Kategorie: Microsoft Azure

Build a scalable security practice with Azure Lighthouse and Azure Sentinel

The Microsoft Azure Lighthouse product group is excited to launch a blog series covering areas in Azure Lighthouse where we are investing to make our service provider partners and enterprise customers successful with Azure. Our first blog in this series covers a top area of consideration for companies worldwide—Security with focus on how Azure Lighthouse can be used alongside Microsoft’s Azure Sentinel service to build an efficient and scalable security practice.

Today, organizations of all sizes are looking to reduce costs, complexity, and gain efficiencies in their security operations. As cloud security solutions help meet these requirements by providing flexibility, simplicity, pay for use, automatic scalability and protection across heterogenous environments, more and more companies are embracing cloud security solutions.

While achieving efficiencies is the need of the hour, organizations are also faced with shortage of security experts in the market.  Here is where there is tremendous potential for service providers to fill this gap by building and offering security services on top of cloud security solutions. Before diving deeper, let me start with a brief introduction to Azure Lighthouse and Azure Sentinel.

Azure Lighthouse helps service providers and large enterprises manage environments of multiple customers or individual subsidiaries, at scale from within their single centralized control plane. Since the launch of Azure Lighthouse at Inspire, Azure Lighthouse has seen wide adoption from both service providers and enterprises, with millions of Azure resources being managed at scale across heterogenous environments.

Azure Sentinel is a cloud native security information event management (SIEM) and security orchestration automated response (SOAR) solution from Microsoft. It enables collection of security data at scale across your entire enterprise including Azure services, Microsoft 365 services or from hybrid environments,from hybrid environments, such as other clouds, firewalls, and partner security tools. Azure Sentinel also uses built-in AI and advanced querying capabilities to detect, investigate, respond to and mitigate threats efficiently.

We will now look at how you can use both these services together to architect a scalable security practice.

To start building a security practice that scales across multiple customer environments for a service provider or helps organizations centrally monitor and manage the security operations across their individual subsidiaries, we recommend using a distributed deployment and centralized management model. This is where you deploy Azure Sentinel workspaces within the tenant that belongs to the customer or subsidiary (data stays locally within the customer’s or individual subsidiary’s environment) and manage it centrally from within a service provider’s or from a central security operations center (SOC) unit’s tenant within an organization.

You can then leverage Azure Lighthouse’s capabilities to manage and perform security operations from the central managing tenant on the Azure Sentinel workspaces located in the managed tenant. To learn more about this model and its applicability for your scenario, read Extend Azure Sentinel across workspaces and tenants.

To deploy and configure these workspaces at scale, both Azure Sentinel and Azure Lighthouse offer powerful automation capabilities that you can use effectively with CI/CD pipelines across tenants. Here is what ITCSecure, Managed Security Services Provider and Microsoft Partner based in London has to say:

“With Azure Lighthouse’s ability to get delegated access to a customer’s environment and the powerful automation capabilities of both Azure Lighthouse and Azure Sentinel, we are now able to leverage a common set of automations to deploy Azure Sentinel. In real terms, this enables us to configure Azure Sentinel with existing content like queries and analytical rules. This has resulted in significant reductions in customer onboarding times, reducing delivery times from months to a few weeks and even a few hours in certain scenarios. This has enabled us to scale our onboarding processes and practices significantly and delivers faster ROI for our customers. Azure Lighthouse has also provided greater transparency and visibility for our customers, where they can clearly see work delivered. We run queries and apply workbooks across our customer’s subscriptions, deploy playbooks in our customer’s tenants, all from a central pane of glass, further adding to the overall speed of delivery of our service.” —Arno Robbertse, Chief Executive, ITC Secure

Threat hunting and investigation through cross-tenant queries

Running queries to search for threats and as a next step investigating them is an essential part of a SOC analyst’s job. With Azure Lighthouse, you can deploy Log Analytics queries or hunting queries in the central managing tenant (preserving IP for a service provider) and run those queries across the managed tenants using the union operator and workspace expression.                     

Visualizing and monitoring data across customer environments

Another technology that works well across tenants is Azure Monitor Workbooks, Azure Sentinel’s dashboarding technology. You can choose to deploy workbooks in the managing tenant or managed tenant per your requirements. For workbooks deployed in the managing tenant, you can add a multi-workspace selector within a workbook (in case it doesn’t have one already built into it), to visualize and monitor data and essentially get data insights across multiple workspaces and across multiple customers/subsidiaries if needed.

Automated responses through playbooks

Security Playbooks can be used for automatic mitigation when an alert is triggered. The playbooks can be deployed either in the managing tenant or the individual managed tenant, with the response procedures configured based on which tenant's users will need to take action in response to a security threat.

Xcellent, a managed services provider and Microsoft partner based in Netherlands has benefited from access to a central security solution powered by Azure Sentinel and Azure Lighthouse, to monitor the different Microsoft 365 components across customer tenants. Response management and querying against their customer base has also become more efficient—dropping Xcellent’s standard response time to less than 45 minutes and allowed the team to create a more proactive security solution for their customers.

Cross-tenant incident management

Multiple workspace incident view facilitates centralized incident monitoring and management across multiple Azure Sentinel workspaces and across Azure Active Directory (Azure AD) tenants using Azure Lighthouse. This centralized incident view lets you manage incidents directly or drill down transparently to the incident details in the context of the originating workspace.

Resources to get you started

Azure Lighthouse extends Azure Sentinel’s powerful security capabilities to help you centrally monitor and manage security operations from a single interface and efficiently scale your security operations across multiple Azure tenants and customers.

The following resources will help you get started:

Take a look at our detailed documentation and guidance for using Azure Lighthouse with Azure Sentinel.
For latest resources and updates on Azure Sentinel, join us at the Azure Sentinel Tech Community.
You can provide feedback or request new features for Azure Lighthouse in our feedback forum.
Check out Azure PartnerZone for latest content, news, and resources for partners.

Quelle: Azure

Empowering remote learning with Azure Cognitive Services

This blog post was co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services.

As schools and organizations around the world prepare for a new school year, remote learning tools have never been more critical. Educational technology, and especially AI, has a huge opportunity to facilitate new ways for educators and students to connect and learn.

Today, we are excited to announce the general availability of Immersive Reader, and shine a light on how new improvements to Azure Cognitive Services can help developers build AI apps for remote education that empower everyone.

Make content more accessible with Immersive Reader, now generally available

Immersive Reader is an Azure Cognitive Service within the Azure AI platform that helps readers read and comprehend text. Through today’s general availability, developers and partners can add Immersive Reader right into their products, enabling students of all abilities to translate in over 70 languages, read text aloud, focus attention through highlighting, other design elements, and more. 

Immersive Reader has become a critical resource for distance learning, with more than 23 million people every month using the tool to improve their reading and writing comprehension. Between February and May 2020, when many schools moved to a distance learning model, we saw a 560 percent increase in Immersive Reader usage. As the education community embarks on a new school year in the Fall, we expect to see continued momentum for Immersive Reader as a tool for educators, parents, and students.

With the general availability of Immersive Reader, we are also rolling out the following enhancements:

Immersive Reader SDK 1.1: Updates include support to have a page read aloud automatically, pre-translating content, and more. Learn about SDK updates.
New Neural Text-to-Speech (TTS) languages: Immersive Reader is adding 15 new Neural Text to Speech voices, enabling students to have content read aloud in even more languages. Learn about the new Neural Text to Speech languages.
New Translator languages: Translator is adding five new languages that will also be available in Immersive Reader—Odia, Kurdish (Northern), Kurdish (Central), Pashto, and Dari. Learn about the latest Translator languages.

Today, we’re adding new partners who are integrating Immersive Reader to make content more accessible, Code.org and SAFARI Montage. 

Code.org is a nonprofit dedicated to expanding access to computer science in schools. To ensure that students of all backgrounds and abilities can access their resources and course content, Code.org is integrating Immersive Reader into their platform.

“We’re thrilled to partner with Microsoft to bring Immersive Reader to the Code.org community. The inclusive capabilities of Immersive Reader to improve reading fluency and comprehension in learners of varied backgrounds, abilities, and learning styles directly aligns with our mission to ensure every student in every school has the opportunity to learn computer science.” – Hadi Partovi, Founder and CEO of Code.org

SAFARI Montage, a leading learning object repository, is integrating Immersive Reader to make it possible for students of any language background or accessibility needs to engage with content, and enable families who don’t speak the language of instruction to be more involved in their students’ learning journeys.  

"Immersive Reader is a crucial support for CPS students and families. During remote learning, particularly for our younger learners, student learning is often supported by parents, guardians, or other caregivers. Since Immersive Reader can be used to translate the student-facing instructions in our digital curriculum, families can support student learning in over 80 languages, making digital learning far more equitable and accessible than ever before! In addition, read-aloud and readability supports are game-changers for diverse learners" – Giovanni Benincasa, UX Manager, Department of Curriculum, Instruction, and Digital Learning, Chicago Public Schools  

With Immersive Reader, all it takes is a single API call to help users boost literacy. To start exploring how to integrate Immersive Reader into your app or service, check out these resources: 

Software Development Kit (SDK): Immersive Reader SDK. 
Documentation: Immersive Reader documentation. 
Getting started videos: Immersive Reader videos .

To see the growing list of Immersive Reader partners and learn more, check out our partners page and Immersive Reader education blog.

Bring online courses to life with speech-enabled apps

With the shift to remote learning, another challenge that educators may face is continuing to drive student engagement.

Text to Speech, a Speech service feature that allows users to convert text to lifelike audio can facilitate new ways for students to interact with content. In addition to powering features like Read Aloud in Immersive Reader and the Microsoft Edge browser, Text to Speech enables developers to build apps that speak naturally in over 110 voices with more than 45 languages and variants.

With the Audio Content Creation tool, users can more easily bring audiobooks to life and finetune audio characteristics like voice style, rate, pitch, and pronunciation to fit their scenarios—no code required. Voices can even be customized for specific characters or personas; the Custom Neural Voice capability makes it possible to build one-of-a-kind voices, starting with 30 minutes of audio. Duolingo, for example, is using the Custom Neural Voice capability to create unique voices to represent different characters in its language courses.

To learn more about how to start creating speech-enabled apps for remote learning, check out the technical Text to Speech blog and other resources:

Demos: Text to Speech. 
Documentation: Text to Speech.
Software Development Kit (SDK): Speech SDK.

Improve productivity and accessibility with transcription and voice commands 

AI can also be a useful tool for more seamless note-taking, making it possible for students and teachers to type with their voice. Transcribe in Word uses Speech to Text in Azure Cognitive Services to automatically transcribe your conversations. Now with speaker diarization, you can get a transcript that identifies who said what, when. 

In addition, adding voice enables more seamless experiences in Microsoft 365. For students who have difficulties writing things down, they can use AI-powered tools in Office not just for dictation but also for controls such as adding, formatting, editing, and organizing text. Word uses Language Understanding, an Azure Cognitive Service that enables you to add custom natural language understanding to your apps, to make it possible to capture ideas easily. To learn more about Language Understanding and how it is powering voice commands, check out our Language Understanding blog.

For more details on how AI is powering experiences in Microsoft 365, read the Microsoft 365 blog.

Get started today

We can’t wait to see what you’ll build. Get started today with Azure Cognitive Services and an Azure free account.
Quelle: Azure

Microsoft and TSMC announce Joint Innovation Lab to accelerate silicon design on Azure

Right now, every industry and every customer is going through a massive transformation, and cloud computing is often a central enabler of this. The silicon industry is also experiencing change as a critical part of the fast-growing cloud computing ecosystem—an ecosystem where silicon and chip design workloads must meet the rising bar for performance, complexity, and perceived costs. In Azure, we focus on the needs of the semiconductor design industry, so we can solve problems and provide solutions for our customers. With our partners at TSMC, we share the belief that using the cloud for silicon design will be a competitive advantage for those that embrace it. 

Through our deep partnership, we have worked closely to implement an Azure-based architecture for TSMC’s Virtual Design Environment, refined cloud resource selection and storage architectures for specific workloads, and demonstrated cost versus performance optimizations for scalable workloads. This requires both new virtual machine (VM) types most suitable for EDA (Electronics Design Automation) workloads, and a cloud-optimized design solution that fully utilizes EDA parallelism. Starting from our collaboration with TSMC and its EDA ecosystem partners, we have jointly achieved multiple breakthroughs in both areas.

Microsoft and TSMC support cloud adoption in the silicon industry

To continue this momentum, Microsoft launches the Joint Innovation Lab with TSMC to serve as a collaboration platform to best integrate cloud and EDA innovations, and help provide the semiconductor industry with the performance and cost effectiveness to accelerate time-to-market and optimize development cost to unleash product innovations.

“Nurturing ecosystem collaboration has been the core of TSMC Open Innovation Platform® (OIP), and this Joint Innovation Lab with Microsoft is one big step forward elevating cross-industry partnership to the next level. TSMC has been one of the earliest drivers of cloud to speed up design enablement for customers since 2018. Through our collaboration with Cloud Alliance members, we can lower entry barriers of Cloud adoption for our common customers and help customers conduct IC design securely in the Cloud and achieve faster time-to-market. Microsoft has been a great partner, and its Silicon on Azure team shares a similar vision with us.” – Dr. Cliff Hou, Senior Vice President of Technology Development at TSMC

Security is foundational for Azure, and we are one of the first cloud service providers certified by TSMC. In addition to our investments of over $1 billion a year on cybersecurity and over 3,500 engineers dedicated to security, we continue to focus on the needs of the semiconductor industry to protect their intellectual property. Building on top of the security foundation, the Joint Innovation Lab aims at hosting in-depth collaborations among ecosystem partners to drive new solutions and transform IC design in the Cloud:

Next-generation VM types: We are working to optimize new VM types in all aspects of CPU performance, number of cores, memory-to-core ratio, and local storage, combined with the most effective storage options, targeting EDA workloads of highly complicated IC designs enabled by the most advanced process technologies.
Cloud-optimized EDA solutions: We are working to create cloud-optimized design solutions, tools, and methodologies combined, to fully utilize EDA parallelism. With massive computational power in the cloud lifting in-house compute limitation, it opens up a brand-new category of EDA optimization to fully explore parallelism opportunities.

TSMC and Microsoft have worked together since 2018 when TSMC announced its OIP Cloud Alliance and OIP Virtual Design Environment (OIP VDE), to enable cloud-powered silicon design approaches using Azure that have demonstrated significant design cycle improvements. Using the vast computational power of Azure services and TSMC’s silicon expertise, we have been able to optimize production runs around the world. It is not uncommon for EDA design jobs to take months due to in-house compute limitations. With access to Azure’s burstable resources to scale to tens of thousands of cores quickly, silicon designers are now able to achieve much faster time to market with improved efficiencies while dealing with their surge demand.

The Silicon on Azure team, led by Mujtaba Hamid, has always taken a broad industry approach to silicon design on Azure, and both our teams are committed to sharing what we learn with the semiconductor design community. Most recently, our collaboration led to two technical whitepapers available through TSMC-Online, outlining optimal cloud usage to speed up mission-critical timing sign-off jobs in a big way, while achieving cost optimization at the same time.

I am very excited about the Joint Lab effort and look forward to jointly engaging with all our partners. Stay tuned for more details and announcements coming out of this collaboration.

For further details on running silicon workloads on Azure, visit our Azure high-performance computing (HPC) for silicon page. To contact the team, please email Azure for Silicon. 
Quelle: Azure

Power mission-critical applications with Azure Database for PostgreSQL

In our current environment, organizations are increasingly looking towards digital solutions to engage their customers and remain competitive. They’re discovering that their customers’ needs can be best met through differentiated, digital experiences delivered by cloud-native applications.

When building a new application, one of the most important decisions to make is where to store the application data. We see tremendous interest in Azure Database for PostgreSQL when it comes to storing relational data in the cloud for mission-critical applications. Here’s why:

Why Azure Database for PostgreSQL?

100 percent open source.

Azure Database for PostgreSQL is built on community edition Postgres, with open extension support so you can leverage valuable PostgreSQL features, including JSONB, geospatial support, and rich indexing.
Our Postgres team at Microsoft is committed to nurturing a culture of contributing to and collaborating with the Postgres community, and we’re excited to welcome Postgres committers to the team. These committers review submitted code for Postgres, “commit” it into the source code repository, and work with other contributors to test, refine, and eventually incorporate it into the next Postgres build. In future blogs, they’ll share what they’re working on when it comes to new versions of Postgres and updates to the Citus open source extension.

Fully managed.

Using a managed Postgres database service on Azure makes your job simpler and allows you to focus on your application, by automating time- and cost-intensive tasks like configuring and managing high-availability, disaster recovery, backups, and data replication across regions.
Azure Database for PostgreSQL has enterprise-grade security and compliance capabilities, such as Azure Advanced Threat Protection, and provides customized performance recommendations, making it suitable for your most mission-critical applications.

High-performance horizontal scaling.

Azure Database for PostgreSQL Hyperscale (Citus) scales out horizontally to hundreds of nodes, with no application rewrites, so you can easily build incredibly scalable applications. This is done using the Citus open source Postgres extension that intelligently and transparently distributes your data and queries across multiple nodes to achieve massive parallelism, along with a much bigger compute, memory, and disk footprint.
In future blogs, we’ll dive deeper into key use cases for Hyperscale (Citus) and share how organizations are building powerful applications using these capabilities. Hyperscale (Citus) enables organizations to scale multi-tenant SaaS applications, build high throughput transactional applications, and more.

Key considerations for running modern enterprise applications on Postgres

If you’re new to the world of Postgres and want to understand whether it’s a good fit for building your enterprise applications, check out our new whitepaper, Running Enterprise Applications on PostgreSQL, to learn about:

Benefits of using Postgres for modern, mission-critical applications, and considerations around performance, scalability, security, and more.
Postgres’ extensible design that enables you to future-proof your applications.
Postgres capabilities that empower developers to work more productively.

Announcing our Postgres blog series

We are here to help you at every stage of your journey with Azure Database for PostgreSQL, and will be keeping you up to date with regular posts both here and on our Azure Database for PostgreSQL blog. We’ll be sharing:

Insights and updates directly from Postgres committers Thomas Munro, Jeff Davis, Andres Freund, and David Rowley—you can read the first post in the series, How to securely authenticate with SCRAM in Postgres 13, now.
Key customer use cases, stories, and architectures.
Resources and best practices to help you develop innovative applications to solve business challenges.

Whether you’re new to Postgres, new to Azure, or are already using Azure Database for PostgreSQL to power your enterprise applications, we look forward to supporting your application development journey.

Stay tuned for future blogs here and subscribe to our Azure Database for PostgreSQL blog.

Learn more about Azure Database for PostgreSQL.
Quelle: Azure

Bringing AI supercomputing to customers

The trend toward the use of massive AI models to power a large number of tasks is changing how AI is built. At Microsoft Build 2020, we shared our vision for AI at Scale utilizing state-of-the-art AI supercomputing in Azure and a new class of large-scale AI models enabling next-generation AI. The advantage of large scale models is that they only need to be trained once with massive amounts of data using AI supercomputing, enabling them to then be “fine-tuned” for different tasks and domains with much smaller datasets and resources. The more parameters that a model has, the better it can capture the difficult nuances of the data, as demonstrated by our 17-billion-parameter Turing Natural Language Generation (T-NLG) model and its ability to understand language to answer questions from or summarize documents seen for the first time. Natural language models like this, significantly larger than the state-of-the-art models a year ago, and many orders of magnitude the size of earlier image-centric models, are now powering a variety of tasks throughout Bing, Word, Outlook, and Dynamics.

Training models at this scale requires large clusters of hundreds of machines with specialized AI accelerators interconnected by high-bandwidth networks inside and across the machines. We have been building such clusters in Azure to enable new natural language generation and understanding capabilities across Microsoft products, and to power OpenAI on their mission to build safe artificial general intelligence. Our latest clusters provide so much aggregated compute power that they are referred to as AI supercomputers, with the one built for OpenAI reaching the top-five publicly disclosed supercomputers in the world. Using this supercomputer, OpenAI unveiled in May their 175-billion-parameter GPT-3 model and its ability to support a wide range of tasks it wasn’t specifically trained for, including writing poetry or translation.

The work that we have done on large-scale compute clusters, leading network design, and the software stack, including Azure Machine Learning, ONNX Runtime, and other Azure AI services, to manage it is directly aligned with our AI at Scale strategy. The innovation generated through this process is ultimately making Azure better at supporting the AI needs of all our customers, irrespective of their scale. For example, with the NDv2 VM series, Azure was the first and only public cloud offering clusters of VMs with NVIDIA’s V100 Tensor Core GPUs, connected by high-bandwidth low-latency NVIDIA Mellanox InfiniBand networking. A good analogy is how automotive technology is pioneered in the high-end racing industry and then makes its way into the cars that we drive every day.

New frontiers with unprecedented scale

“Advancing AI toward general intelligence requires, in part, powerful systems that can train increasingly more capable models. The computing capability required was just not possible until recently. Azure AI and its supercomputing capabilities provide us with leading systems that help accelerate our progress”  – Sam Altman, OpenAI CEO

In our continuum of Azure innovation, we’re excited to announce the new ND A100 v4 VM series, our most powerful and massively scalable AI VM, available on-demand from eight, to thousands of interconnected NVIDIA GPUs across hundreds of VMs.

The ND A100 v4 VM series starts with a single virtual machine (VM) and eight NVIDIA Ampere A100 Tensor Core GPUs, but just like the human brain is composed of interconnected neurons, our ND A100 v4-based clusters can scale up to thousands of GPUs with an unprecedented 1.6 Tb/s of interconnect bandwidth per VM. Each GPU is provided with its own dedicated topology-agnostic 200 Gb/s NVIDIA Mellanox HDR InfiniBand connection. Tens, hundreds, or thousands of GPUs can then work together as part of a Mellanox InfiniBand HDR cluster to achieve any level of AI ambition. Any AI goal (training a model from scratch, continuing its training with your own data, or fine-tuning it for your desired tasks) will be achieved much faster with dedicated GPU-to-GPU bandwidth 16x higher than any other public cloud offering.

The ND A100 v4 VM series is backed by an all-new Azure-engineered AMD Rome-powered platform with the latest hardware standards like PCIe Gen4 built into all major system components. PCIe Gen 4 and NVIDIA’s third-generation NVLINK architecture for the fastest GPU-to-GPU interconnection within each VM keeps data moving through the system more than 2x faster than before. 

Most customers will see an immediate boost of 2x to 3x compute performance over the previous generation of systems based on NVIDIA V100 GPUs with no engineering work. Customers leveraging new A100 features like multi-precision Tensor Cores with sparsity acceleration and Multi-Instance GPU (MIG) can achieve a boost of up to 20x.

“Leveraging NVIDIA’s most advanced compute and networking capabilities, Azure has architected an incredible platform for AI at scale in the cloud. Through an elastic architecture that can scale from a single partition of an NVIDIA A100 GPU to thousands of A100 GPUs with NVIDIA Mellanox Infiniband interconnects, Azure customers will be able to run the world’s most demanding AI workloads.” – Ian Buck, General Manager and Vice President of Accelerated Computing at NVIDIA

The ND A100 v4 VM series leverages Azure core scalability blocks like VM Scale Sets to transparently configure clusters of any size automatically and dynamically. This will allow anyone, anywhere, to achieve AI at any scale, instantiating even AI supercomputer on-demand in minutes. You can then access VMs independently or launch and manage training jobs across the cluster using the Azure Machine Learning service.

The ND A100 v4 VM series and clusters are now in preview and will become a standard offering in the Azure portfolio, allowing anyone to unlock the potential of AI at Scale in the cloud. Please reach out to your local Microsoft account team for more information.
Quelle: Azure

Build resilient applications with Kubernetes on Azure

Welcome to KubeCon EU 2020, the virtual edition. While we won’t be able to see each other in person at KubeCon EU this year, we're excited that this new virtual format of KubeCon will make the conference more accessible than ever, with more people from the amazing Kubernetes community able to join and participate from around the world without leaving their homes.

With everything that has been happening, the last year has been an up and down experience, but through it all I’m incredibly proud of the focus and dedication from the Azure Kubernetes team. They have continued to iterate and improve our Kubernetes on Azure that provides enterprise-grade experience for our customers.

Kubernetes on Azure (and indeed anywhere) delivers an open and portable ecosystem for cloud-native development. In addition to this core promise, we also deliver a unique enterprise-grade experience that ensures the reliability and security your workloads demand, while also enabling the agility and efficiency that business today desires. You can securely deploy any workload to Azure Kubernetes Service (AKS) to drive cost-savings at scale across your business. Today, we're going to tell you about even more capabilities that can help you along on your cloud-native journey to Kubernetes on Azure.

Improving latency and operational efficiency

One of the key drivers of cloud adoption is reducing latency. It used to be that it took days to get physical computers and set them up in a cluster. Today, you can deploy a Kubernetes cluster on Azure in less than five minutes. These improvements benefit the agility of our customers. For customers who want to scale and provision faster, we are announcing a preview of ephemeral OS disk support which makes responding to new compute demands on your cluster even faster.

Latency isn’t just about the length of time to create a cluster. It’s also about how fast you can detect and respond to operational problems. To help enterprises improve their operational efficiency, we’re announcing preview integration with Azure Resource Health which can alert you if your cluster is unhealthy for any reason. We’re also announcing the general availability of node image updates which allow you to upgrade the underlying operating system to respond to bugs or vulnerabilities in your cluster while staying on the same Kubernetes version for stability.

Finally, though Kubernetes has always enabled enterprises to drive cost savings through containerization, the new economic realities of the world during a pandemic mean that achieving cost efficiency for your business is more important than ever. We’ve got a great exercise that can help you learn how to optimize your costs using containers and the Azure Kubernetes Service.

Secure by design with Kubernetes on Azure

One of the key pillars of any enterprise computing platform is security. With market-leading features like policy integration and Azure Active Directory identity for Pods and cloud-native security have always been an important part of the Azure Kubernetes Service. I’m excited about some new features we’ve added recently to further enhance the security of your workloads running on Kubernetes.

Though Kubernetes has built-in support for secrets, most enterprise environments require a more secure and more compliant implementation. In the Azure Kubernetes Service, being enterprise-grade means providing integration between Azure Key Vault and the Azure Kubernetes service. Using Key Vault with Kubernetes enables you to securely store your credentials, certificates, and other secrets in state of the art, compliant secret store, and easily use them with your applications in an Azure Kubernetes cluster.

It’s even more exciting that this integration is built on the back of an open Container Storage Interface (CSI) driver that the Azure team built and open sourced for the entire Kubernetes community. Giving back to open source is an important part of what it means to be a community steward, and it was exciting to see our approach get validated as it was picked up and used by the HashiCorp Vault team for their secrets integration. Our open source team has been hard at work on improving many other parts of the security ecosystem. We’ve enhanced the CSI driver for Windows, and worked on cgroups v2 and containerd. If you want to learn more about how to secure your cloud-native workloads and make sure that your enterprise is following Microsoft’s best practices, check out our guide to Kubernetes best practices. They will teach you how to integrate firewalls, policy, and more to ensure you have both security and agility in your cloud-native development.

Next steps and KubeCon EU

I hope that you have an awesome KubeCon EU. As you go through the conference and learn more about Kubernetes, you can also learn more about Kubernetes on Azure with all of the great information online and in our virtual booth. If you’re new to KubeCon and Kubernetes and wondering how you can adopt Kubernetes for workloads from hobbyist to enterprise, we’ve got a great Kubernetes adoption guide for you.
Quelle: Azure