mobile app performance in real time for Xamarin, the open-source cross-platform app development framework. Xamarin was acquired by Microsoft for between $400 and $500 million in 2016. Before working at Xamarin, the co-founders also worked together at Canonical, the private commercial company behind the Ubuntu Project.
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Atlassian today launched a slew of DevOps-centric updates to a variety of its services, ranging from Bitbucket Cloud and Pipelines to Jira and others. While it’s quite a grab-bag of announcements, the overall idea behind them is to make it easier for teams to collaborate across functions as companies adopt DevOps as their development practice of choice.
“I’ve seen a lot of these tech companies go through their agile and DevOps transformations over the years,” Tiffany To, the head of agile and DevOps solutions at Atlassian told me. “Everyone wants the benefits of DevOps, but — we know it — it gets complicated when we mix these teams together, we add all these tools. As we’ve talked with a lot of our users, for them to succeed in DevOps, they actually need a lot more than just the toolset. They have to enable the teams. And so that’s what a lot of these features are focused on.”
As To stressed, the company also worked with several ecosystem partners, for example, to extend the automation features in Jira Software Cloud, which can now also be triggered by commits and pull requests in GitHub, GitLab and other code repositories that are integrated into Jira Software Cloud. “Now you get these really nice integrations for DevOps where we are enabling these developers to not spend time updating the issues,” To noted.
Indeed, a lot of the announcements focus on integrations with third-party tools. This, To said, is meant to allow Atlassian to meet developers where they are. If your code editor of choice is VS Code, for example, you can now try Atlassian’s now VS Code extension, which brings your task like from Jira Software Cloud to the editor, as well as a code review experience and CI/CD tracking from Bitbucket Pipelines.

Also new is the “Your Work” dashboard in Bitbucket Cloud, which can now show you all of your assigned Jira issues, as well as Code Insights in Bitbucket Cloud. Code Insights features integrations with Mabl for test automation, Sentry for monitoring and Snyk for finding security vulnerabilities. These integrations were built on top of an open API, so teams can build their own integrations, too.
“There’s a really important trend to shift left. How do we remove the bugs and the security issues earlier in that dev cycle, because it costs more to fix it later,” said To. “You need to move that whole detection process much earlier in the software lifecycle.”
Jira Service Desk Cloud is getting a new Risk Management Engine that can score the risk of changes and auto-approve low-risk ones, as well as a new change management view to streamline the approval process.
Finally, there is new Opsgenie and Bitbucket Cloud integration that centralizes alerts and promises to filter out the noise, as well as a nice incident investigation dashboard to help teams take a look at the last deployment that happened before the incident occurred.
“The reason why you need all these little features is that as you stitch together a very large number of tools […], there is just lots of these friction points,” said To. “And so there is this balance of, if you bought a single toolchain, all from one vendor, you would have fewer of these friction points, but then you don’t get to choose best of breed. Our mission is to enable you to pick the best tools because it’s not one-size-fits-all.”
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It seems that we are in the middle of a mini acquisition spree for Kubernetes startups, specifically those that can help with Kubernetes security. In the latest development, Venafi, a vendor of certificate and key management for machine-to-machine connections, is acquiring Jetstack, a U.K. startup that helps enterprises migrate and work within Kubernetes and cloud-based ecosystems, which has also been behind the development of cert-manager, a popular, open-source native Kubernetes certificate management controller.
Financial terms of the deal, which is expected to close in June of this year, have not been disclosed, but Jetstack has been working with Venafi to integrate its services and had a strategic investment from Venafi’s Machine Identity Protection Development Fund.
Venafi is part of the so-called “Silicon Slopes” cluster of startups in Utah. It has raised about $190 million from investors that include TCV, Silver Lake and Intel Capital and was last valued at $600 million. That was in 2018, when it raised $100 million, so now it’s likely Venafi is worth more, especially considering its customers include the top five U.S. health insurers, the top five U.S. airlines, the top four credit card issuers, three out of the top four accounting and consulting firms, four of the top five U.S., U.K., Australian and South African banks and four of the top five U.S. retailers.
For the time being, the two organizations will continue to operate separately, and cert-manager — which has hundreds of contributors and millions of downloads — will continue on as before, with a public release of version 1 expected in the June-July time frame.
The deal underscores not just how Kubernetes -based containers have quickly gained momentum and critical mass in the enterprise IT landscape, in particular around digital transformation, but specifically the need to provide better security services around that at speed and at scale. The deal comes just one day after VMware announced that it was acquiring Octarine, another Kubernetes security startup, to fold into Carbon Black (an acquisition it made last year).
“Nowadays, business success depends on how quickly you can respond to the market,” said Matt Barker, CEO and co-founder of Jetstack . “This reality led us to re-think how software is built and Kubernetes has given us the ideal platform to work from. However, putting speed before security is risky. By joining Venafi, Jetstack will give our customers a chance to build fast while acting securely.”
To be clear, Venafi had been offering Kubernetes integrations prior to this — and Venafi and Jetstack have worked together for two years. But acquiring Jetstack will give it direct, in-house expertise to speed up development and deployment of better tools to meet the challenges of a rapidly expanding landscape of machines and applications, all of which require unique certificates to connect securely.
“In the race to virtualize everything, businesses need faster application innovation and better security; both are mandatory,” said Jeff Hudson, CEO of Venafi, in a statement. “Most people see these requirements as opposing forces, but we don’t. We see a massive opportunity for innovation. This acquisition brings together two leaders who are already working together to accelerate the development process while simultaneously securing applications against attack, and there’s a lot more to do. Our mutual customers are urgently asking for more help to solve this problem because they know that speed wins, as long as you don’t crash.”
The crux of the issue is the sheer volume of machines that are being used in computing environments, thanks to the growth of Kubernetes clusters, cloud instances, microservices and more, with each machine requiring a unique identity to connect, communicate and execute securely, Venafi notes, with disruptions or misfires in the system leaving holes for security breaches.
Jetstack’s approach to information security came by way of its expertise in Kubernetes, developing cert-mananger specifically so that its developer customers could easily create and maintain certificates for their networks.
“At Jetstack we help customers realize the benefits of Kubernetes and cloud native infrastructure, and we see transformative results to businesses firsthand,” said Matt Bates, CTO and co-founder of Jetstack, in a statement. “We developed cert-manager to make it easy for developers to scale Kubernetes with consistent, secure, and declared-as-code machine identity protection. The project has been a huge hit with the community and has been adopted far beyond our expectations. Our team is thrilled to join Venafi so we can accelerate our plans to bring machine identity protection to the cloud native stack, grow the community and contribute to a wider range of projects across the ecosystem.” Both Bates and Barker will report to Venafi’s Hudson and join the bigger company’s executive team.
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Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.
What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.
That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.
Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.
Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.
As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.
We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.
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