Oct
04
2019
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Annual Extra Crunch members can receive $1,000 in AWS credits

We’re excited to announce a new partnership with Amazon Web Services for annual members of Extra Crunch. Starting today, qualified annual members can receive $1,000 in AWS credits. You also must be a startup founder to claim this Extra Crunch community perk.

AWS is the premier service for your application hosting needs, and we want to make sure our community is well-resourced to build. We understand that hosting and infrastructure costs can be a major hurdle for tech startups, and we’re hoping that this offer will help better support your team.

What’s included in the perk:

  • $1,000 in AWS Promotional Credit valid for 1 year
  • 2 months of AWS Business Support
  • 80 credits for self-paced labs

Applications are processed in 7-10 days, once an application is received. Companies may not be eligible for AWS Promotional Credits if they previously received a similar or greater amount of credit. Companies may be eligible to be “topped up” to a higher credit amount if they previously received a lower credit.

In addition to the AWS community perk, Extra Crunch members also get access to how-tos and guides on company building, intelligence on what’s happening in the startup ecosystem, stories about founders and exits, transcripts from panels at TechCrunch events, discounts on TechCrunch events, no banner ads on TechCrunch.com and more. To see a full list of the types of articles you get with Extra Crunch, head here.

You can sign up for annual Extra Crunch membership here.

Once you are signed up, you’ll receive a welcome email with a link to the AWS offer. If you are already an annual Extra Crunch member, you will receive an email with the offer at some point today. If you are currently a monthly Extra Crunch subscriber and want to upgrade to annual in order to claim this deal, head over to the “my account” section on TechCrunch.com and click the “upgrade” button.

This is one of several new community perks we’ve been working on for Extra Crunch members. Extra Crunch members also get 20% off all TechCrunch event tickets (email extracrunch@techcrunch.com with the event name to receive a discount code for event tickets). You can learn more about our events lineup here. You also can read about our Brex community perk here.

Aug
26
2019
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Nvidia and VMware team up to make GPU virtualization easier

Nvidia today announced that it has been working with VMware to bring its virtual GPU technology (vGPU) to VMware’s vSphere and VMware Cloud on AWS. The company’s core vGPU technology isn’t new, but it now supports server virtualization to enable enterprises to run their hardware-accelerated AI and data science workloads in environments like VMware’s vSphere, using its new vComputeServer technology.

Traditionally (as far as that’s a thing in AI training), GPU-accelerated workloads tend to run on bare metal servers, which were typically managed separately from the rest of a company’s servers.

“With vComputeServer, IT admins can better streamline management of GPU accelerated virtualized servers while retaining existing workflows and lowering overall operational costs,” Nvidia explains in today’s announcement. This also means that businesses will reap the cost benefits of GPU sharing and aggregation, thanks to the improved utilization this technology promises.

Note that vComputeServer works with VMware Sphere, vCenter and vMotion, as well as VMware Cloud. Indeed, the two companies are using the same vComputeServer technology to also bring accelerated GPU services to VMware Cloud on AWS. This allows enterprises to take their containerized applications and from their own data center to the cloud as needed — and then hook into AWS’s other cloud-based technologies.

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“From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line,” said Nvidia founder and CEO Jensen Huang . “Together with VMware, we’re designing the most advanced and highest performing GPU- accelerated hybrid cloud infrastructure to foster innovation across the enterprise.”

Aug
22
2019
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Enterprise software is hot — who would have thought?

Once considered the most boring of topics, enterprise software is now getting infused with such energy that it is arguably the hottest space in tech.

It’s been a long time coming. And it is the developers, software engineers and veteran technologists with deep experience building at-scale technologies who are energizing enterprise software. They have learned to build resilient and secure applications with open-source components through continuous delivery practices that align technical requirements with customer needs. And now they are developing application architectures and tools for at-scale development and management for enterprises to make the same transformation.

“Enterprise had become a dirty word, but there’s a resurgence going on and Enterprise doesn’t just mean big and slow anymore,” said JD Trask, co-founder of Raygun enterprise monitoring software. “I view the modern enterprise as one that expects their software to be as good as consumer software. Fast. Easy to use. Delivers value.”

The shift to scale out computing and the rise of the container ecosystem, driven largely by startups, is disrupting the entire stack, notes Andrew Randall, vice president of business development at Kinvolk.

In advance of TechCrunch’s first enterprise-focused event, TC Sessions: Enterprise, The New Stack examined the commonalities between the numerous enterprise-focused companies who sponsor us. Their experiences help illustrate the forces at play behind the creation of the modern enterprise tech stack. In every case, the founders and CTOs recognize the need for speed and agility, with the ultimate goal of producing software that’s uniquely in line with customer needs.

We’ll explore these topics in more depth at The New Stack pancake breakfast and podcast recording at TC Sessions: Enterprise. Starting at 7:45 a.m. on Sept. 5, we’ll be serving breakfast and hosting a panel discussion on “The People and Technology You Need to Build a Modern Enterprise,” with Sid Sijbrandij, founder and CEO, GitLab, and Frederic Lardinois, enterprise writer and editor, TechCrunch, among others. Questions from the audience are encouraged and rewarded, with a raffle prize awarded at the end.

Traditional virtual machine infrastructure was originally designed to help manage server sprawl for systems-of-record software — not to scale out across a fabric of distributed nodes. The disruptors transforming the historical technology stack view the application, not the hardware, as the main focus of attention. Companies in The New Stack’s sponsor network provide examples of the shift toward software that they aim to inspire in their enterprise customers. Portworx provides persistent state for containers; NS1 offers a DNS platform that orchestrates the delivery internet and enterprise applications; Lightbend combines the scalability and resilience of microservices architecture with the real-time value of streaming data.

“Application development and delivery have changed. Organizations across all industry verticals are looking to leverage new technologies, vendors and topologies in search of better performance, reliability and time to market,” said Kris Beevers, CEO of NS1. “For many, this means embracing the benefits of agile development in multicloud environments or building edge networks to drive maximum velocity.”

Enterprise software startups are delivering that value, while they embody the practices that help them deliver it.

The secrets to speed, agility and customer focus

Speed matters, but only if the end result aligns with customer needs. Faster time to market is often cited as the main driver behind digital transformation in the enterprise. But speed must also be matched by agility and the ability to adapt to customer needs. That means embracing continuous delivery, which Martin Fowler describes as the process that allows for the ability to put software into production at any time, with the workflows and the pipeline to support it.

Continuous delivery (CD) makes it possible to develop software that can adapt quickly, meet customer demands and provide a level of satisfaction with benefits that enhance the value of the business and the overall brand. CD has become a major category in cloud-native technologies, with companies such as CircleCI, CloudBees, Harness and Semaphore all finding their own ways to approach the problems enterprises face as they often struggle with the shift.

“The best-equipped enterprises are those [that] realize that the speed and quality of their software output are integral to their bottom line,” Rob Zuber, CTO of CircleCI, said.

Speed is also in large part why monitoring and observability have held their value and continue to be part of the larger dimension of at-scale application development, delivery and management. Better data collection and analysis, assisted by machine learning and artificial intelligence, allow companies to quickly troubleshoot and respond to customer needs with reduced downtime and tight DevOps feedback loops. Companies in our sponsor network that fit in this space include Raygun for error detection; Humio, which provides observability capabilities; InfluxData with its time-series data platform for monitoring; Epsagon, the monitoring platform for serverless architectures and Tricentis for software testing.

“Customer focus has always been a priority, but the ability to deliver an exceptional experience will now make or break a “modern enterprise,” said Wolfgang Platz, founder of Tricentis, which makes automated software testing tools. “It’s absolutely essential that you’re highly responsive to the user base, constantly engaging with them to add greater value. This close and constant collaboration has always been central to longevity, but now it’s a matter of survival.”

DevOps is a bit overplayed, but it still is the mainstay workflow for cloud-native technologies and critical to achieving engineering speed and agility in a decoupled, cloud-native architecture. However, DevOps is also undergoing its own transformation, buoyed by the increasing automation and transparency allowed through the rise of declarative infrastructure, microservices and serverless technologies. This is cloud-native DevOps. Not a tool or a new methodology, but an evolution of the longstanding practices that further align developers and operations teams — but now also expanding to include security teams (DevSecOps), business teams (BizDevOps) and networking (NetDevOps).

“We are in this constant feedback loop with our customers where, while helping them in their digital transformation journey, we learn a lot and we apply these learnings for our own digital transformation journey,” Francois Dechery, chief strategy officer and co-founder of CloudBees, said. “It includes finding the right balance between developer freedom and risk management. It requires the creation of what we call a continuous everything culture.”

Leveraging open-source components is also core in achieving speed for engineering. Open-source use allows engineering teams to focus on building code that creates or supports the core business value. Startups in this space include Tidelift and open-source security companies such as Capsule8. Organizations in our sponsor portfolio that play roles in the development of at-scale technologies include The Linux Foundation, the Cloud Native Computing Foundation and the Cloud Foundry Foundation.

“Modern enterprises … think critically about what they should be building themselves and what they should be sourcing from somewhere else,” said Chip Childers, CTO of Cloud Foundry Foundation . “Talented engineers are one of the most valuable assets a company can apply to being competitive, and ensuring they have the freedom to focus on differentiation is super important.”

You need great engineering talent, giving them the ability to build secure and reliable systems at scale while also the trust in providing direct access to hardware as a differentiator.

Is the enterprise really ready?

The bleeding edge can bleed too much for the likings of enterprise customers, said James Ford, an analyst and consultant.

“It’s tempting to live by mantras like ‘wow the customer,’ ‘never do what customers want (instead build innovative solutions that solve their need),’ ‘reduce to the max,’ … and many more,” said Bernd Greifeneder, CTO and co-founder of Dynatrace . “But at the end of the day, the point is that technology is here to help with smart answers … so it’s important to marry technical expertise with enterprise customer need, and vice versa.”

How the enterprise adopts new ways of working will affect how startups ultimately fare. The container hype has cooled a bit and technologists have more solid viewpoints about how to build out architecture.

One notable trend to watch: The role of cloud services through projects such as Firecracker. AWS Lambda is built on Firecracker, the open-source virtualization technology, built originally at Amazon Web Services . Firecracker serves as a way to get the speed and density that comes with containers and the hardware isolation and security capabilities that virtualization offers. Startups such as Weaveworks have developed a platform on Firecracker. OpenStack’s Kata containers also use Firecracker.

“Firecracker makes it easier for the enterprise to have secure code,” Ford said. It reduces the surface security issues. “With its minimal footprint, the user has control. It means less features that are misconfigured, which is a major security vulnerability.”

Enterprise startups are hot. How they succeed will determine how well they may provide a uniqueness in the face of the ever-consuming cloud services and at-scale startups that inevitably launch their own services. The answer may be in the middle with purpose-built architectures that use open-source components such as Firecracker to provide the capabilities of containers and the hardware isolation that comes with virtualization.

Hope to see you at TC Sessions: Enterprise. Get there early. We’ll be serving pancakes to start the day. As we like to say, “Come have a short stack with The New Stack!”

Jul
31
2019
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Amazon acquires flash-based cloud storage startup E8 Storage

Amazon has acquired Israeli storage tech startup E8 Storage, as first reported by Reuters, CNBC and Globes and confirmed by TechCrunch. The acquisition will bring the team and technology from E8 in to Amazon’s existing Amazon Web Services center in Tel Aviv, per reports.

E8 Storage’s particular focus was on building storage hardware that employs flash-based memory to deliver faster performance than competing offerings, according to its own claims. How exactly AWS intends to use the company’s talent or assets isn’t yet known, but it clearly lines up with their primary business.

AWS acquisitions this year include TSO Logic, a Vancouver-based startup that optimizes data center workload operating efficiency, and Israel-based CloudEndure, which provides data recovery services in the event of a disaster.

Jul
03
2019
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Capital One CTO George Brady will join us at TC Sessions: Enterprise

When you think of old, giant mainframes that sit in the basement of a giant corporation, still doing the same work they did 30 years ago, chances are you’re thinking about a financial institution. It’s the financial enterprises, though, that are often leading the charge in bringing new technologies and software development practices to their employees and customers. That’s in part because they are in a period of disruption that forces them to become more nimble. Often, this means leaving behind legacy technology and embracing the cloud.

At TC Sessions: Enterprise, which is happening on September 5 in San Francisco, Capital One executive VP in charge of its technology operations, George Brady, will talk about the company’s journey from legacy hardware and software to embracing the cloud and open source, all while working in a highly regulated industry. Indeed, Capital One was among the first companies to embrace the Facebook-led Open Compute project and it’s a member of the Cloud Native Computing Foundation. It’s this transformation at Capital One that Brady is leading.

At our event, Brady will join a number of other distinguished panelists to specifically talk about his company’s journey to the cloud. There, Capital One is using serverless compute, for example, to power its Credit Offers API using AWS’s Lambda service, as well as a number of other cloud technologies.

Before joining Capital One as its CTO in 2014, Brady ran Fidelity Investment’s global enterprise infrastructure team from 2009 to 2014 and served as Goldman Sachs’ head of global business applications infrastructure before that.

Currently, he leads cloud application and platform productization for Capital One. Part of that portfolio is Critical Stack, a secure container orchestration platform for the enterprise. Capital One’s goal with this work is to help companies across industries become more compliant, secure and cost-effective operating in the public cloud.

Early-bird tickets are still on sale for $249; grab yours today before we sell out.

Student tickets are just $75 — grab them here.

Jun
10
2019
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AWS is now making Amazon Personalize available to all customers

Amazon Personalize, first announced during AWS re:Invent last November, is now available to all Amazon Web Services customers. The API enables developers to add custom machine learning models to their apps, including ones for personalized product recommendations, search results and direct marketing, even if they don’t have machine learning experience.

The API processes data using algorithms originally created for Amazon’s own retail business,  but the company says all data will be “kept completely private, owned entirely by the customer.” The service is now available to AWS users in three U.S. regions, East (Ohio), East (North Virginia) and West (Oregon), two Asia Pacific regions (Tokyo and Singapore) and Ireland in the European Union, with more regions to launch soon.

AWS customers who have already added Amazon Personalize to their apps include Yamaha Corporation of America, Subway, Zola and Segment. In Amazon’s press release, Yamaha Corporation of America Director of Information Technology Ishwar Bharbhari said Amazon Personalize “saves us up to 60% of the time needed to set up and tune the infrastructure and algorithms for our machine learning models when compared to building and configuring the environment on our own.”

Amazon Personalize’s pricing model charges five cents per GB of data uploaded to Amazon Personalize and 24 cents per training hour used to train a custom model with their data. Real-time recommendation requests are priced based on how many are uploaded, with discounts for larger orders.

May
09
2019
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AWS remains in firm control of the cloud infrastructure market

It has to be a bit depressing to be in the cloud infrastructure business if your name isn’t Amazon. Sure, there’s a huge, growing market, and the companies behind Amazon are growing even faster. Yet it seems no matter how fast they grow, Amazon remains a dot on the horizon.

It seems inconceivable that AWS can continue to hold sway over such a large market for so long, but as we’ve pointed out before, it has been able to maintain its position through true first-mover advantage. The other players didn’t even show up until several years after Amazon launched its first service in 2006, and they are paying the price for their failure to see the way computing would change the way Amazon did.

They certainly see it now, whether it’s IBM, Microsoft or Google, or Tencent and Alibaba, both of which are growing fast in the China/Asia markets. All of these companies are trying to find the formula to help differentiate themselves from AWS and give them some additional market traction.

Cloud market growth

Interestingly, even though companies have begun to move with increasing urgency to the cloud, the pace of growth slowed a bit in the first quarter to a 42 percent rate, according to data from Synergy Research, but that doesn’t mean the end of this growth cycle is anywhere close.

Apr
25
2019
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AWS expands cloud infrastructure offerings with new AMD EPYC-powered T3a instances

Amazon is always looking for ways to increase the options it offers developers in AWS, and to that end, today it announced a bunch of new AMD EPYC-powered T3a instances. These were originally announced at the end of last year at re:Invent, AWS’s annual customer conference.

Today’s announcement is about making these chips generally available. They have been designed for a specific type of burstable workload, where you might not always need a sustained amount of compute power.

“These instances deliver burstable, cost-effective performance and are a great fit for workloads that do not need high sustained compute power but experience temporary spikes in usage. You get a generous and assured baseline amount of processing power and the ability to transparently scale up to full core performance when you need more processing power, for as long as necessary,” AWS’s Jeff Barr wrote in a blog post.

These instances are built on the AWS Nitro System, Amazon’s custom networking interface hardware that the company has been working on for the last several years. The primary components of this system include the Nitro Card I/O Acceleration, Nitro Security Chip and the Nitro Hypervisor.

Today’s release comes on top of the announcement last year that the company would be releasing EC2 instances powered by Arm-based AWS Graviton Processors, another option for developers looking for a solution for scale-out workloads.

It also comes on the heels of last month’s announcement that it was releasing EC2 M5 and R5 instances, which use lower-cost AMD chips. These are also built on top of the Nitro System.

The EPCY processors are available starting today in seven sizes in your choice of spot instances, reserved instances or on-demand, as needed. They are available in US East in northern Virginia, US West in Oregon, Europe in Ireland, US East in Ohio and Asia-Pacific in Singapore.

Apr
24
2019
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Docker developers can now build Arm containers on their desktops

Docker and Arm today announced a major new partnership that will see the two companies collaborate in bringing improved support for the Arm platform to Docker’s tools.

The main idea here is to make it easy for Docker developers to build their applications for the Arm platform right from their x86 desktops and then deploy them to the cloud (including the Arm-based AWS EC2 A1 instances), edge and IoT devices. Developers will be able to build their containers for Arm just like they do today, without the need for any cross-compilation.

This new capability, which will work for applications written in JavaScript/Node.js, Python, Java, C++, Ruby, .NET core, Go, Rust and PHP, will become available as a tech preview next week, when Docker hosts its annual North American developer conference in San Francisco.

Typically, developers would have to build the containers they want to run on the Arm platform on an Arm-based server. With this system, which is the first result of this new partnership, Docker essentially emulates an Arm chip on the PC for building these images.

“Overnight, the 2 million Docker developers that are out there can use the Docker commands they already know and become Arm developers,” Docker EVP of Strategic Alliances David Messina told me. “Docker, just like we’ve done many times over, has simplified and streamlined processes and made them simpler and accessible to developers. And in this case, we’re making x86 developers on their laptops Arm developers overnight.”

Given that cloud-based Arm servers like Amazon’s A1 instances are often significantly cheaper than x86 machines, users can achieve some immediate cost benefits by using this new system and running their containers on Arm.

For Docker, this partnership opens up new opportunities, especially in areas where Arm chips are already strong, including edge and IoT scenarios. Arm, similarly, is interested in strengthening its developer ecosystem by making it easier to develop for its platform. The easier it is to build apps for the platform, the more likely developers are to then run them on servers that feature chips from Arm’s partners.

“Arm’s perspective on the infrastructure really spans all the way from the endpoint, all the way through the edge to the cloud data center, because we are one of the few companies that have a presence all the way through that entire path,” Mohamed Awad, Arm’s VP of Marketing, Infrastructure Line of Business, said. “It’s that perspective that drove us to make sure that we engage Docker in a meaningful way and have a meaningful relationship with them. We are seeing compute and the infrastructure sort of transforming itself right now from the old model of centralized compute, general purpose architecture, to a more distributed and more heterogeneous compute system.”

Developers, however, Awad rightly noted, don’t want to have to deal with this complexity, yet they also increasingly need to ensure that their applications run on a wide variety of platforms and that they can move them around as needed. “For us, this is about enabling developers and freeing them from lock-in on any particular area and allowing them to choose the right compute for the right job that is the most efficient for them,” Awad said.

Messina noted that the promise of Docker has long been to remove the dependence of applications from the infrastructure on which they run. Adding Arm support simply extends this promise to an additional platform. He also stressed that the work on this was driven by the company’s enterprise customers. These are the users who have already set up their systems for cloud-native development with Docker’s tools — at least for their x86 development. Those customers are now looking at developing for their edge devices, too, and that often means developing for Arm-based devices.

Awad and Messina both stressed that developers really don’t have to learn anything new to make this work. All of the usual Docker commands will just work.

Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

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