Toyota-Tochter: Lexus zeigt erste Bilder seines Elektro-Sportwagens
Lexus plant einen Sportwagen, der rein elektrisch 700 km fährt. Erste Bilder des Elektroautos mit Festkörperakku wurden jetzt veröffentlicht. (Lexus, Technologie)
Quelle: Golem
Lexus plant einen Sportwagen, der rein elektrisch 700 km fährt. Erste Bilder des Elektroautos mit Festkörperakku wurden jetzt veröffentlicht. (Lexus, Technologie)
Quelle: Golem
Will ein Anbieter eine andere Bezahlmethode im App Store nutzen, braucht es eine ganz neue App. Kartellwächter lassen das Apple nicht durchgehen. (Apple App Store, Apple)
Quelle: Golem
Wie in den USA verklagen auch hierzulande Händler Paypal wegen Kontensperren und einbehaltenem Geld. Ein Anwalt berichtet von Problemen und ersten Erfolgen. Ein Interview von Moritz Tremmel (Paypal, Interview)
Quelle: Golem
Auf der CES 2022 gezeigt, seitdem Funkstille: Die Geforce RTX 3090 Ti soll unter Problemen leiden – etwa wegen des geplanten Videospeichers. (Nvidia Ampere, Grafikhardware)
Quelle: Golem
Digital twin environments (DTE) can help organizations simulate an entire system or subsystem and can be used to test software component interactions or simulate scenarios. We produced a blueprint with some of our architectural approaches to help customers implement digital twin soulutions and lessons learned.
Quelle: CloudForms
Today, we are introducing Cloud Functions (2nd gen), Google Cloud’s next-generation Functions-as-a-Service product. This next generation version of Cloud Functions comes with an advanced feature set giving you more powerful infrastructure, advanced control over performance and scalability, more control around the functions runtime and triggers from over 90 event sources. Further, the infrastructure is powered by Google Cloud’s cutting-edge serverless and eventing infrastructure, Cloud Run and Eventarc.Infrastructure that meets your workloads’ needsCloud Functions adds a range of new capabilities for 2nd gen functions, such as concurrency (up to 1,000 concurrent requests per function instance), larger instances (16 GB memory and 4 vCPUs) and longer processing time for HTTP functions (up to 60 mins) and minimum instances (prewarmed instances). Longer request processing – Run your 2nd gen cloud functions for up to 60 mins for HTTP functions, making it easier to run longer-request workloads such as processing large streams of data from Cloud Storage or BigQuery.Larger instances – Leverage up to 16GB of RAM and 4 vCPUs on 2nd gen cloud functions, allowing larger in-memory, compute-intensive and more parallel workloads.Concurrency – Leverage up to 1000 concurrent requests with a single function, minimizing cold starts and improving latency and cost when scaling.Minimum instances – Provide for pre-warmed instances to cut your cold starts and make sure the bootstrap time of your application does not impact application performance.Traffic splitting – 2nd gen cloud functions support multiple revisions of your functions, splitting traffic between different revisions and rolling your function back to a prior version.Broader event coverage and CloudEvents support2nd gen cloud functions now include native support for Eventarc, which brings over 90+ event sources from direct sources and Cloud Audit logs (e.g., BigQuery, Cloud SQL, Cloud Storage, Firebase…). And of course, Cloud Functions still supports events from custom sources by publishing to Pub/Sub directly. These event-driven functions adhere to industry-standard CloudEvents, regardless of the source, to ensure a consistent developer experience.New developer experienceCloud Functions features an enhanced UI, customizable dashboard, improved developer experience and accessibility updates. A new seamless onboarding experience makes it easy to quickly develop and deploy your 1st gen and 2nd gen functions in one place. A deployment progress tracker navigates through the process of the 2nd gen function deployment and helps to spot the errors associated with each step. The UI also simplifies integrations with Eventarc using new menus and badges to help you find information about your function.Portability based on OSS buildpacks and Functions Frameworks2nd gen functions are built using open-source buildpacks and Functions Frameworks, giving you the portability to run your functions anywhere. Check out the new Cloud FunctionsWe are excited to see what you build with Cloud Functions. You can learn more about Cloud Functions here and get started using Quickstarts: Cloud Functions.Related ArticleNew Cloud Functions min instances reduces serverless cold startsSetting ‘min instances’ on your Cloud Functions applications translates to lower startup times.Read Article
Quelle: Google Cloud Platform
What does modernization mean?As an IT leader or architect, you may notice that your software architecture is encountering performance issues. You may be considering moving your datastore from a mainframe or a traditional relational database (RDBMS) to a more modern database to take advantage of advanced analytics, scale at a faster rate, and opportunities to cut costs. Such is the impetus for modernization.An approach to modernization can be defined as, “An open, cross-functional collaboration dedicated to building new design systems and patterns that support evolving computing capabilities, information formats, and user needs.”Within the same spirit of modernization we can say that MongoDB works along with Google Cloud technologies to provide joint solutions and some reference architectures to help our customers leverage this partnership.Principles of modern technology solutionsA point of view to Modernization is understood through four basic principles that focus on outcomes for our customers. These principles can be applied to envision what a modern solution should achieve or to identify whether a given solution is modern or not.Help users get more done. Bring quality information forward and make it actionable in context. Actions are the new blue links.Feed curiosity. Open doorways to rich, endless discovery. Remove dead ends for users who want to engage more.Reflect the world, in real time. Surface fresh, dynamic content. Help users be in the know.Be personal, then personalize. Encourage the user’s personal touch to surface personal content and personalized experiences. Be stateful and contextual.Modern applications should be capable of presenting information in a way that enables users to not only make decisions, but also transform those decisions into actions. This requires the use of variable data formats and integration mechanisms that will allow the end user to interact with various systems and produce real-time results, without the need to log in to each one of them.MongoDB Atlas, a modern database management systemIf we are to use the four principles of modernization as a reference to identify modern solutions, then MongoDB Atlas reflects these directly. Altas helps database and infrastructure administrators get more done faster and with less effort than managing MongoDB on premises. It is a fully managed database service that takes care of the most critical and time-consuming tasks related to providing a continuous and reliable service, including security and compliance features out of the box, freeing administrators’ and developers’ time to focus on innovation.The third principle talks about reflecting the world in real time. This is the most cumbersome and daunting task for anybody who is responsible for the design of a modern technology system, since it requires an architecture capable of receiving, processing, storing, and producing results from data streams originated by different systems, at different velocity rates, and in different formats. Atlas frees the solution architect from this burden. As a managed service, it takes care of the networking, processing, and storage resources allocation, so it will scale as needed, when needed. And as a document-based database, it also allows for flexibility in regards to the format and organization of incoming data, Developers can focus on the actual process rather than spend their time modeling the information to make it fit into the RDBMS, as so often happens with traditional relational database schemas. It also provides real-time data processing features that allow for the execution of code or the consumption of external APIs residing in separate applications or even in various clouds.Of course, the combination of the first three principles leads to the fourth, which is to personalize the experience to the end user. Businesses must be able to solve specific user needs, rather than limit their processes solely to what their database or application is capable of. Putting the user first invariably leads to a better and modern experience—and that starts with choosing the best cloud provider and a database that aligns with these principles.A reference architecture for data modernizationLet’s dive into a general view of the migration reference architecture that enables the four aforementioned principles.An Operational Data Layer (or ODL) is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. It enables a range of board-level strategic initiatives such as Legacy Modernization and Data as a Service, and use cases such as single view, real-time analytics and mainframe offload.An Operational Data Layer is an intermediary between existing data sources and consumers that need to access that data. An ODL deployed in front of legacy systems can enable new business initiatives and meet new requirements that the existing architecture can’t handle— without the difficulty and risk of a full rip and replace of legacy systems.For an initial migration that will keep the current architecture in place while replicating records that are produced over the production system, the following reference shows some components that can be taken into account to achieve a starting point in time backup and restore on MongoDB Atlas, while at the same time enabling real time synchronization.Figure 1. One-time data migration and real-time data syncThe above solution architecture shows both general views for one-time data migration and real-time data synchronization using Google Cloud technologies. A one-time data migration involves initial bulk ETL of data from the source relational database to MongoDB. Google Cloud Data Fusion can be used along with Apache Sqoop or Spark SQL’s JDBC connector powered by Dataproc to extract data from the source and store it in Google Cloud Storage temporarily. Custom Spark jobs powered by Dataproc are deployed to transform the data and load into MongoDB Atlas. MongoDB has a native spark connector which will allow storing Spark DataFrame as collections.Figure 2. One-time data migrationIn most of the migrations, the source database will not be retired for a few weeks to months. In such cases, MongoDB Atlas needs to be up to date with the source database. We can use Change Data Capture (CDC) tools like Google Cloud Datastream or Debezium on Dataflow to capture the changes, which can then be pushed to message queues like Google Cloud Pub/Sub. We can write custom transformation jobs using Apache beam powered by Dataflow, Java, or Python, which can consume the data from the message queue, transform it, and push it to MongoDB Atlas using native drivers. Google Cloud Composer will help orchestrate all the workflows.Figure 3. Real-time data synchronizationCommon use cases for MongoDBBelow are some observed common patterns of MongoDB. (For a more general treatment of more patterns please check out the MongoDB use case page.)Monolith to microservice – With its flexible schema and capabilities for redundancy, automation, and scalability, MongoDB (and MongoDB Atlas, its managed services version) is very well suited for microservices architecture. Together, MongoDB Atlas and microservices on Google Cloud can help organizations better align teams, innovate faster, and meet today’s demanding development and delivery requirements with full sharding across regions and globally.Legacy modernization – Relationship databases impose a tax on a business—a Data and Innovation Recurring Tax (DIRT). By modernizing with MongoDB, you can build new business functionality 3-5x faster, scale to millions of users wherever they are on the planet, and cut costs by 70% and more—all by unshackling yourself from legacy systems and, at the same time, taking advantage of the Google Cloud ecosystem. Mainframe offload – MongoDB can help offload key applications from the mainframe to a modern data platform without impacting your core systems, and help achieve agility while also reducing costs.Real-time analytics – MongoDB makes it easy to scale to the needs of real-time analytics with Atlas on Google Cloud; coupled with Google cloud analytics, such as BigQuery, the sky’s the limit.Mobile application development- MongoDB Realm helps companies build better apps faster with edge-to-cloud sync and fully managed backend services, including triggers, functions, and GraphQL.Other reference architecturesBelow are some reference architectures that can be applied to particular requirements. For more information, visit:MongoDB Use CasesGoogle Cloud Architecture CenterAn Operational Data Warehouse requires swift response times to keep data updated to the most recent state possible, with the final goal to produce near-real-time analytics. It also has to be scalable, robust, and secure to adapt to the highest standards and be compliant with various regulations.Figure 4. Operationalized Data Warehouse (ODS + EDW)The above referenced architecture describes which Google Cloud components can be combined to ingest data from any source into an ODS supported by MongoDB Atlas and how to integrate this ODS with an Enterprise Data Warehouse (BigQuery) that enables structured data for analytical tools like Looker.Shopping Cart AnalysisFigure 5 illustrates an implementation example of the Operationalized Data Warehouse reference architecture shown previously. In this scenario, several data sources (including shopping cart information) are replicated in real time to MongoDB through the Spark Connector. Information is then processed using Dataflow as a graphical interface to generate data processing jobs that are executed over an ephemeral, managed Hadoop & Spark cluster (Dataproc). Finally, processed data can be structured and stored for fast querying in BigQuery, supporting Shopping Cart, Product Browsing, and Outreach applications.Figure 5. Shopping cart analysisRecommendation EnginesFigure 6 presents a continuation of the idea presented in the last example. Now the objective is to use MongoDB Atlas as an Operational Data Warehouse that combines structured and semistructured data (SQL and noSQL data) in real time. This works as a centralized repository that enables machine learning tools such as Spark Mlib running on Dataproc, Cloud Machine Learning (now Vertex AI), and Prediction API to analyze data and produce personalized recommendations for customers visiting an online store in real time.Data from various systems can be ingested as-is and stored and indexed in JSON format in MongoDB. Dataproc would then use MongoDB Apache Spark Connector to perform the analysis. The insight would be stored in BigQuery and distributed to applications downstream.Learn more about MongoDB and Google Cloud at cloud.google.com/mongodbRelated ArticleLooker lets you choose what works best for your dataEmbrace platform freedom with Looker. Learn about how we are expanding our features as a cloud platform to meet the unique needs of every…Read Article
Quelle: Google Cloud Platform
“As we head into the fourth calendar year of the Advancing Reliability blog series, empowering organizations to run their workloads reliably on Azure remains one of our top priorities. We continually invest in evolving the Azure platform to help achieve this on a daily basis. Your ability to monitor virtual machine (VM) availability in a robust and comprehensive way is paramount to ensuring that your applications are available and resilient. For today’s post in the series, I have asked Program Manager, Pujitha Desiraju, from our Azure Core Platform Fundamentals Engineering team to talk about the latest observability enhancements for VM availability monitoring, as well as planned investments to deliver the best monitoring experience.”—Mark Russinovich, CTO, Azure
This post was co-authored by Principal Software Engineering Manager, Gaurav Jagtiani.
Flash, as the project is internally known, is a collection of efforts across Azure Engineering, that aims to evolve Azure’s virtual machine (VM) availability monitoring ecosystem into a centralized, holistic, and intelligible solution customers can rely on to meet their specific observability needs. Today, we’re excited to announce the completion of the project’s first two milestones—the preview of VM availability data in Azure Resource Graph, and the private preview of a VM availability metric in Azure Monitor.
What is Project Flash?
Project Flash derives its name from our commitment to building robust and rapid ways to monitor virtual machine (VM) availability as comprehensively as possible—a key prerequisite for efficient application performance. It’s our mission to ensure you can:
Consume accurate and actionable data on VM availability disruptions (for example, VM reboots and restarts, application freezes due to network driver updates, and 30-second host OS updates), along with precise failure details (for example, platform versus user-initiated, reboot versus freeze, planned versus unplanned).
Analyze and alert on trends in VM availability for quick debugging and month-over-month reporting.
Periodically monitor data at scale and build custom dashboards to stay updated on the latest availability states of all resources.
Receive automated root cause analyses (RCAs) detailing impacted VMs, downtime cause and duration, consequent fixes, and similar—all to enable targeted investigations and post-mortem analyses.
Receive instantaneous notifications on critical changes in VM availability to quickly trigger remediation actions and prevent end-user impact.
Dynamically tailor and automate platform recovery policies, based on ever-changing workload sensitivities and failover needs.
With these goals in mind, we’ve divided our execution strategy into two phases—a near-term phase to meet critical current needs, and a long-term phase to deliver the best VM availability monitoring experience. This two-phased approach helps us continually bridge gaps, iterate on service quality, and learn from your feedback at every step along the way.
Announcing new monitoring options
For the first phase, we are providing different options to enable convenient access to VM availability data to address a range of observability needs. We aim to maintain data consistency with similar rigorous quality standards across all of these existing features and solutions, like Resource Health or Activity Log, to deliver a consistent view agnostic of the solution you choose.
Introducing at-scale analysis for VM availability
Today, we’re excited to reach our first Project Flash milestone—with the preview release of VM availability states in Azure Resource Graph for at-scale programmatic consumption.
Azure Resource Graph is a service in Azure that is extensively adopted for its efficient ability to query across many subscriptions, all at once and at low latencies. We’re currently emitting VM availability states (Available, Unavailable, and Unknown) to the Health Resources table in Azure Resource Graph, so you can perform complex Kusto Query Language (KQL) queries for sieving through large datasets at once. This functionality is handy for tracking historical changes in VM availability, for building custom dashboards, and for performing detailed investigations across numerous resource properties spread across multiple tables.
Figure 1: Azure Resource Graph Explorer Window with query and results, to demonstrate fetching data from the HealthResources table.
We are planning to add failure details and degraded VM scenarios to the Health Resources table in Azure Resource Graph, later this year. These details will ensure you are properly informed on the cause and impact of any failures—so you can either failover, reboot in place, or take the appropriate mitigations to prevent end-user impact.
Navigate to Azure Resource Graph Explorer on the Azure portal to get started with any of the KQL queries published for the Health Resources table.
Introducing VM availability metric in Azure Monitor
We’re also pleased to announce the private preview of an out-of-box VM availability metric in Azure Monitor, for a curated metric alerting and monitoring experience.
Metrics in Azure Monitor are great for monitoring and analyzing time series representations of VM availability for quick and easy debugging, receiving scoped alerts on concerning trends, catching early indicators of degraded availability, correlating with other platform metrics, and more.
The metric allows you to track the pulse of your VMs—during expected behavior, the metric displays a value of 1. In response to any VM availability disruptions, the metric dips to a 0 for the duration of impact. In case of an Azure infrastructure outage, we will emit nulls represented as a dotted line on the portal.
Figure 2: Screenshot of VM availability metric as seen on Metrics Explorer in the Azure portal, with occasional dips to reflect VM availability disruptions.
We released the private preview of the metric as phase one of our rollout plan, and are currently collecting customer feedback, to further improve our offering. We are planning to add failure details such as metric dimensions and platform logs next year, to allow you to precisely alert on failure scenarios that are impactful.
Coming soon
The two monitoring options introduced above are just the beginning for Project Flash! We will continue to build upon our existing solutions by improving data quality and failure attribution. In parallel, we are designing two new monitoring offerings to meet your latency and mitigation needs, while also investing heavily in the underlying platform to make our fault detection more resilient and comprehensive.
Azure Event Grid for instantaneous notifications
Successfully running business-critical applications requires hyper-awareness of any VM availability impacting event, so remediation actions can be triggered instantaneously to prevent end-user impact. To support you in your daily operations, we are planning to design a notification mechanism that leverages the low-latency technology of Azure Event Grid. This will allow you to simply subscribe to an Event Grid system topic, and route scoped events via event handlers to any downstream tooling, instantaneously.
Automate and tailor platform recovery policies
Considering the numerous ongoing investments to improve your VM availability monitoring experience, Project Flash intends to empower you even further by providing you knobs to customize recovery policies triggered by the platform, in response to cases of VM availability disruptions.
One such knob we are designing is the ability to opt-out of Service Healing for single-instance VMs, in response to a specific set of unanticipated Availability disruptions. This knob will be made available via the portal or at the time of VM deployment and can be updated dynamically. Note that leveraging this feature will render the usual Azure Virtual Machine availability SLAs ineffective.
In the future, we will explore introducing knobs to also opt-out of other applicable recovery policies (for example, Live Migration or Tardigrade), to ensure you can easily adapt to your ever-changing mitigation needs.
Ongoing platform quality investments
While the first phase is designed to meet your current observability needs, we remain focused on our long-term goal of delivering a world-class observability experience surrounding VM availability. We are extremely excited for all the data enrichments and technology advancements that will contribute to this experience, so here’s an early look at our roadmap of planned investments:
Fault detection and attribution: We are continuously evolving our underlying infrastructure to detect and attribute failures both precisely and instantaneously—so that we can reduce unknown or missing health status reports, emit actionable failure details, and handle platform recovery customizations. This remains our top investment area on which we continue to iterate every cycle.
Root cause analysis (RCA) automation: We are planning to implement easy tracking mechanisms for every unique VM downtime, along with automatic construction and emission of detailed downtime RCA statements to reduce manual tracking and churn on your end.
AIOps integration: We are looking to leverage the tremendous advancements being made in AIOps across Microsoft, for enabling smart insights and anomaly detection and diagnosis across the multitude of data points on VM Availability.
Centralized and cohesive user experience: We acknowledge that a consequence of our near-term approach is that across our different services we have multiple monitoring, alerting, and recovery tools which may lead to a confusing and disparate experience for you. This is a problem we intend to solve with our final phase. Our north star goal is to provide end-users access to distinct and necessary representations of VM availability, consolidated within Azure Monitor, and categorized according to common usage patterns for discoverability, ease of use and intuitive onboarding.
Learn more
This list is certainly not exhaustive as we have multiple enrichments planned as part of our long-term strategy. To reiterate, our intention with Project Flash is to make VM availability monitoring extremely intuitive, comprehensive, and seamless—so you are always prepared for and informed about any changes in the health of your workloads, ultimately to maintain your own SLAs and business promises.
We will continue to share updates on Project Flash through blogs like this, to ensure you stay up to date on the latest. Stay tuned!
Quelle: Azure
AWS kündigt die allgemeine Verfügbarkeit von AWS CloudFormation Hooks an, einer Funktion, mit der Kunden benutzerdefinierte Logik aufrufen können, um Aktionen zu automatisieren oder Ressourcenkonfigurationen vor einer CloudFormation-Stack-Operation zum Erstellen, Aktualisieren oder Löschen zu überprüfen. Mehr als 1 Million Kunden nutzen AWS CloudFormation jede Woche, um ihre Cloud-Anwendungen und -Infrastruktur sicher, vorhersehbar und wiederholbar zu modellieren, bereitzustellen und zu verwalten. Mit AWS CloudFormation Hooks können Kunden jetzt Ressourceneigenschaften validieren und bei nicht konformen Ressourcen eine Warnung senden oder den Bereitstellungsvorgang verhindern, um Sicherheits- und Compliance-Risiken zu reduzieren, den betrieblichen Aufwand zu senken und die Kosten zu optimieren.
Quelle: aws.amazon.com
AWS Elemental MediaConnect unterstützt jetzt AWS PrivateLink und bietet direkten Zugriff auf MediaConnect über einen privaten Endpunkt innerhalb des virtuellen privaten Netzwerks eines Kunden. Jetzt können Sie auf MediaConnect API über private IP-Adressen in Ihrer Virtual Private Cloud (VPC) zugreifen und müssen keine öffentlichen IPs mehr verwenden.
Quelle: aws.amazon.com