Kategorie: Google Cloud Platform

New Cloud Asset Inventory capabilities help assess your Google Cloud environment

Businesses that operate in complex cloud environments, large fleets, or sophisticated security operations all require visibility into their cloud assets in order to keep their teams nimble and their data secure. Cloud Asset Inventory (CAI) helps these teams understand their Google Cloud  and Anthos environments by providing complete visibility, real-time monitoring, and powerful asset analysis capabilities. Today, Cloud Asset Inventory gets four new capabilities that help you understand your environment more clearly and easily than ever before.New user interface eases asset and insight discovery Cloud Asset Inventory console preview is now publicly available for GCP and Anthos customers. This preview provides insights into your cloud footprint, history and details of resource usage with powerful filtering and search capabilities. For example, you can view your global distribution of resources and policies, how your GCE VM footprint has been changing over time, as well as full metadata and change history for all your assets. The CAI console can be filtered at the organization, folder, or project-level, so each user can view the resources they have permissions for down to project level granularity.Asset discovery and Datadog integrationA new asset list service in CAI provides quick and comprehensive asset discovery, including asset history, without needing to export the data to a storage destination. Datadog, a leading multi-cloud monitoring and security service provider, relies on deep integration with CAI for service and asset discovery. Datadog has been piloting and taking full advantage of the newly released asset list service. Datadog Product Manager, Steve Harrington, commented: “Google’s new Cloud Asset Inventory API provides us with an immensely valuable, single source of truth for determining the resources present in a given GCP environment. Along with its rich metadata, this enables us to enhance multiple aspects of our integration with GCP, including streamlined metric collection and ingestion of custom labels. We plan to continue building around Cloud Asset Inventory in the future to improve existing features, and are envisioning ways it could help us provide entirely new insights to our customers.”Answer “who can access what resources?”Determining authoritative answers to security-related questions like “Who can read data from my storage bucket that contains PII?” or “Does a terminated employee still have any remaining access to my system?” can be difficult and time consuming. This is why access management and identity certification is one of the top security priorities for enterprises running workloads in the cloud. To help alleviate this challenge, the new Policy Analyzer capability in CAI thoroughly analyzes the relationship between IAM policies and resources. The analysis includes powerful and efficient group expansion, service account impersonation, conditional access analysis, resource expansion, and more. You can even export the results to a BigQuery table or Cloud Storage bucket for further analysis and record keeping. CAI’s enhanced UI makes it even easier for you to build your own flexible queries and quickly get to a comprehensive answer.Create asset posture visibility Cloud Asset Inventory now provides seven types of Asset Insights through the Active Assist platform. These new asset insights help proactively detect anomalies within your organization’s IAM policies, which may be opportunities to improve your security posture. The insights can be aggregated at the Organization, Folder or Project level. The seven new Asset Insights include: External members in IAM policies. External users that impersonate your service accounts.External members as policy editors.External users who can view cloud storage buckets.Terminated users/groups that are still in IAM policiesIAM policies containing all users or all authenticated users.Projects with only terminated users as owners.As a Google Cloud customer you can get started and use all the recently released capabilities and features immediately; check out our documentation to see how. We’d love to hear your feedback; email us with any questions or concerns!Related ArticleKeep a better eye on your Google Cloud environmentThe fully managed metadata inventory service from Google Cloud — Cloud Asset Inventory — can help manage all your cloud assets.Read Article
Quelle: Google Cloud Platform

Introducing container-native Cloud DNS: Global DNS for Kubernetes

Kubernetes networking almost always starts with a DNS request. DNS has broad impacts on your application and cluster performance, scalability, and resilience. That is why we are excited to announce the release of container-native Cloud DNS—the native integration of Cloud DNS with Google Kubernetes Engine (GKE) to provide in-cluster Service DNS resolution with Cloud DNS, our scalable and full-featured DNS service. Several new capabilities are introduced when using Cloud DNS as the cluster DNS provider:Managed DNS that removes the need for in-cluster DNS PodsDNS resolution local to every GKE node for high throughput, horizontally scalable DNS performanceMulti-regional, cross-cluster service discovery for GKE ServicesIntegration with Google Cloud’s operations suite for DNS monitoring and loggingContainer-native Cloud DNS lowers the operational burden on the cluster administrator by obviating the need for clusters to allocate resources for managing DNS. It also scales transparently—you no longer need to worry about bottlenecks due to increased demand for name resolutions. It provides capabilities for public and private DNS resolution for GKE applications outside of the cluster. This flexibility opens up many service discovery use-cases which reduce friction introduced by cluster boundaries.Finally, existing tooling, monitoring, and logging for Cloud DNS can be extended for all DNS resolution inside GKE as well without separate monitoring systems for containers and VMs. All in all, Cloud DNS provides a highly-available, globally distributed DNS infrastructure, managed entirely by GoogleWith Cloud DNS, every new Service creates a DNS record that can be resolved locally on the GKE node using the Cloud DNS dataplane. Cloud DNS local caching and resolution ensures that DNS requests don’t need to go across the network, improving performance dramatically.Click to enlargeCluster-scope DNSWith a new mode of operation called cluster-scope DNS, each GKE cluster gets its own private DNS zone. You can only resolve Services within the scope of this DNS zone, and VMs or Pods outside the cluster have no visibility to the DNS records of that cluster. This allows GKE clusters using kube-dns to transparently migrate to Cloud DNS without having to make application changes. The records are automatically synced between Cloud DNS with the ClusterIP or Pod IPs depending on the type of Service:Click to enlargeVPC-scope DNSThanks to its global, multi-regional scale, Cloud DNS enables a new mode of operation in GKE called VPC-scope DNS. This enables GKE DNS records to be resolvable within the entire VPC for truly global, multi-cluster service discovery.With the new ability to customize the cluster DNS domain, GKE can now provide unique domains for each cluster, allowing them to be uniquely resolved from a GKE cluster in a different region, a VM that isn’t part of GKE, or even an on-premises client that has access across a VPN.Click to enlargeVPC-scope DNS creates a single service discovery domain across all your GKE clusters and clients in the network. This seamless service discovery is completely automatic and can easily be enabled on a per-cluster basis.Between global service discovery, local DNS resolution on every node, and integration with Google Cloud’s operations suite and observability, container-native Cloud DNS vastly improves the operator experience while greatly improving application performance. Give it a try today and see for yourself how much your team can benefit!Related ArticleGKE best practices: Exposing GKE applications through Ingress and ServicesWe’ll walk through the different factors you should consider when exposing applications on GKE, explain how they impact application expos…Read Article
Quelle: Google Cloud Platform

How BIG is Cloud Bigtable?

“Building an application that needs low latency and high throughput?”—You need a database that can scale for a large number of reads and writes. Cloud Bigtable is designed to handle just that. Cloud Bigtable is a fully managed wide-column NoSQL database that scales to petabyte-scale. It’s optimized for low latency, large numbers of reads and writes, and maintaining performance at scale. It offers really low latency of the order of single-digit milliseconds. It is an ideal data source for time series and MapReduce-style operations. Bigtable supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark. It also integrates with Google Cloud ecosystem including Memorystore, BigQuery, Dataproc, Dataflow and more. Click to enlargeSome Cloud Bigtable FeaturesData is by default encrypted with Google managed encryption keys but, for specific compliance and regulatory requirements if customers need to manage their own keys, customer managed encryption keys (CMEK) are also supported.Bigtable backups let you save a copy of a table’s schema and data, then restore from the backup to a new table at a later time. Backups can help you recover from application-level data corruption or from operator errors such as accidentally deleting a table.Scale and High Availability (HA)How BIG is Bigtable? Overall,  Bigtable has nearly 10 Exabytes of data under management. It delivers highly predictable performance that is linearly scalable. Throughput can be adjusted by adding/removing nodes — each node provides up to 10,000 operations per second (read and write). You can use Bigtable as the storage engine for large-scale, low-latency applications as well as throughput-intensive data processing and analytics. It offers high availability with an SLA of 99.5% for zonal instances. It’s strongly consistent in a single cluster; replication between clusters adds eventual consistency. If you leverage Bigtable’s multi cluster routing across two clusters, the  SLA increases to 99.99% and if that routing policy is utilized across clusters in 3 different regions you get a 99.999% uptime SLA.Replication for Cloud Bigtable enables you to increase the availability and durability of your data by copying it across multiple regions or multiple zones within the same region. To use replication in a Bigtable instance, just create an instance with more than 1 cluster or add clusters to an existing instance. Bigtable supports up to 4 replicated clusters located in Google Cloud zones where Bigtable is available. Placing clusters in different zones or regions enables you to access your data even if one zone or region becomes unavailable. Bigtable treats each cluster in your instance as a primary cluster, so you can perform reads and writes in each cluster. You can also set up your instance so that requests from different types of applications are routed to different clusters. The data and changes to data are synchronized automatically across clusters. How does it optimize throughputThrough separation of processing and storage, Cloud Bigtable is able to automatically configure throughput by adjusting the association of nodes and data. In the rebalancing example, if Node A is experiencing a heavy load, the routing layer can move some of the traffic to a less heavily loaded node, improving overall performance. Resizing comes into play when a node is added to again ensure a balanced load across nodes, ensuring best overall throughput.Click to enlargeChoice of app profile and traffic routing can also affect performance. An app profile with multi-cluster routing automatically routes requests to the closest cluster in an instance from the perspective of the application, and the writes are then replicated to the other clusters in the instance. This automatic choice of the shortest distance results in the lowest possible latency. An app profile that uses single-cluster routing can be optimal for certain use cases, like separating workloads or to have read-after-write semantics on a single cluster, but it will not reduce latency in the way multi-cluster routing does.Replication can improve read throughput, especially when you use multi-cluster routing. And it can reduce read latency by placing your data geographically closer to your users.  Write throughput does not increase with replication because write to one cluster must be replicated to all other clusters in the instance. Resulting in each cluster spending the CPU resources to pull changes from the other clusters.ConclusionBigtable is a database of choice for use cases that require a specific amount of  scale or throughput with strict latency requirements, such as IoT, AdTech, FinTech, gaming and ML based personalizations. You can ingest 100s of thousands of events per second from websites or IoT devices through Pub/Sub, process them in Dataflow and send them to Cloud Bigtable. For a more in-depth look into Cloud Bigtable check out the documentation or join the upcoming webinar with our experts Build high-throughput, low-latency apps with Cloud Bigtable.For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.Related ArticleBigtable vs. BigQuery: What’s the difference?Bigtable vs BigQuery? What’s the difference? In this blog, you’ll get a side-by-side view of Google BigQuery and Google Cloud Bigtable.Read Article
Quelle: Google Cloud Platform

One click retail: How Marxent uses Google Cloud to power self-service 3D shopping apps

As ecommerce for home goods exploded in popularity during COVID, furniture and DIY retailers looked to find new ways to grow online transaction sizes to in-store levels. Shopping for furniture and home improvement projects has always been challenging online. Furniture, kitchen cabinets, fixtures, and appliances become a part of daily life, are challenging to return, and have a low purchase frequency. Once a shopper makes a decision, they tend to live with it for many years. These are visual, tactile, decisions that require measurements, style choices, budgeting, an understanding of available products, and an involved consideration processes. During the pandemic, retailers turned to 3D to enhance these virtual shopping experiences. Inspiration and visualization cultivate confidenceOur 3D Room Commerce company, Marxentoffers 3D visualization and configuration solutions that help retailers sell complex, configurable products online through consumer-facing 3D design and visualization apps. The Marxent 3D Room Planner with HD Renders helps shoppers to visualize how furniture or kitchen cabinet configurations will look in the specific floorplan of their home. Founded in 2011, Marxent envisions a world where buying a dining table or remodeling an entire kitchen is as easy as buying a car from Carvana or ordering dinner through  Bite Squad. Our 3D apps create a streamlined inspiration to transaction to advocacy model that cultivates shopper confidence and allows retailers to sell the whole room, not just individual items. Using Google Cloud as a foundation, we help some of the largest retail and home goods companies in the world provide exceptional customer journeys. We trust Google Cloud because our clients trust us to make it faster and easier for shoppers to buy semi-custom, configurable projects.Embracing the power of 3D to super-charge ecommerce It’s inarguable: e-commerce is on the rise. More people than ever before are shopping online for furniture, kitchen cabinets, decking, and other large-scale configurable products. Customers who design with a retailer, usually buy from them. When shoppers visit stores and showrooms, they find inspiration in merchandised scenes that illustrate how products like sofas, chairs, rugs and lamps work together to accomplish a look. Skilled sales people make suggestions and offer advice on how to put pieces together. They may even work up a quick floor plan to show how multiple items work together in a room. In store, the inspiration phase is intimately tied to consideration and, ultimately, to driving transactions.By contrast, online shoppers typically start home projects by seeking inspiration and ideas from Pinterest, Instagram and unbranded online image searches. Once they have formed their style preferences, shoppers keep searching to compare products across multiple retailers, plan their project, and put together a final budget. To own the whole project sale, retailers need to own the entire inspiration to transaction to advocacy journey. With both online and in-store applications, Retailers leverage Marxent’s 3D Room Planner app to build shopper confidence, capture the whole room sale and win the customer over.PRE-RENDERED MID-POLY 3D SCENEClick to enlargePOST-RENDERED MID-POLY 3D SCENE – This is a slightly different angle of the same room that has been rendered into a “Raw Render” (rendered in under 2 minutes).Click to enlargeThrough Marxent’s 3D Room Commerce solution, users experience a cyclical inspiration to transaction to advocacy journey. It starts with shoppers viewing inspirational images and media online. Using Marxent’s applications, they can design directly from inspirational images to create a custom, configured space without any product catalog knowledge. Shoppers can visualize the products they love together and in the context of their own floor plan instead of navigating product pages and wondering if items will work together. Then, they can add the whole room to a shopping cart with a single click. While this virtual experience does lead to a transaction, it also allows users to save, collaborate on, and share the spaces they’ve created. They become advocates by sharing their projects on social media, starting the inspirational content cycle again. Putting an end to manual operationsTo deliver renders at scale, Marxent needed to update their cloud render solution. Initially, our 3D Art team operated a manual on-premise render fleet. However, this required many hours of manual setup, configuration, and operation. We also had to manage expensive graphics servers—bare metal, CPUs, GPUs, RAM, HDD, and more. The only solution was to automate the 3D rendering process and empower end-users to rapidly create their own 3D room renders. When we were evaluating new solutions, we saw distinct advantages in the Google Cloud Platform that would help them safely and securely scale their business, while strengthening their partnerships with end customers. For example, in moving to Google Cloud, we could automate and scale our rendering process without having to manage fleets of physical servers. We also viewed the platform as an asset due to Google’s secure-by-design infrastructure, agility, data analytics capabilities, and potential for joining the Marketplace.Creating magical customer experiences that inspire purchaseTo provide our customers and shoppers with contextual experiences, Marxent’s applications use mid-poly 3D models that balance speed and realism. These models provide a latency-free, real-time design experience that can be rendered into scenes that are realistic enough to be perceived as photos on social media. The complex process of rendering these images requires a combination of efficiency, speed, analytics, and consistent performance that Google Cloud provides. When configured with powerful and speedy gaming GPUs, Marxent can provide fast rendering that meets customers and shopper demands. Here’s a look at our HD Renders application.Click to enlargeBefore a user can request an HD Render, they must create a room in the Marxent 3D Room Planner. Once requested, Marxent pulls the saved project from the database and kicks off the process with Cloud Pub/Sub. The project loads into a gaming GPU, using the same platform code running when the user first creates the room in the application. It boots up the app in the cloud to load the room.The code then scours the space and prepares it for rendering, adjusting texture formats, and adding in lighting. After going through the render engine, the project automatically uploads to Cloud Storage. Finally, the user receives a link to the final product. Throughout, Cloud Pub/Sub handles messages ensuring the right event processes are happening, such as rendering success or failure.Using this process, it’s possible to create dozens of images out of a single scene, trading products in and out of a floor plan by leveraging a complete catalog of content geometries and covers, textures, and finishes.Utilizing Google Cloud throughout the buying journey Today, Marxent’s applications power world-class retailers with AR, VR, and 3D commerce experiences. We use cutting-edge graphics hardware to create renderings in less than 2 minutes per screenshot, often much faster. We’re also saving money as we no longer have to manage expensive servers or purchase expensive hardware upfront. Our clients are happier because we have passed on the cost savings to them while now having limitless scaling capabilities to meet demand.By partnering with Google Cloud, Marxent can confidently offer our customers secure applications built on infrastructure with advanced security tools that support compliance and data confidentiality. Backed by a globally consistent platform, we can also help brands build reliable purchasing experiences across customer touchpoints—without fear of downtime during peak sales periods. This strategic partnership has allowed us to provide a best-in-breed, customer-first experience that our customers demand while providing the reliability that our partners expect.With customers demanding seamless shopping experiences, Marxent’s 3D technologies open doors to new, easier, more convenient, and more satisfying shopping experiences that empower consumers to buy the right products the first time. If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.Related ArticleGoogle Cloud EMEA Retail & Consumer Goods Summit: The Future of RetailJoin us at the Google Cloud Retail & Consumer Goods Summit and learn how combining technology and business insights can solve retail chal…Read Article
Quelle: Google Cloud Platform

AI Simplified: Managing ML data sets with Vertex AI

At Google I/O this year, we introduced Vertex AI to bring together all our ML offerings into a single environment that lets you build and manage the lifecycle of ML projects. In a previous post, we gave you an overview of Vertex AI, sharing how it supports your entire ML workflow—from data management all the way to predictions. Today, we’ll talk a little about how to manage ML datasets with Vertex AI.Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can help. Vertex AI provides tools for every step of the machine learning workflow—from managing data sets to different ways of training the model, evaluating, deploying, and making predictions. It also supports varying levels of ML expertise, so you don’t need to be an ML expert to use Vertex AI.Types of data you can use in Vertex AIDatasets are the first step of the machine learning lifecycle—to get started you need data, and lots of it. Vertex AI currently supports managed datasets forfour data types—image, tabular, text, and videos. ImageImage datasets let you do:Image classification—Identifying items within an image.Object detection—Identifying the location of an item in an imageImage segmentation—Assigning labels to pixel level regions in an image.To ensure your model performs well in production, use training images similar to what your users will send. For example, if users are likely to send low quality images, be sure to have blurry and low resolution images in your data set. Don’t forget to include different angles, backgrounds, and resolutions. We recommend you include at least 1,000 images per label (item you want to identify), but you can always get started with 10 per label. The more examples you provide, the better your model will be.TabularTabular datasets enable you to do:Regression—Predicting a numerical value.Classification—Predicting a category associated with a particular example.Forecasting—Predicting the likelihood of sudden events or demands.Tabular data sets support hundreds of columns and millions of rows. TextWith text datasets, you can do:Classification—Assigning one or more labels to an entire document.Entity extraction—Identifying custom text entities within a document, like “too expensive” or “great value”.Sentiment analysis—Identifying the overall sentiment expressed in a block of text, for example, if a customer was happy or upset or frustrated.VideoVideo datasets enable:Classification—Labeling entire videos, shots, or frames.Action recognition—Identifying clips video clips where specific actions occur.Object tracking—Tracking specific objects in a video.Creating and managing datasets in Vertex AINow that we’ve covered the different types of data you can use, let’s shift to creating and managing those datasets. In the Cloud Console, go to Vertex AI dashboard page and click Datasets, then click Create Project.Say you want to classify items within a set of photos. Create an image dataset and select image classification. You can import files directly from your computer, which will be stored in Cloud Storage. Then, you’ll need to add the corresponding labels (items you want to identify) for your images. If you already have labels, you can use the Import File option to import a CSV with your image URLs and their labels. If your data is not labeled and you would like human help to label it, you can use the Vertex AI data labeling service. Once the files are uploaded, you can create labels and assign them to the images. You can also analyze the images in the data set, the number of images per label, and a few other properties. Depending on the type of data you use, your options might vary slightly. For example, if you want to use tabular data, you could upload a CSV file from your computer, use one from Cloud Storage, or select a table from BigQuery directly. Once you select the table, the data is available for analysis.More to comeThis concludes our overview of creating and managing datasets in Vertex AI. In a future installment, we’ll go over the next phase of the machine learning workflow: building and training ML models. If you enjoyed this post, keep an eye out for more AI Simplified episodes on YouTube. In the meantime, here’s where you can learn more about Vertex AI.Related ArticleWhat is Vertex AI? Developer advocates share moreDeveloper Advocates Priyanka Vergadia and Sara Robinson explain how Vertex AI supports your entire ML workflow—from data management all t…Read Article
Quelle: Google Cloud Platform

Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics

We are excited to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Thank you to our strong community of customers and partners for working with us to deliver a customer focused product. We believe Forrester’s recognition is an acknowledgement of our leadership across an integrated set of capabilities that rely on data to drive transformation. We were also honored to be named a leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021. Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria and according to the report: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability. Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.” Harnessing the power of real-time dataThe speed with which businesses are able to respond to change is the difference between those that successfully navigate the future and those that get left behind. In order to accelerate their digital transformation, reimagine their business and leverage the power of real-time data,  today’s data leaders require a streaming analytics platform that provides both depth and breadth.  Cloud Pub/Sub and Cloud Dataflow, based on more than a decade of experience in internet scale systems for Google’s own needs, provide customers with a reliable, scalable, performant platform. In addition, we’ve designed these products for ease of use to make streaming analytics accessible to more users, which is why customers such as Sky and others from across all industries use Dataflow to run streaming analytics workloads.5 out of 5 across key streaming analytics criteriaWhile Forrester gave Dataflow a score of 5 out of 5 in 12 criteria, the product achieved the highest possible scores in areas that are top of mind for our customers.We continue to be focused on solving problems that matter to you. For example, just in the last month we announced Dataflow Prime and  Auto Sharding for BigQuery – two new auto tuning capabilities that bring efficiency and simplicity to your streaming pipelines. Dataflow achieves highest score possible in strategy With Google, organizations gain an industry leading product and a partner that has the vision and strategy to help you tackle new business challenges and provide delightful experiences to your customers.In summary, we are honored to be a Leader in The Forrester Wave™, Streaming Analytics, and look forward to continuing to innovate and partner with you on your digital transformation journey. Download the full report: The Forrester Wave™: Streaming Analytics, Q2 2021 and check out these smart analytics reference patterns. To learn more about Dataflow, visit our website and get to know the product by taking an interactive tutorial. You can also watch recordings from the Data Cloud Summit event (May 2021), where we provided an in-depth view of new product innovations in Dataflow and other data analytics products.Related ArticleGartner 2020 Magic Quadrant for Cloud Database Management Systems names Google a LeaderGartner’s first-ever database management systems (DBMS) Magic Quadrant (MQ) names Google Cloud a Leader.Read Article
Quelle: Google Cloud Platform