cloud computing
Auto Added by WPeMatico
Pixie Labs raises $9.15M Series A round for its Kubernetes observability platform
Pixie, a startup that provides developers with tools to get observability into their Kubernetes-native applications, today announced that it has raised a $9.15 million Series A round led by Benchmark, with participation from GV. In addition, the company also today said that its service is now available as a public beta.
The company was co-founded by Zain Asgar (CEO), a former Google engineer working on Google AI and adjunct professor at Stanford, and Ishan Mukherjee (CPO), who led Apple’s Siri Knowledge Graph product team and also previously worked on Amazon’s Robotics efforts. Asgar had originally joined Benchmark to work on developer tools for machine learning. Over time, the idea changed to using machine learning to power tools to help developers manage large-scale deployments instead.
“We saw data systems, this move to the edge, and we felt like this old cloud 1.0 model of manually collecting data and shipping it to databases in the cloud seems pretty inefficient,” Mukherjee explained. “And the other part was: I was on call. I got gray hair and all that stuff. We felt like we could build this new generation of developer tools and get to Michael Jordan’s vision of intelligent augmentation, which is giving creatives tools where they can be a lot more productive.”
Image Credits: Pixie
The team argues that most competing monitoring and observability systems focus on operators and IT teams — and often involve a long manual setup process. But Pixie wants to automate most of this manual process and build a tool that developers want to use.
Pixie runs inside a developer’s Kubernetes platform and developers get instant and automatic visibility into their production environments. With Pixie, which the team is making available as a freemium SaaS product, there is no instrumentation to install. Instead, the team uses relatively new Linux kernel techniques like eBPF to collect data right at the source.
“One of the really cool things about this is that we can deploy Pixie in about a minute and you’ll instantly get data,” said Asgar. “Our goal here is that this really helps you when there are cases where you don’t want your business logic to be full of monitoring code, especially if you forget something — when you have an outage.”
Image Credits: Pixie
At the core of the developer experience is what the company calls “Pixie scripts.” Using a Python-like language (PxL), developers can codify their debugging workflows. The company’s system already features a number of scripts written by the team itself and the community at large. But as Asgar noted, not every user will write scripts. “The way scripts work, it’s supposed to capture human knowledge in that problem. We don’t expect the average user — or even the way-above-average developer — ever to touch a script or write one. They’re just going to use it in a specific scenario,” he explained.
Looking ahead, the team plans to make these scripts and the scripting language more robust and usable to allow developers to go from passively monitoring their systems to building scripts that can actively take actions on their clusters based on the monitoring data the system collects.
“Zain and Ishan’s provocative idea was to move software monitoring to the source,” said Eric Vishria, general partner at Benchmark. “Pixie enables engineering teams to fundamentally rethink their monitoring strategy as it presents a vision of the future where we detect anomalous behavior and make operational decisions inside the infrastructure layer itself. This allows companies of all sizes to monitor their digital experiences in a more responsive, cost-effective and scalable manner.”
Powered by WPeMatico
Microsoft challenges Twilio with the launch of Azure Communication Services
Microsoft today announced the launch of Azure Communication Services, a new set of features in its cloud that enable developers to add voice and video calling, chat and text messages to their apps, as well as old-school telephony.
The company describes the new set of services as the “first fully managed communication platform offering from a major cloud provider,” and that seems right, given that Google and AWS offer some of these features, including the AWS notification service, for example, but not as part of a cohesive communication service. Indeed, it seems Azure Communication Service is more of a competitor to the core features of Twilio or up-and-coming MessageBird.
Over the course of the last few years, Microsoft has built up a lot of experience in this area, in large parts thanks to the success of its Teams service. Unsurprisingly, that’s something Microsoft is also playing up in its announcement.
“Azure Communication Services is built natively on top a global, reliable cloud — Azure. Businesses can confidently build and deploy on the same low latency global communication network used by Microsoft Teams to support over 5 billion meeting minutes in a single day,” writes Scott Van Vliet, corporate vice president for Intelligent Communication at the company.
Microsoft also stresses that it offers a set of additional smart services that developers can tap into to build out their communication services, including its translation tools, for example. The company also notes that its services are encrypted to meet HIPPA and GDPR standards.
Like similar services, developers access the various capabilities through a set of new APIs and SDKs.
As for the core services, the capabilities here are pretty much what you’d expect. There’s voice and video calling (and the ability to shift between them). There’s support for chat and, starting in October, users will also be able to send text messages. Microsoft says developers will be able to send these to users anywhere, with Microsoft positioning it as a global service.
Provisioning phone numbers, too, is part of the services and developers will be able to provision those for in-bound and out-bound calls, port existing numbers, request new ones and — most importantly for contact-center users — integrate them with existing on-premises equipment and carrier networks.
“Our goal is to meet businesses where they are and provide solutions to help them be resilient and move their business forward in today’s market,” writes Van Vliet. “We see rich communication experiences – enabled by voice, video, chat, and SMS – continuing to be an integral part in how businesses connect with their customers across devices and platforms.”
Powered by WPeMatico
Microsoft brings data services to its Arc multi-cloud management service
Microsoft today launched a major update to its Arc multi-cloud service that allows Azure customers to run and manage workloads across clouds — including those of Microsoft’s competitors — and their on-premises data centers. First announced at Microsoft Ignite in 2019, Arc was always meant to not just help users manage their servers but also allow them to run data services like Azure SQL and Azure Database for PostgreSQL, close to where their data sits.
Today, the company is making good on this promise with the preview launch of Azure Arc-enabled data services with support for, as expected, Azure SQL and Azure Database for PostgreSQL.
In addition, Microsoft is making the core feature of Arc, Arc-enabled servers, generally available. These are the tools at the core of the service that allow enterprises that use the standard Azure Portal to manage and monitor their Windows and Linux servers across their multi-cloud and edge environments.
Image Credits: Microsoft
“We’ve always known that enterprises are looking to unlock the agility of the cloud — they love the app model, they love the business model — while balancing a need to maintain certain applications and workloads on premises,” Rohan Kumar, Microsoft’s corporate VP for Azure Data said. “A lot of customers actually have a multi-cloud strategy. In some cases, they need to keep the data specifically for regulatory compliance. And in many cases, they want to maximize their existing investments. They’ve spent a lot of CapEx.”
As Kumar stressed, Microsoft wants to meet customers where they are, without forcing them to adopt a container architecture, for example, or replace their specialized engineered appliances to use Arc.
“Hybrid is really [about] providing that flexible choice to our customers, meeting them where they are, and not prescribing a solution,” he said.
He admitted that this approach makes engineering the solution more difficult, but the team decided the baseline should be a container endpoint and nothing more. And for the most part, Microsoft packaged up the tools its own engineers were already using to run Azure services on the company’s own infrastructure to manage these services in a multi-cloud environment.
“In hindsight, it was a little challenging at the beginning, because, you can imagine, when we initially built them, we didn’t imagine that we’ll be packaging them like this. But it’s a very modern design point,” Kumar said. But the result is that supporting customers is now relatively easy because it’s so similar to what the team does in Azure, too.
Kumar noted that one of the selling points for the Azure Data Services is also that the version of Azure SQL is essentially evergreen, allowing them to stop worrying about SQL Server licensing and end-of-life support questions.
Powered by WPeMatico
Pure Storage acquires data service platform Portworx for $370M
Pure Storage, the public enterprise data storage company, today announced that it has acquired Portworx, a well-funded startup that provides a cloud-native storage and data-management platform based on Kubernetes, for $370 million in cash. This marks Pure Storage’s largest acquisition to date and shows how important this market for multicloud data services has become.
Current Portworx enterprise customers include the likes of Carrefour, Comcast, GE Digital, Kroger, Lufthansa, and T-Mobile. At the core of the service is its ability to help users migrate their data and create backups. It creates a storage layer that allows developers to then access that data, no matter where it resides.
Pure Storage will use Portworx’s technology to expand its hybrid and multicloud services and provide Kubernetes -based data services across clouds.
Image Credits: Portworx
“I’m tremendously proud of what we’ve built at Portworx: An unparalleled data services platform for customers running mission-critical applications in hybrid and multicloud environments,” said Portworx CEO Murli Thirumale. “The traction and growth we see in our business daily shows that containers and Kubernetes are fundamental to the next-generation application architecture and thus competitiveness. We are excited for the accelerated growth and customer impact we will be able to achieve as a part of Pure.”
When the company raised its Series C round last year, Thirumale told me that Portworx had expanded its customer base by over 100% and its bookings increased by 376 from 2018 to 2019.
“As forward-thinking enterprises adopt cloud-native strategies to advance their business, we are thrilled to have the Portworx team and their groundbreaking technology joining us at Pure to expand our success in delivering multicloud data services for Kubernetes,” said Charles Giancarlo, chairman and CEO of Pure Storage. “This acquisition marks a significant milestone in expanding our Modern Data Experience to cover traditional and cloud native applications alike.”
Powered by WPeMatico
Latent AI makes edge AI workloads more efficient
Latent AI, a startup that was spun out of SRI International, makes it easier to run AI workloads at the edge by dynamically managing workloads as necessary.
Using its proprietary compression and compilation process, Latent AI promises to compress library files by 10x and run them with 5x lower latency than other systems, all while using less power thanks to its new adaptive AI technology, which the company is launching as part of its appearance in the TechCrunch Disrupt Battlefield competition today.
Founded by CEO Jags Kandasamy and CTO Sek Chai, the company has already raised a $6.5 million seed round led by Steve Jurvetson of Future Ventures and followed by Autotech Ventures .
Before starting Latent AI, Kandasamy sold his previous startup OtoSense to Analog Devices (in addition to managing HPE Mid-Market Security business before that). OtoSense used data from sound and vibration sensors for predictive maintenance use cases. Before its sale, the company worked with the likes of Delta Airlines and Airbus.
Image Credits: Latent AI
In some ways, Latent AI picks up some of this work and marries it with IP from SRI International .
“With OtoSense, I had already done some edge work,” Kandasamy said. “We had moved the audio recognition part out of the cloud. We did the learning in the cloud, but the recognition was done in the edge device and we had to convert quickly and get it down. Our bill in the first few months made us move that way. You couldn’t be streaming data over LTE or 3G for too long.”
At SRI, Chai worked on a project that looked at how to best manage power for flying objects where, if you have a single source of power, the system could intelligently allocate resources for either powering the flight or running the onboard compute workloads, mostly for surveillance, and then switch between them as needed. Most of the time, in a surveillance use case, nothing happens. And while that’s the case, you don’t need to compute every frame you see.
“We took that and we made it into a tool and a platform so that you can apply it to all sorts of use cases, from voice to vision to segmentation to time series stuff,” Kandasamy explained.
What’s important to note here is that the company offers the various components of what it calls the Latent AI Efficient Inference Platform (LEIP) as standalone modules or as a fully integrated system. The compressor and compiler are the first two of these and what the company is launching today is LEIP Adapt, the part of the system that manages the dynamic AI workloads Kandasamy described above.
Image Credits: Latent AI
In practical terms, the use case for LEIP Adapt is that your battery-powered smart doorbell, for example, can run in a low-powered mode for a long time, waiting for something to happen. Then, when somebody arrives at your door, the camera wakes up to run a larger model — maybe even on the doorbell’s base station that is plugged into power — to do image recognition. And if a whole group of people arrives at ones (which isn’t likely right now, but maybe next year, after the pandemic is under control), the system can offload the workload to the cloud as needed.
Kandasamy tells me that the interest in the technology has been “tremendous.” Given his previous experience and the network of SRI International, it’s maybe no surprise that Latent AI is getting a lot of interest from the automotive industry, but Kandasamy also noted that the company is working with consumer companies, including a camera and a hearing aid maker.
The company is also working with a major telco company that is looking at Latent AI as part of its AI orchestration platform and a large CDN provider to help them run AI workloads on a JavaScript backend.
Powered by WPeMatico
Google Cloud launches its Business Application Platform based on Apigee and AppSheet
Unlike some of its competitors, Google Cloud has recently started emphasizing how its large lineup of different services can be combined to solve common business problems. Instead of trying to sell individual services, Google is focusing on solutions and the latest effort here is what it calls its Business Application Platform, which combines the API management capabilities of Apigee with the no-code application development platform of AppSheet, which Google acquired earlier this year.
As part of this process, Google is also launching a number of new features for both services today. The company is launching the beta of a new API Gateway, built on top of the open-source Envoy project, for example. This is a fully managed service that is meant to make it easier for developers to secure and manage their API across Google’s cloud computing services and serverless offerings like Cloud Functions and Cloud Run. The new gateway, which has been in alpha for a while now, offers all the standard features you’d expect, including authentication, key validation and rate limiting.
As for its low-code service AppSheet, the Google Cloud team is now making it easier to bring in data from third-party applications thanks to the general availability to Apigee as a data source for the service. AppSheet already supported standard sources like MySQL, Salesforce and G Suite, but this new feature adds a lot of flexibility to the service.
With more data comes more complexity, so AppSheet is also launching new tools for automating processes inside the service today, thanks to the early access launch of AppSheet Automation. Like the rest of AppSheet, the promise here is that developers won’t have to write any code. Instead, AppSheet Automation provides a visual interface, that, according to Google, “provides contextual suggestions based on natural language inputs.”
“We are confident the new category of business application platforms will help empower both technical and line of business developers with the core ability to create and extend applications, build and automate workflows, and connect and modernize applications,” Google notes in today’s announcement. And indeed, this looks like a smart way to combine the no-code environment of AppSheet with the power of Apigee .
Powered by WPeMatico
Suse contributes EiriniX to the Cloud Foundry Foundation
Suse today announced that it has contributed EiriniX, a framework for building extensions for Eirini, a technology that brings support for Kubernetes-based container orchestration to the Cloud Foundry platform-as-a-service project.
About a year ago, Suse also contributed the KubeCF project to the foundation, which itself allows the Cloud Foundry Application Runtime — the core of Cloud Foundry — to run on top of Kubernetes.
Image Credits: Suse
“At Suse we are developing upstream first as much as possible,” said Thomas Di Giacomo, president of Engineering and Innovation at Suse. “So, after experiencing the value of contributing KubeCF to the Foundation earlier this year, we decided it would be beneficial to both the Cloud Foundry community and the EiriniX team to do it again. We have seen an uptick in contributions to and usage of KubeCF since it became a Foundation project, indicating that more organizations are investing developer time into the upstream. Contributing EiriniX to the Foundation is a surefire way to get the broader community involved.”
Suse first demonstrated EiriniX a year ago. The tool implements features like the ability to SSH into a container and debug it, for example, or to use alternative logging solutions for KubeCF.
“There is significant value in contributing this project to the Foundation, as it ensures that other project teams looking for a similar solution to creating Extensions around Eirini will not reinvent the wheel,” said Chip Childers, executive director, Cloud Foundry Foundation. “Now that EiriniX exists within the Foundation, developers can take full advantage of its library of add-ons to Eirini and modify core features of Cloud Foundry. I’m excited to see all of the use cases for this project that have not yet been invented.”
Powered by WPeMatico
Adaptive Shield raises $4M for its SaaS security platform
Adaptive Shield, a Tel Aviv-based security startup, is coming out of stealth today and announcing its $4 million seed round led by Vertex Ventures Israel. The company’s platform helps businesses protect their SaaS applications by regularly scanning their various setting for security issues.
The company’s co-founders met in the Israeli Defense Forces, where they were trained on cybersecurity, and then worked at a number of other security companies before starting their own venture. Adaptive Shield CEO Maor Bin, who previously led cloud research at Proofpoint, told me the team decided to look at SaaS security because they believe this is an urgent problem few other companies are addressing.
Pictured is a representative sample of nine apps being monitored by the Adaptive Shield platform, including the total score of each application, affected categories and affected security frameworks and standards. (Image Credits: Adaptive Shield)
“When you look at the problems that are out there — you want to solve something that is critical, that is urgent,” he said. “And what’s more critical than business applications? All the information is out there and every day, we see people moving their on-prem infrastructure into the cloud.”
Bin argues that as companies adopt a large variety of SaaS applications, all with their own security settings and user privileges, security teams are often either overwhelmed or simply not focused on these SaaS tools because they aren’t the system owners and may not even have access to them.
“Every enterprise today is heavily using SaaS services without addressing the associated and ever-changing security risks,” says Emanuel Timor, general partner at Vertex Ventures Israel . “We are impressed by the vision Adaptive Shield has to elegantly solve this complex problem and by the level of interest and fast adoption of its solution by customers.”
Onboarding is pretty easy, as Bin showed me, and typically involves setting up a user in the SaaS app and then logging into a given service through Adaptive Shield. Currently, the company supports most of the standard SaaS enterprise applications you would expect, including GitHub, Office 365, Salesforce, Slack, SuccessFactors and Zoom.
“I think that one of the most important differentiators for us is the amount of applications that we support,” Bin noted.
The company already has paying customers, including some Fortune 500 companies across a number of verticals, and it has already invested some of the new funding round, which closed before the global COVID-19 pandemic hit, into building out more integrations for these customers. Bin tells me that Adaptive Shield immediately started hiring once the round closed and is now also in the process of hiring its first employee in the U.S. to help with sales.
Powered by WPeMatico
Google Cloud launches Confidential VMs
At its virtual Cloud Next ’20 event, Google Cloud today announced Confidential VMs, a new type of virtual machine that makes use of the company’s work around confidential computing to ensure that data isn’t just encrypted at rest but also while it is in memory.
“We already employ a variety of isolation and sandboxing techniques as part of our cloud infrastructure to help make our multi-tenant architecture secure,” the company notes in today’s announcement. “Confidential VMs take this to the next level by offering memory encryption so that you can further isolate your workloads in the cloud. Confidential VMs can help all our customers protect sensitive data, but we think it will be especially interesting to those in regulated industries.”
In the backend, Confidential VMs make use of AMD’s Secure Encrypted Virtualization feature, available in its second-generation EPYC CPUs. With that, the data will stay encrypted when used and the encryption keys to make this happen are automatically generated in hardware and can’t be exported — and with that, even Google doesn’t have access to the keys either.
Image Credits: Google
Developers who want to shift their existing VMs to a Confidential VM can do so with just a few clicks. Google notes that it built Confidential VMs on top of its Shielded VMs, which already provide protection against rootkits and other exploits.
“With built-in secure encrypted virtualization, 2nd Gen AMD EPYC processors provide an innovative hardware-based security feature that helps secure data in a virtualized environment,” said Raghu Nambiar, corporate vice president, Data Center Ecosystem, AMD. “For the new Google Compute Engine Confidential VMs in the N2D series, we worked with Google to help customers both secure their data and achieve performance of their workloads.”
That last part is obviously important, given that the extra encryption and decryption steps do incur at least a minor performance penalty. Google says it worked with AMD and developed new open-source drivers to ensure that “the performance metrics of Confidential VMs are close to those of non-confidential VMs.” At least according to the benchmarks Google itself has disclosed so far, both startup times and memory read and throughput performance are virtually the same for regular VMs and Confidential VMs.
Powered by WPeMatico
Google Cloud’s new BigQuery Omni will let developers query data in GCP, AWS and Azure
At its virtual Cloud Next ’20 event, Google today announced a number of updates to its cloud portfolio, but the private alpha launch of BigQuery Omni is probably the highlight of this year’s event. Powered by Google Cloud’s Anthos hybrid-cloud platform, BigQuery Omni allows developers to use the BigQuery engine to analyze data that sits in multiple clouds, including those of Google Cloud competitors like AWS and Microsoft Azure — though for now, the service only supports AWS, with Azure support coming later.
Using a unified interface, developers can analyze this data locally without having to move data sets between platforms.
“Our customers store petabytes of information in BigQuery, with the knowledge that it is safe and that it’s protected,” said Debanjan Saha, the GM and VP of Engineering for Data Analytics at Google Cloud, in a press conference ahead of today’s announcement. “A lot of our customers do many different types of analytics in BigQuery. For example, they use the built-in machine learning capabilities to run real-time analytics and predictive analytics. […] A lot of our customers who are very excited about using BigQuery in GCP are also asking, ‘how can they extend the use of BigQuery to other clouds?’ ”
Image Credits: Google
Google has long said that it believes that multi-cloud is the future — something that most of its competitors would probably agree with, though they all would obviously like you to use their tools, even if the data sits in other clouds or is generated off-platform. It’s the tools and services that help businesses to make use of all of this data, after all, where the different vendors can differentiate themselves from each other. Maybe it’s no surprise then, given Google Cloud’s expertise in data analytics, that BigQuery is now joining the multi-cloud fray.
“With BigQuery Omni customers get what they wanted,” Saha said. “They wanted to analyze their data no matter where the data sits and they get it today with BigQuery Omni.”
Image Credits: Google
He noted that Google Cloud believes that this will help enterprises break down their data silos and gain new insights into their data, all while allowing developers and analysts to use a standard SQL interface.
Today’s announcement is also a good example of how Google’s bet on Anthos is paying off by making it easier for the company to not just allow its customers to manage their multi-cloud deployments but also to extend the reach of its own products across clouds. This also explains why BigQuery Omni isn’t available for Azure yet, given that Anthos for Azure is still in preview, while AWS support became generally available in April.
Powered by WPeMatico