At AWS re:Invent today, Andy Jassy announced DevOps Guru, a new tool for DevOps teams to help the operations side find issues that could be having an impact on an application performance. Consider it like the sibling of CodeGuru, the service the company announced last year to find issues in your code before you deploy.
It works in a similar fashion using machine learning to find issues on the operations side of the equation. “I’m excited to launch a new service today called Amazon DevOps Guru, which is a new service that uses machine learning to identify operational issues long before they impact customers,” Jassy said today.
The way it works is that it collects and analyzes data from application metrics, logs, and events “to identify behavior that deviates from normal operational patterns,” the company explained in the blog post announcing the new service.
This service essentially gives AWS a product that would be competing with companies like Sumo Logic, DataDog or Splunk by providing deep operational insight on problems that could be having an impact on your application such as misconfigurations or resources that are over capacity.
When it finds a problem, the service can send an SMS, Slack message or other communication to the team and provides recommendations on how to fix the problem as quickly as possible.
What’s more, you pay for the data analyzed by the service, rather than a monthly fee. The company says this means that there is no upfront cost or commitment involved.
The term ‘DevOps’ has been rendered meaningless and developers still don’t have access to the right tools to put the overall idea into practice, the team behind DevOps startup OpsLevel argues. The company, which was co-founded by John Laban and Kenneth Rose, two of PagerDuty’s earliest employees, today announced that it has raised a $5 million seed funding round, led by Vertex Ventures. S28 Capital, Webb Investment Network and Union Capital also participated in this round, as well as a number of angels, including the three co-founders of PagerDuty .
“[PagerDuty] was an important part of the DevOps movement. Getting engineers on call was really important for DevOps, but on-call and getting paged about incidents and things, it’s very reactive in nature. It’s all about fixing incidents as quickly as possible. Ken [Rose] and I saw an opportunity to help companies take a more proactive stance. Nobody really wants to have any downtime or any security breaches in the first place. They want to prevent them before they happen.”
With that mission in mind, the team set out to bring engineering organizations back to the roots of DevOps by giving those teams ownership over their services and creating what Rose called a “you build it, you own it” culture. Service ownership, he noted, is something the team regularly sees companies struggle with. When teams move to microservices or even serverless architectures for their systems, it quickly becomes unclear who owns what and as a result, you end up with orphaned services that nobody is maintaining. The natural result of that is security and reliability issues. And at the same time, because nobody knows which systems already exist, other teams reinvent the wheel and rebuild the same service to solve their own problems.
“We’ve underinvested in tools to make DevOps actually work,” the team says in today’s announcement. “There’s a lot we still need to build to help engineering teams adopt service ownership and unlock the full power of DevOps.”
So at the core of OpsLevel is what the team calls a “service ownership platform,” starting with a catalog of the services that an engineering organization is currently running.
“What we’re trying to do is take back the meaning of DevOps,” said Laban. “We believe it’s been rendered meaningless and we wanted to refocus it on service ownership. We’re going to be investing heavily on building out our product, and then working with our customers to get them to really own their services and get really down to solving that problem.”
Among the companies OpsLevel is already working with are Segment, Zapier, Convoy and Under Armour. As the team noted, its service becomes most useful once a company runs somewhere around 20 or 30 different services. Before that, a wiki or spreadsheet is often enough to manage them, but at that point, those systems tend to break.
OpsLevel gives them different onramps to start cataloging their services. If they prefer to use a ‘config-as-code’ approach, they can use those YAML files as part of their existing Git workflows. But OpsLevel offers APIs that teams can plug into their various systems if they already have existing service creating workflows.
The company’s funding round closed in late September. The pandemic, the team said, didn’t really hinder its fundraising efforts, something I’ve lately heard from a lot of companies (though the ones I talk obviously to tend to be the ones that recently raised money).
“The reason why [we raised] is because we wanted to really invest in building out our product,” Laban said. “We’ve been getting this traction with our customers and we really wanted to double down and build out a lot of product and invest into our go-to-market team as well and really wanted to accelerate things.”
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
Render, the winner of our Disrupt SF 2019 Startup Battlefield, today announced that it has added another $4.5 million onto its existing seed funding round, bringing total investment into the company to $6.75 million.
The round was led by General Catalyst, with participation from previous investors South Park Commons Fund and a group of angels that includes Lee Fixel, Elad Gil and GitHub CTO (and former VP of Engineering at Heroku) Jason Warner.
The company, which describes itself as a “Zero DevOps alternative to AWS, Azure and Google Cloud,” originally raised a $2.25 million seed round in April 2019, but it got a lot of inbound interest after winning the Disrupt Battlefield. In the end, though, the team decided to simply raise more money from its existing investors.
“We spoke to a bunch of people after Disrupt, including Ashton Kutcher’s firm, because he was one of the judges,” Render co-founder and CEO Anurag Goel explained. “In the end, we decided that we would just raise more money from our existing investors because we like them and it helped us get a better deal from our existing investors. And they were all super interested in continuing to invest.”
What makes Render stand out is that it fulfills many of the promises of Heroku and maybe Google Cloud’s App Engine. You simply tell it what kind of service you are going to deploy and it handles the deployment and manages the infrastructure for you.
“Our customers are all people who are writing code. And they just want to deploy this code really easily without having to worry about servers, or maintenance, or depending on DevOps teams — or, in many cases, hiring DevOps teams,” Goel said. “DevOps engineers are extremely expensive to hire and extremely hard to find, especially good ones. Our goal is to eliminate all of that work that DevOps people do at every company, because it’s very similar at every company.”
One new feature the company is launching today is preview environments. You can think of them as disposable staging or development environments that developers can spin up to test their code — and Render promises that the testing environment will look the same as your production environment (or you can specify changes, too). Developers can then test their updates collaboratively with QA or their product and sales teams in this environment.
Development teams on Render specify their infrastructure environments in a YAML file and turning on these new preview environments is as easy as setting a flag in that file.
“Once they do that, then for every pull request — because we’re integrated with GitHub and GitLab — we automatically spin up a copy of that environment. That can include anything you have in production, or things like a Redis instance, or managed Postgres database, or Elasticsearch instance, or obviously APIs and web services and static sites,” Goel said. Every time you push a change to that branch or pull request, the environment is automatically updated, too. Once the pull request is closed or merged, Render destroys the environment automatically.
The company will use the new funding to grow its team and build out its service. The plan, Goel tells me, is to raise a larger Series A round next year.
Contrast, a developer-centric application security company with customers that include Liberty Mutual Insurance, NTT Data, AXA and Bandwidth, today announced the launch of its security observability platform. The idea here is to offer developers a single pane of glass to manage an application’s security across its lifecycle, combined with real-time analysis and reporting, as well as remediation tools.
“Every line of code that’s happening increases the risk to a business if it’s not secure,” said Contrast CEO and chairman Alan Nauman. “We’re focused on securing all that code that businesses are writing for both automation and digital transformation.”
Over the course of the last few years, the well-funded company, which raised a $65 million Series D round last year, launched numerous security tools that cover a wide range of use cases from automated penetration testing to cloud application security and now DevOps — and this new platform is meant to tie them all together.
DevOps, the company argues, is really what necessitates a platform like this, given that developers now push more code into production than ever — and the onus of ensuring that this code is secure is now also often on that.
Traditionally, Nauman argues, security services focused on the code itself and looking at traffic.
“We think at the application layer, the same principles of observability apply that have been used in the IT infrastructure space,” he said. “Specifically, we do instrumentation of the code and we weave security sensors into the code as it’s being developed and are looking for vulnerabilities and observing running code. […] Our view is: the world’s most complex systems are best when instrumented, whether it’s an airplane, a spacecraft, an IT infrastructure. We think the same is true for code. So our breakthrough is applying instrumentation to code and observing for security vulnerabilities.”
With this new platform, Contrast is aggregating information from its existing systems into a single dashboard. And while Contrast observes the code throughout its lifecycle, it also scans for vulnerabilities whenever a developers check code into the CI/CD pipeline, thanks to integrations with most of the standard tools like Jenkins. It’s worth noting that the service also scans for vulnerabilities in open-source libraries. Once deployed, Contrast’s new platform keeps an eye on the data that runs through the various APIs and systems the application connects to and scans for potential security issues there as well.
The platform currently supports all of the large cloud providers like AWS, Azure and Google Cloud, and languages and frameworks like Java, Python, .NET and Ruby.
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.
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 being 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.
Since 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.
Founded by longtime developers and Georgia Institute of Technology alumni, Ken Ahrens, Matthew LeRay and Nate Lee had known each other for roughly twenty years before making the jump to working together.
A circuitous path of interconnecting programming jobs in the devops and monitoring space led the three men to realize that there was an opportunity to address one of the main struggles new programmers now face — making sure that updates to api integrations in a containerized programming world don’t wind up breaking apps or services.
“We were helping to solve incident outages and incidents that would cause downtime,” said Lee. “It’s hard to ensure the quality between all of these connection points [between applications]. And these connection points are growing as people add apis and containers. We said, ‘How about we solve this space? How could we preempt all of this and ensure maintaining release velocity with scalable automation?'”
Typically companies release new updates to code in a phased approach or in a test environment to ensure that they’re not going to break anything. Speedscale proposes test automation using real traffic so that developers can accelerate the release time.
“They want to change very frequently,” said Ahrens, speaking about the development life cycle. “Most of the changes are great, but every once in a while they make a change and break part of the system. The state of the art is to wait for it to be broken and get someone to fix it quickly.”
The pitch SpeedScale makes to developers is that its service can give coders the ability to see the problems before the release. They automate the creation of the staging environment, automation suite and orchestration to create that environment.
“One of the big things for me was when I saw the rise of Kubernetes was what’s really happening is that engineering leaders have been able to give more autonomy to developers, but no one has come up with a great way to validate and I really think that Speedscale can solve that problem.”
The Atlanta-based company, which only just graduated from Y Combinator a few months ago, is currently in a closed alpha with select pilot partners, according to LeRay. And the nine month-old company has raised $2.2 million from investors including Sierra Ventures from the Bay Area and Atlanta’s own Tech Square Ventures to grow the business.
“Apis are a huge market,” Ahrens said of the potential opportunity for the company. “there’s 11 million developers who develop against apis… We think the addressable market for us is in the billions.”
Diffblue, a spin-out from Oxford University, uses machine learning to help developers automatically create unit tests for their Java code. Since few developers enjoy writing unit tests to ensure that their code works as expected, increased automation doesn’t just help developers focus on writing the code that actually makes a difference but also lead to code with fewer bugs. Current Diffblue customers include the likes of Goldman Sachs and AWS.
So far, Diffblue only offered its service through a paid — and pricey — subscription. Today, however, the company also launched its free community edition, Diffblue Cover: Community Edition, which doesn’t feature all of the enterprise features in its paid versions, but still offers an IntelliJ plugin and the same AI-generated unit tests as the paid editions.
The company also plans to launch a new lower cost ‘individual’ plan for Diffblue Cover soon, starting at $120 per month. This plan will offer access to support and other advanced features as well.
At its core, Diffblue uses unsupervised learning to build these unit tests. “What we’re doing is unique in the sense that there have been tools before that use what’s called static analysis,” Diffblue CEO Mathew Loge, who joined the company about a year ago, explained. “They look at the program and they basically understand the path through the program and try and work backwards from the path. So if the path gets to this point, what inputs do we need to put into the program in order to get here?” That approach has its limitations, though, which Diffblue’s reinforcement learning method aims to get around.
Once the process has run its course, Diffblue provides developers with readable tests. That’s important, Loge stressed, because if a test fails and a developer can’t figure out what happened, it’s virtually impossible for the developer to fix the issue. That’s something the team learning the hard way, as early version so Diffblue used a very aggressive algorithm that provided great test coverage (the key metric for unit tests), but made it very hard for developers to figure out what was happening.
With the community edition, which doesn’t offer the command-line interface (CLI) of Diffblue’s paid editions, developers can write their code in IntelliJ as before and then simply click a button to have Diffblue write the tests for that code.
“The Community Edition is designed to be very accessible. It is literally one click in the IDE and you get your tests. The CLI version is more sophisticated and it covers more cases and solves for teams and large deployments inside of an organization,” Loge explained.
Diffblue has actually been around for a bit. The company raised a $22 million Series A round led by Goldman Sachs and with participation from Oxford Sciences Innovation and the Oxford Technology and Innovations Fund in 2017. You obviously don’t raise that kind of money to focus only on unit tests for Java code. Besides support for more language, unit tests are just the first step in the company’s overall goal of automating more of the programming process with the help of AI.
“We started with testing because it’s an important and urgent problem, especially with the impact that it has on DevOps and the adoption of more rapid software cycles,” Loge said. The next obvious step is to then take a similar approach to automatically fixing bugs — and especially security bugs — in code as well.
“The idea is that there are these steppingstones to machines writing more and more code,” he said. “And also, frankly, it’s a way of getting developers used to that. Because developer acceptance is a crucial part of making this successful.”