Dec
03
2019
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AWS launches discounted spot capacity for its Fargate container platform

AWS today quietly brought spot capacity to Fargate, its serverless compute engine for containers that supports both the company’s Elastic Container Service and, now, its Elastic Kubernetes service.

Like spot instances for the EC2 compute platform, Fargate Spot pricing is significantly cheaper, both for storage and compute, than regular Fargate pricing. In return, though, you have to be able to accept the fact that your instance may get terminated when AWS needs additional capacity. While that means Fargate Spot may not be perfect for every workload, there are plenty of applications that can easily handle an interruption.

“Fargate now has on-demand, savings plan, spot,” AWS VP of Compute Services Deepak Singh told me. “If you think about Fargate as a compute layer for, as we call it, serverless compute for containers, you now have the pricing worked out and you now have both orchestrators on top of it.”

He also noted that containers already drive a significant percentage of spot usage on AWS in general, so adding this functionality to Fargate makes a lot of sense (and may save users a few dollars here and there). Pricing, of course, is the major draw here, and an hour of CPU time on Fargate Spot will only cost $0.01245364 (yes, AWS is pretty precise there) compared to $0.04048 for the on-demand price,

With this, AWS is also launching another important new feature: capacity providers. The idea here is to automate capacity provisioning for Fargate and EC2, both of which now offer on-demand and spot instances, after all. You simply write a config file that, for example, says you want to run 70% of your capacity on EC2 and the rest on spot instances. The scheduler will then keep that capacity on spot as instances come and go, and if there are no spot instances available, it will move it to on-demand instances and back to spot once instances are available again.

In the future, you will also be able to mix and match EC2 and Fargate. “You can say, I want some of my services running on EC2 on demand, some running on Fargate on demand, and the rest running on Fargate Spot,” Singh explained. “And the scheduler manages it for you. You squint hard, capacity is capacity. We can attach other capacity providers.” Outposts, AWS’ fully managed service for running AWS services in your data center, could be a capacity provider, for example.

These new features and prices will be officially announced in Thursday’s re:Invent keynote, but the documentation and pricing is already live today.

Dec
03
2019
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AWS speeds up Redshift queries 10x with AQUA

At its re:Invent conference, AWS CEO Andy Jassy today announced the launch of AQUA (the Advanced Query Accelerator) for Amazon Redshift, the company’s data warehousing service. As Jassy noted in his keynote, it’s hard to scale data warehouses when you want to do analytics over that data. At some point, as your data warehouse or lake grows, the data starts overwhelming your network or available compute, even with today’s highspeed networks and chips. So to handle this, AQUA is essentially a hardware-accelerated cache and promises up to 10x better query performance than competing cloud-based data warehouses.

“Think about how much data you have to move over the network to get to your compute,” Jassy said. And if that’s not a problem for a company today, he added, it will likely become one soon, given how much data most enterprises now generate.

With this, Jassy explained, you’re bringing the compute power you need directly to the storage layer. The cache sits on top of Amazon’s standard S3 service and can hence scale out as needed across as many nodes as needed.

AWS designed its own analytics processors to power this service and accelerate the data compression and encryption on the fly.

Unsurprisingly, the service is also 100% compatible with the current version of Redshift.

In addition, AWS also today announced next-generation compute instances for Redshift, the RA3 instances, with 48 vCPUs and 384GiB of memory and up to 64 TB of storage. You can build clusters of these with up to 128 instances.

Dec
02
2019
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CircleCI launches improved AWS support

For about a year now, continuous integration and delivery service CircleCI has offered Orbs, a way to easily reuse commands and integrations with third-party services. Unsurprisingly, some of the most popular Orbs focus on AWS, as that’s where most of the company’s developers are either testing their code or deploying it. Today, right in time for AWS’s annual re:Invent developer conference in Las Vegas, the company announced that it has now added Orb support for the AWS Serverless Application Model (SAM), which makes setting up automated CI/CD platforms for testing and deploying to AWS Lambda significantly easier.

In total, the company says, more than 11,000 organizations started using Orbs since it launched a year ago. Among the AWS-centric Orbs are those for building and updating images for the Amazon Elastic Container Services and the Elastic Container Service for Kubernetes (EKS), for example, as well as AWS CodeDeploy support, an Orb for installing and configuring the AWS command line interface, an Orb for working with the S3 storage service and more.

“We’re just seeing a momentum of more and more companies being ready to adopt [managed services like Lambda, ECS and EKS], so this became really the ideal time to do most of the work with the product team at AWS that manages their serverless ecosystem and to add in this capability to leverage that serverless application model and really have this out of the box CI/CD flow ready for users who wanted to start adding these into to Lambda,” CircleCI VP of business development Tom Trahan told me. “I think when Lambda was in its earlier days, a lot of people would use it and they would use it and not necessarily follow the same software patterns and delivery flow that they might have with their traditional software. As they put more and more into Lambda and are really putting a lot more what I would call ‘production quality code’ out there to leverage. They realize they do want to have that same software delivery capability and discipline for Lambda as well.”

Trahan stressed that he’s still talking about early adopters and companies that started out as cloud-native companies, but these days, this group includes a lot of traditional companies, as well, that are now rapidly going through their own digital transformations.

Nov
26
2019
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New Amazon capabilities put machine learning in reach of more developers

Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.

Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

Nov
25
2019
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AWS expands its IoT services, brings Alexa to devices with only 1MB of RAM

AWS today announced a number of IoT-related updates that, for the most part, aim to make getting started with its IoT services easier, especially for companies that are trying to deploy a large fleet of devices. The marquee announcement, however, is about the Alexa Voice Service, which makes Amazon’s Alex voice assistant available to hardware manufacturers who want to build it into their devices. These manufacturers can now create “Alexa built-in” devices with very low-powered chips and 1MB of RAM.

Until now, you needed at least 100MB of RAM and an ARM Cortex A-class processor. Now, the requirement for Alexa Voice Service integration for AWS IoT Core has come down 1MB and a cheaper Cortex-M processor. With that, chances are you’ll see even more lightbulbs, light switches and other simple, single-purpose devices with Alexa functionality. You obviously can’t run a complex voice-recognition model and decision engine on a device like this, so all of the media retrieval, audio decoding, etc. is done in the cloud. All it needs to be able to do is detect the wake word to start the Alexa functionality, which is a comparably simple model.

“We now offload the vast majority of all of this to the cloud,” AWS IoT VP Dirk Didascalou told me. “So the device can be ultra dumb. The only thing that the device still needs to do is wake word detection. That still needs to be covered on the device.” Didascalou noted that with new, lower-powered processors from NXP and Qualcomm, OEMs can reduce their engineering bill of materials by up to 50 percent, which will only make this capability more attractive to many companies.

Didascalou believes we’ll see manufacturers in all kinds of areas use this new functionality, but most of it will likely be in the consumer space. “It just opens up the what we call the real ambient intelligence and ambient computing space,” he said. “Because now you don’t need to identify where’s my hub — you just speak to your environment and your environment can interact with you. I think that’s a massive step towards this ambient intelligence via Alexa.”

No cloud computing announcement these days would be complete without talking about containers. Today’s container announcement for AWS’ IoT services is that IoT Greengrass, the company’s main platform for extending AWS to edge devices, now offers support for Docker containers. The reason for this is pretty straightforward. The early idea of Greengrass was to have developers write Lambda functions for it. But as Didascalou told me, a lot of companies also wanted to bring legacy and third-party applications to Greengrass devices, as well as those written in languages that are not currently supported by Greengrass. Didascalou noted that this also means you can bring any container from the Docker Hub or any other Docker container registry to Greengrass now, too.

“The idea of Greengrass was, you build an application once. And whether you deploy it to the cloud or at the edge or hybrid, it doesn’t matter, because it’s the same programming model,” he explained. “But very many older applications use containers. And then, of course, you saying, okay, as a company, I don’t necessarily want to rewrite something that works.”

Another notable new feature is Stream Manager for Greengrass. Until now, developers had to cobble together their own solutions for managing data streams from edge devices, using Lambda functions. Now, with this new feature, they don’t have to reinvent the wheel every time they want to build a new solution for connection management and data retention policies, etc., but can instead rely on this new functionality to do that for them. It’s pre-integrated with AWS Kinesis and IoT Analytics, too.

Also new for AWS IoT Greengrass are fleet provisioning, which makes it easier for businesses to quickly set up lots of new devices automatically, as well as secure tunneling for AWS IoT Device Management, which makes it easier for developers to remote access into a device and troubleshoot them. In addition, AWS IoT Core now features configurable endpoints.

Nov
07
2019
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AWS announces new savings plans to reduce complexity of reserved instances

Reserved instances (RIs) have provided a mechanism for companies, which expect to use a certain level of AWS infrastructure resources, to get some cost certainty. But as AWS’ Jeff Barr points out, they are on the complex side. To fix that, the company announced a new method called Savings Plans.

“Today we are launching Savings Plans, a new and flexible discount model that provides you with the same discounts as RIs, in exchange for a commitment to use a specific amount (measured in dollars per hour) of compute power over a one or three year period,” Barr wrote in a blog post announcing the new program.

Amazon charges customers in a couple of ways. First, there is an on-demand price, which is basically the equivalent of the rack rate at a hotel. You are going to pay more for this because you’re walking up and ordering it on the fly.

Most organizations know they are going to need a certain level of resources over a period of time, and in these cases, they can save some money by buying in bulk up front. This gives them cost certainty as an organization, and it helps Amazon because it knows it’s going to have a certain level of usage and can plan accordingly.

While Reserved Instances aren’t going away yet, it sounds like Amazon is trying to steer customers to the new savings plans. “We will continue to sell RIs, but Savings Plans are more flexible and I think many of you will prefer them,” Barr wrote.

The Savings Plans come in two flavors. Compute Savings Plans provide up to 66% savings and are similar to RIs in this regard. The aspect that customers should like is that the savings are broadly applicable across AWS products, and you can even move workloads between regions and maintain the same discounted rate.

The other is an EC2 Instance Savings Plan. With this one, also similar to the reserved instance, you can save up to 72% over the on-demand price, but with this option you are limited to a single region. It does offer a measure of flexibility though, allowing you to select different sizes of the same instance type or even switch operating systems from Windows to Linux without affecting your discount with your region of choice.

You can sign up today through the AWS Cost Explorer.

Oct
15
2019
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Amazon migrates more than 100 consumer services from Oracle to AWS databases

AWS and Oracle love to take shots at each other, but as much as Amazon has knocked Oracle over the years, it was forced to admit that it was in fact a customer. Today in a company blog post, the company announced it was shedding Oracle for AWS databases, and had effectively turned off its final Oracle database.

The move involved 75 petabytes of internal data stored in nearly 7,500 Oracle databases, according to the company. “I am happy to report that this database migration effort is now complete. Amazon’s Consumer business just turned off its final Oracle database (some third-party applications are tightly bound to Oracle and were not migrated),” AWS’s Jeff Barr wrote in the company blog post announcing the migration.

Over the last several years, the company has been working to move off of Oracle databases, but it’s not an easy task to move projects on Amazon scale. Barr wrote there were lots of reasons the company wanted to make the move. “Over the years we realized that we were spending too much time managing and scaling thousands of legacy Oracle databases. Instead of focusing on high-value differentiated work, our database administrators (DBAs) spent a lot of time simply keeping the lights on while transaction rates climbed and the overall amount of stored data mounted,” he wrote.

More than 100 consumer services have been moved to AWS databases, including customer-facing tools like Alexa, Amazon Prime and Twitch, among others. It also moved internal tools like AdTech, its fulfillment system, external payments and ordering. These are not minor matters. They are the heart and soul of Amazon’s operations.

Each team moved the Oracle database to an AWS database service like Amazon DynamoDB, Amazon Aurora, Amazon Relational Database Service (RDS) and Amazon Redshift. Each group was allowed to choose the service they wanted, based on its individual needs and requirements.

Oracle declined to comment on this story.

 

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!”

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