Kategorie: Google Cloud Platform

Introducing Firehose: An open source tool from Gojek for seamless data ingestion to BigQuery and Cloud Storage

Indonesia’s largest hyperlocal company, Gojek has evolved from a motorcycle ride-hailing service into an on-demand mobile platform, providing a range of services that include transportation, logistics, food delivery, and payments. A total of 2 million driver-partners collectively cover an average distance of 16.5 million kilometers each day, making Gojek Indonesia’s de-facto transportation partner.To continue supporting this growth, Gojek runs hundreds of microservices that communicate across multiple data centers. Applications are based on an event-driven architecture and produce billions of events every day. To empower data-driven decision-making, Gojek uses these events across products and services for analytics, machine learning, and more.Data warehouse ingestion challenges To make sense of large amounts of data — and to better understand customers for the purpose of app development, customer support, growth, and marketing purposes — data must first be ingested into a data warehouse. Gojek uses BigQuery as its primary data warehouse. But ingesting events at Gojek’s scale, with rapid changes, poses the following challenges:With multiple products and microservices offered, Gojek releases new Kafka topics almost every day and they need to be ingested for analytical purposes. This can quickly result in significant operational overhead for the data engineering team that is deploying new jobs to load data into BigQuery and Cloud Storage. Frequent schema changes in Kafka topics require consumers of those topics to load the new schema to avoid data loss and capture more recent changes. Data volumes can vary and grow exponentially as people start building new products and logging new activities on top of a new topic. Each topic can also have a different load during peak business hours. Customers need to handle the rising volume of data to quickly scale per their business needs.Firehose and Google Cloud to the rescue To solve these challenges, Gojek uses Firehose, a cloud-native service to deliver real-time streaming data to destinations like service endpoints, managed databases, data lakes, and data warehouses like Cloud Storage and BigQuery. Firehose is part of the Open Data Ops Foundation (ODPF), and is fully open source. Gojek is one of the major contributors to ODPF.Here are Firehose’s key features:Sinks – Firehose supports sinking stream data to the log console, HTTP, GRPC, PostgresDB (JDBC), InfluxDB, Elastic Search, Redis, Prometheus, MongoDB, GCS, and BigQuery.Extensibility – Firehose allows users to add a custom sink with a clearly defined interface, or choose from existing sinks.Scale – Firehose scales in an instant, both vertically and horizontally, for a high-performance streaming sink with zero data drops.Runtime – Firehose can run inside containers or VMs in a fully-managed runtime environment like Kubernetes.Metrics – Firehose always lets you know what’s going on with your deployment, with built-in monitoring of throughput, response times, errors, and more.Key advantagesUsing Firehose for ingesting data in BigQuery and Cloud Storage has multiple advantages. Reliability Firehose is battle-tested for large-scale data ingestion. At Gojek, Firehose streams 600 Kafka topics in BigQuery and 700 Kafka topics in Cloud Storage. On average, 6 billion events are ingested daily in BigQuery, resulting in more than 10 terabytes of daily data ingestion.  Streaming ingestionA single Kafka topic can produce up to billions of records in a day. Depending on the nature of the business, scalability and data freshness are key to ensuring the usability of that data, regardless of the load. Firehose uses BigQuery streaming ingestion to load data in near-real-time. This allows analysts to query data within five minutes of it being produced.Schema evolutionWith multiple products and microservices offered, new Kafka topics are released almost every day, and the schema of Kafka topics constantly evolves as new data is produced. A common challenge is ensuring that as these topics evolve, their schema changes are adjusted in BigQuery tables and Cloud Storage. Firehose tracks schema changes by integrating with Stencil, a cloud-native schema registry, and automatically updates the schema of BigQuery tables without human intervention. This reduces data errors and saves developers hundreds of hours. Elastic infrastructureFirehose can be deployed on Kubernetes and runs as a stateless service. This allows Firehose to scale horizontally as data volumes vary.Organizing data in cloud storage Firehose GCS Sink provides capabilities to store data based on specific timestamp information, allowing users to customize how their data is partitioned in Cloud Storage.Supporting a wide range of open source softwareBuilt for flexibility and reliability, Google Cloud products like BigQuery and Cloud Storage are made to support a multi-cloud architecture. Open source software like Firehose is just one of many examples that can help developers and engineers optimize productivity. Taken together, these tools can deliver a seamless data ingestion process, with less maintenance and better automation.How you can contributeDevelopment of Firehose happens in the open on GitHub, and we are grateful to the community for contributing bug fixes and improvements. We would love to hear your feedback via GitHub discussions or Slack.Related ArticleTransform satellite imagery from Earth Engine into tabular data in BigQueryWith Geobeam on Dataflow, you can transform Geospatial data from raster format in Earth Engine to vector format in BigQuery.Read Article
Quelle: Google Cloud Platform

Pride Month: Q&A with bunny.money founders about saving for good

June is Pride Month—a time for us to come together to bring visibility and belonging, and celebrate the diverse set of experiences, perspectives, and identities of the LGBTQ+ community. This month, Lindsey Scrase, Managing Director, Global SMB and Startups at Google Cloud, is showcasing conversations with startups led by LGBTQ+ founders and how they use Google Cloud to grow their businesses. This feature highlights bunny.money and its founders, Fabien Lamaison, CEO, Thomas Ramé, Technology Lead, and Cyril Goust, Engineering Lead. Lindsey: Thanks Fabien, Thomas, and Cyril. It’s great to connect with you and talk about bunny.money. I love how you’re bringing a creative twist to fintech and giving back to communities. What inspired you to found the company?Fabien: One of my favorite childhood toys was an old-fashioned piggy bank. I remember staring at it and trying to figure out how much of my allowance should be saved, spent, or given to charity. As you can imagine, there were lots of ideas racing through my mind but saving and giving back were always important to me. Years later, I realized I could combine my passions for banking, technology, and helping others by creating a fintech service that makes it easy for people to save while donating to their favorite causes.Fabien Lamaison, CEO of bunny.moneyLindsey: My brothers and I did something similar where we allocated a portion of any money we made as kids to giving. And I too had a piggy bank – a beautiful one that could only be opened by breaking it. Needless to say it was a good saving mechanism! It’s inspiring to see you carrying your personal value forward into bunny.money to help others do the same. Tell us more about bunny.money?Fabien: bunny.money plays with the concept of reimagining saving—and offers a way to positively disrupt conventional banking. For us bunnybankers, financial and social responsibility go hand in hand. We empower people to build more sustainable, inclusive financial futures. Looking ahead, we not only want to help people set up recurring schedules for saving and donating, but also offer more options for socially responsible investing and help companies better match employee donations to charitable causes and build out retirement plans.Lindsey: It sounds like you’re not only disrupting traditional banking services but also how people manage their finances. How does bunny.money serve its customers?Fabien: bunny.money is a fintech company founded on the principles of providing easy, free, and ethical banking services. Our comprehensive banking platform enables customers to quickly open savings wallet and schedule recurring deposits.Thomas: bunny.money is also a fintech bridge that connects people and businesses to the communities and causes they care about. With bunny.money, customers can make one-time or recurring donations to the nonprofits of their choice. bunny.money doesn’t charge recipients fees to process donations. We give customers the option of offering us a tip, but it’s not required.Lindsey: So with bunny.money, what are some of the nonprofits people can donate to?Fabien: Over 30 organizations have already joined bunny.money’s nonprofit marketplace, includingStartOut,TurnOut,Trans Lifeline, and Techqueria. Some are seeing donations increase by up to 20 percent as they leverage bunny.money to gamify fundraising, promote social sharing, and encourage micro-donations from their members and supporters.Cyril: bunny.money also helps people discover local causes and nonprofits such as food banks requesting volunteers, parks that need to be cleaned, and mentoring opportunities. I’m particularly excited to see bunny.money help people build a fairer, greener society by donating to environmental nonprofits, including, Carbon Lighthouse,Sustainable Conservation, Public Land Water Association, back2earth andFARMS. We also decided to “lead by the example” and pledge to give 1% of our revenues to 1% for the Planet.Lindsey: Given your business and the services you offer, I imagine you’ve encountered immense complexity along the way. What were some of the biggest challenges that you had to overcome?Fabien: One of our biggest challenges was helping people understand saving for good, and purpose-led banking, which is a relatively new idea in fintech. Although there are plenty of mobile banking apps, most don’t offer an easy way for people to improve their personal finances and donate to their favorite causes in one convenient place.Cyril: On the technical side, we needed to comply with strict industry regulations, including all applicable requirements under the Bank Secrecy Act and the USA PATRIOT Act. These regulations protect sensitive financial data and help fight against fraudulent activities such as money laundering.Lindsey: Can you talk about how Google Cloud is helping you address these challenges?  Thomas: Protecting client data is a top priority for us, so we built bunny.money on thehighly secure-by-design infrastructure of Google Cloud. Google Cloud automatically encrypts data in transit and at rest, and the solutions comply with all major international security standards and regulations right out of the box. Although we serve customers in the U.S. today, Google Cloud distributed data centers will allow us to meet regional security requirements and eventually reach customers worldwide with quality financial services.Thomas Ramé, Technology Lead at bunny.moneyFabien: We wanted to build a reliable, feature-rich fintech platform and design a responsive mobile app with an intuitive user interface (UI). We knew from experience that Google Cloud is easy to use and offers integrated tools, APIs, and solutions. We also wanted to tap into the deep technical knowledge of theGoogle for Startups team to help us scale bunny.money and affordably trial different solutions with Google for Startups Cloud Program credits.Cyril: As aCertified Benefit Corporation™ (B Corp™), it is also important for us to work with companies that align with the values we champion such as diversity and environmental sustainability. Google Cloud iscarbon neutral and enables us to accuratelymeasure, report, and reduce our cloud carbon emissions. Lindsey: This is exactly how we strive to support startups at all stages – with the right technology, offerings, and support to help you scale quickly and securely, all while being the cleanest cloud in the industry. Can you go into more detail about the Google Cloud solutions you use—and how they all come together to support your business and customers? Fabien: Our save for good® mobile app enables customers to securely create accounts, verify identities, and connect to external banks in just under four minutes. Thomas: With Google Cloud, bunny.money consistently delivers a reliable, secure, and seamless banking experience. Since recently launching our fintech app, we’ve already seen an incredible amount of interest in our services that enable people to grow financially while contributing to causes they are passionate about. Right now, we’re seeing customers typically allocate about 10 percent of each deposit to their favorite charities.Cyril: The extensive Google Cloud technology stack helps us make it happen. We can useBigQuery to unlock data insights,Cloud SQL to seamlessly manage relational database services, andGoogle Kubernetes Engine (GKE) to automatically deploy and scale Kubernetes. These solutions enable us to cost-effectively scale bunny.money and build out a profitable fintech platform.Cyril Goust, Engineering Lead at bunny.moneyThomas: In addition to the solutions Cyril mentioned, we useCloud Scheduler to manage cron job services,Dataflow to unify stream and batch data processing, andContainer Registry to securely store Docker container images. We’re always innovating, and Google Cloud helps our small team accelerate the development and deployment of new services.Lindsey: It’s exciting to hear your story and the many different ways that Google Cloud technology has been able to support you along the way. You’re creating something that affects change on many levels—from how people save and give to how businesses and nonprofits can engage.Since it is also Pride month, I want to change focus for a minute and talk about how being part of the LGBTQ+ community impacted your approach to starting bunny.money?Fabien: I believe we all belong to several communities (family, friends “tribes,” sports, group of interests) that are different layers of our own identity and way of life. I’m part of the LGBTQ+ community, and I’m also an immigrant for example. I’m now a French-American, as is my husband, and we live in San Francisco. But even as a couple, we still had to live apart for several years—he in Paris and I in San Francisco—as we worked through issues with his U.S. work visa (same sex weddings were not possible at that time at the federal level, we couldn’t be under the same visa application).Fortunately, the LGBTQ+ community can be like an extended family, both professionally and personally. Personally, I’ve had the support of friends as my husband and I dealt with immigration and work challenges. And professionally, I’ve experienced incredible support in the startup world with nonprofits such asStartOut, which provides key resources to help LGBTQ+ entrepreneurs grow their businesses.Lindsey: I can only imagine the emotional toll that being apart created for you and your husband and I’m so glad that it eventually worked out. My wife is Austrian and while we are fortunate to be here together, this intersectionality has created an additional layer of complexity for us over the years as we have started a family. Do you have any advice for others in the LGBTQ+ community looking to start and grow their own companies? You mentioned StartOut, and I know there are additional organizations LGBTQ+ entrepreneurs can turn to for help, includingLesbians who Tech,Out in Tech,High Tech Gays (HTG) – Queer Silicon Valley, andQueerTech NYC (Meetup).Fabien: I would suggest really exploring what you’re passionate about. I’ve enjoyed focusing on saving and finances since I was young and have always been passionate about giving back. Being part of the LGBTQ+ community—or really any community that’s viewed as an “outsider”—gives you the opportunity to think differently. When you bring your passion and life experiences together, you can start to imagine new ways of doing things. By engaging in your communities, it can be easier to find others who share your experiences, interests, and even values. You bring the best from each world.Since LGBTQ+ founders and entrepreneurs might belong to several groups, it’s good to explore all available avenues and resources, including the organizations you mentioned earlier. We can always learn and accomplish more when we work together. I’ve experienced that both in the LGTBQ+, immigrant and Fintech communities.Lindsey: The importance of community underlies so many aspects of your identity as a founder, as someone who has moved to the US from France, and as a member of the LGBTQ+ community. I’m so glad that you’ve sought out – and received – support along the way. I agree it’s so important  for others to seek out this community and support.  And to close, would you be able to share any next steps for bunny.money?Fabien: We’re looking forward to helping customers build more sustainable and inclusive financial futures on our platform. We’ll continue contributing to positive change in the world by rolling out new AI-powered services to enable ethical investing and personalized giving and impact programs. As we build this first banking app for personal and workplace giving, our goal is to benefit all communities by bridging the gap between businesses and people—which is why we’re excited to continue working with partners like Google for Startups andGV (GV offers us valuable mentor sessions during our accelerator program at StartOut).If you want to learn more about how Google Cloud can help your startup, visit our pagehere to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.Related ArticlePride Month: Q&A with Beepboop founders about more creative, effective approaches to learning a new languageRead how Beepboop democratizes language instruction by helping students learn to speak Spanish and English in dynamic, fun environments l…Read Article
Quelle: Google Cloud Platform

Mercari leverages Google's vector search technology to create a new marketplace

Mercari is one of the most successful marketplace services in recent years, with 5.3 million active users in the US and 20 million active users in Japan. In Oct 2021, the company launched a new service Mercari Shops in Japan that allows small business owners and individuals to open their e-commerce portal in 3 minutes. At the core of the new service, Mercari introduced Google’s vector search technology to realize the crucial part: creating a new marketplace for small shops using “similarity”.Mercari has 5.3M active users in the USThe Challenge: collection of shops doesn’t make a marketplaceAt the time of the launch, Mercari Shops was just a collection of small e-commerce sites where shoppers could only see the items sold by each shop one by one. For the shoppers, it was a somewhat painful experience to go back to the top page and choose a shop each time. This loses the most important value of the service; an enjoyable shopping experience for the shoppers.The challenge of Mercari Shops: shoppers were only able to browse the items from the selected shopShoppers would love something like “a real marketplace on smartphones” where they can easily browse hundreds of items from a wide variety of shops with a single finger gesture. But how do you manage the relationships across all the items to realize the experience? You would need to carefully define millions of item categories and SKUs shared across the thousands of sellers, and keep maintaining it all by manual operation of support staff. It also requires the sellers to search and choose the exact category for each item to sell. This is the way traditional marketplace services are built, involving much operational cost, and also losing another key value of Mercari Shops that anyone can build an e-commerce site within 3 minutes.How about using a recommendation system? The popular recommendation algorithm such as collaborative filtering usually requires large purchase or click histories to recommend other items, and doesn’t work well for recommending new items or long-tail items that don’t have any relationship with existing items. Also, collaborative filtering only memorizes the relationships between the items, such as “many customers purchase/view these other items also”. Meaning, it doesn’t actually make any recommendations with insights by looking at the item descriptions, names, images or many other side features.So Mercari decided to introduce a new way: using “similarity” to create a marketplace. A new marketplace created by similarityWhat does it mean by similarity? For example, you can define a vector (a list of numbers) with three elements (0.1, 0.02, 0.03) to represent an item that has 10% affinity to the concept of “fresh”, 2% to “vegetable”, and 30% to “tomato”. This vector represents the meaning or semantics of “a fresh tomato” as an item. If you search near vectors around it, those items would also have similar meaning or semantics – you will find other fresh tomatoes (note: this is a simplified explanation of the concept and the actual vectors have much complex vector space). Vector search finds items with similar meaningThis similarity between vectors exemplifies the marketplace in Mericari Shops that allows the shopper to browse all the similar items collected on a page. You don’t need to define and update item categories and SKUs manually to connect between the millions of items from thousands of sellers. Instead, machine learning (ML) algorithms extract the vectors from each item automatically, every time a seller adds a new item or updates an item. This is exactly the same way Google uses for finding relevant contents on Search, YouTube, Play and other services; called Vector Search.Enabled by the technology, now the shoppers of Mercari Shops can easily browse relevant items sold by different shops on the same page.The marketplace created with the similarity: shoppers can easily browse the relevant itemsVector search made easy with Matching EngineLet’s take a look at how Mercari built the marketplace using the vector search technology. With analytics results and experiments, they found that the item description written by the sellers represents the value of each item well, compared to other features such as the item images. So they decided to use item description texts to extract the feature vector of each item. Thus, the marketplace of Mercari Shops is organized by “how items are similar to each other in the text description”.Extracting feature vectors from the item description textsFor extracting the text feature vector, they used a word2vec model combined with TF-IDF. Mercari also tried other models such as BERT, but they decided to use word2vec as it’s simple and lightweighted, suitable for production use with less GPU cost for prediction.There was another challenge. Building a production vector search infrastructure is not an easy task. In the past, Mercari built their own vector search from scratch for an image search service. It took for them to assign a dedicated DevOps engineer, let them build Kubernetes servers, design and maintain the service. Also, they had to build and operate a data pipeline for continuous index update. To keep the search results fresh, you need to update the vector search index every hour with newly added items using the data pipeline. This pipeline had some incidents in the past and consumed DevOps engineers’ resources. Considering these factors, it was almost impossible for Mercari Shops to add a new vector search under a limited resource. Instead of building it from scratch, they introduced Vertex AI Matching Engine. It’s a fully managed service that shares the same vector search backend with the major Google services such as Google Search, YouTube and Play. So there is no need to implement the infrastructure from scratch, maintain it, and design and run the index update pipeline by yourself. Yet, you can quickly take advantage of the responsiveness, accuracy, scalability and availability of Google’s latest vector search technology.The feature extraction pipelineMercari Shops’ search service has two components: 1) feature extraction pipeline and 2) vector search service. Let’s see how each component works.The feature extraction pipelineThe feature extraction pipeline is defined with Vertex AI Pipelines, and is invoked by Cloud Scheduler and Cloud Functions periodically to initiate the following process:Get item data: The pipeline makes a query BigQuery to fetch the updated item dataExtract feature vector: The pipeline runs predictions on the data with the word2vec model to extract feature vectorsUpdate index: The pipeline calls Matching Engine APIs for adding the feature vectors to the vector index. The vectors are also saved to Cloud BigtableThe following is the actual definition of the feature extraction pipeline on Vertex AI Pipelines:The feature extraction pipeline definition on Vertex AI PipelinesVector search serviceThe second component is the vector search service that works in the following manner:The vector search serviceClient makes a query: a client makes a query to the Cloud Run frontend specifying an item idGet the feature vector: get a feature vector of the item from BigtableFind similar items: using Matching Engine API, find similar items with the feature vectorReturns the similar items: returns item ids of the similar itemsBy introducing Matching Engine, Mercari Shops was able to build the production vector search service within a couple of months. As of one month after launching the service, they haven’t seen any incidents. From development to production, only a single ML engineer (the author) implements and operates the whole service.Looking aheadWith the successful introduction, Mercari Shops is now working on adding more functionalities and extending the service to future shop projects. For example, Matching Engine has a filter vector match function that applies simple filters to the search results.  With this function, they may only show “on sale” items, or exclude items from specific shops. Also, Matching Engine will support a streaming index update soon that would allow the users to find items as soon as they are added by the sellers. Vertex AI Feature Store looks attractive too as a replacement for the Cloud Bigtable as the repository of feature vectors with its additional functionality including feature monitoring for better observability on the service quality. With those Google Cloud technologies and products, Mercari can turn their new ideas into reality with less time and resources, adding significant value to their business. 
Quelle: Google Cloud Platform

Announcing general availability of Confidential GKE Nodes

Today, we’re excited to announce the general availability of Confidential GKE Nodes. Many organizations have made Google Kubernetes Engine (GKE) the foundation of their modern application architectures. While the benefits of containers and Kubernetes can outweigh that of traditional architectures, moving to and running those apps in the cloud often entails careful planning to minimize risk and potential data exposure. To help increase security of your GKE clusters, Confidential GKE Nodes can be used.Part of the growing Confidential Computing product portfolio, Confidential GKE Nodes leverage hardware to make sure your data is encrypted in memory. The GKE workloads you run today can run confidentially without any code changes on your end. Bringing confidential computing to your container workloadsWith Confidential GKE Nodes, you can achieve encryption in-use for data processed inside your GKE cluster, without significant performance degradation. Confidential GKE Nodes are built on the same technology foundation as Confidential VM and utilize AMD Secure Encrypted Virtualization (SEV). This feature allows you to keep data encrypted in memory with node-specific, dedicated keys that are generated and managed by the processor. The keys are generated in hardware during node creation and reside solely within the processor, making them unavailable to Google or other nodes running on the host. Confidential GKE Nodes also leverage Shielded GKE nodes to offer additional protection against rootkit and bootkits, helping to ensure the integrity of the operating system you run on your Confidential GKE Nodes.Mixed node pools and stateful workloads Two new features have been added for the general availability release of Confidential GKE Nodes: mixed node pool support and PersistentVolumes. Mixing confidential node pools with non-confidential node poolsConfidential GKE Nodes can be enabled as a cluster-level security setting or a node pool-level security setting. When enabled at the cluster level, Confidential GKE Nodes enforce the use of Confidential VMs on all worker nodes. Worker nodes in a cluster can only use confidential nodes, and confidential computing can not be disabled on individual node pools. All worker nodes, including the workloads running on them, are encrypted in-use. When enabled at the node level, Confidential GKE Nodes enforce the use of Confidential VMs on specific node pools, so only worker nodes in specified node pools are running confidentially. This new capability can allow a single GKE cluster to run both confidential and non-confidential workloads. Creating regular node pools and confidential node pools in a single cluster can help minimize cluster management. To learn more, see our guide to enabling Confidential GKE Nodes on node pools.Supporting PersistentVolumes for stateful container workloadsConfidential GKE Nodes are great for protecting data in stateless and stateful workloads. Confidential GKE Nodes recently added support for PersistentVolume resources. In GKE, a PersistentVolume is a cluster resource that Pods can use for durable storage and is typically backed by a persistent disk. The pairing of PersistentVolumes with Confidential GKE Nodes is ideal for containerized applications that require block storage.PricingThere is no additional cost to deploy Confidential GKE Nodes, other than the cost of Compute Engine Confidential VM.Get started with this game-changing technologyCreating a GKE cluster that uses Confidential GKE Nodes on all nodes is easy. Simply go to the Cloud Console, click Kubernetes Engine and then click Clusters. Select “Create” and then “Configure” on GKE Standard. Under Cluster, there is a security section where you click the checkbox that says “Enable Confidential GKE Nodes.”GKE clusters can be enabled to run as Confidential under the Security Setting for Kubernetes Engine.Confidential computing transforms the way organizations process data in the cloud while preserving confidentiality and privacy. To learn more, read about our Confidential VMs and get started using your own confidential GKE Nodes today.Related ArticleA deeper dive into Confidential GKE Nodes—now available in previewConfidential GKE Nodes, now in preview, encrypt the memory of your nodes and the workloads that run on top of them.Read Article
Quelle: Google Cloud Platform

Anthos on-prem and on bare metal now power Google Distributed Cloud Virtual

Last year, we announced Google Distributed Cloud (GDC), a portfolio of hardware, software, and services that will bring our infrastructure to the edge and into your data centers. In March, Google Distributed Cloud Edge became generally availableto deliver an integrated hardware and software solution for new telco and enterprise edge workloads. And today, we are pleased to share our next update for Google Distributed Cloud Virtual — a software-and services-only solution that brings our existing Anthos on-prem (for VMware vSphere and Anthos bare metal services) into the GDC portfolio under this unified new product family. Customers of Anthos on-premises (now known as GDC Virtual) will continue to enjoy the consistent management and developer experience they have come to know and expect, with no changes to current capabilities, pricing structure, or look and feel across user interfaces and will continue to see consistent roadmap additions. For customers just getting to know GDC Virtual, its capabilities will round out our GDC Edge and Hosted offerings which are designed to accelerate your cloud transformation.  Taken together, the Google Distributed Cloud portfolio for Edge, Hosted, and Virtual provides a uniform set of experiences for development, security, and management across any IT environment you choose, backed by a common Anthos API. This includes the ability to select across system form factor types, choosing between software only or integrated hardware and software solutions, and whether you prefer to be self or fully managed, by Google or another trusted partner. Based on your unique business and workload needs, you choose the scenario that works best for your organization.Cloud-managed and deployed onto your infrastructure, GDC Virtual provides a software only extension of Google Cloud allowing you to: Automate provisioning and management of GKE clusters on VMs and existing bare metal infrastructure with the requirements and form factors you choose and use the Google Cloud Console to provision Anthos clusters on vSphereEnable developers to build and deploy container-based workloads to Kubernetes directly or an application runtimeApply federated security, access control, and identity management across cloud and on-premises clustersTo further illustrate these capabilities, here are some examples of where customers might choose to deploy GDC Virtual. A customer has  a significant investment in their own VM environment. Selecting GDC  Virtual enables them to leverage their existing infrastructure to run new and modernized applications.A customer wants to bring advanced AI/ML workloads into each store. Given the need to deploy in their establishments, they have specific requirements for footprint and hardware.  With GDC Virtual, the new workloads can be deployed on hardware and in a footprint that meets their specific needs.A large auto manufacturer is on a journey to migrate their applications to the cloud.  As part of this journey, they are leveraging their existing on-premises investments. By choosing GDC Virtual, they would be able to  modernize applications in-place  before migrating to the cloud.GDC Virtual Adoption is Growing Rapidly We built Anthos three years ago to deliver a consistent cloud operational model and developer experience, and adoption has continued to grow exponentially. In fact, between 2021 and 2022, the number of customers for Anthos products grew by over five times. This includes Anthos bare metal customers (now Google GDC Virtual) growing by over four times over that same period. Now customers have more ways to consume Anthos – GDC Edge, Virtual, and Hosted – which are all powered by Anthos. Some notable customers include:TELUS – For TELUS, a leading provider of communications and technology, Anthos on-premises capabilities help enable their new Multi-Access Edge Computing (MEC) use cases. This new Telco edge solution, moves the processing and management of traffic from a centralized cloud to the edge of TELUS’ 5G network, making it possible to deploy applications and process content closer to its customers, and thus yielding several benefits including better performance, security, and customization. This includes enabling a new Connected Worker Safety solution that can be applied across a range of business verticals to help improve safety, prevent injury, and save lives. More details on this solution can be found here.  Major League Baseball (MLB) – MLB supports 30 teams spread across the US and Canada, running workloads in the cloud and at the edge with on-premises data centers at each of their ballparks. By using Anthos, they can containerize those workloads and run them in the location that makes the most sense for the application. In particular, this enables scenarios where local computing needs to occur in the park for latency reasons, such as delivering stats in the stadium, to fans, or to broadcast, or to the scoreboard. This has enabled data democratization and distribution to its 30 teams and can support improved time to insight and fan engagement.Google Corporate Engineering – With Anthos, Google Corporate Engineering is working to make operations consistent and to reduce costs across Google Cloud and on-prem distributed cloud environments by using common tooling. In 2021, Google began running its first production workloads in edge environments at corporate offices with Anthos Bare Metal and in 2022 added the first production workloads in hosted data centers. By the end of the year Google plans to have a sizable portion of our virtualized platform migrated to GDC Virtual; this final migration will include a number of enterprise workloads that are critical for the operation of our company, including security, financials, and IT. Freedom of choice with Google Distributed CloudTo us, supporting our customers’ transformation requirements means providing a Google Distributed Cloud portfolio that embraces openness and choice. GDC Virtual offers a new consumption model for customers built upon a proven Anthos stack as a software only deployment option on your infrastructure. This enables modernization efforts to progress in place at a pace that makes sense for your business. With this update for GDC Virtual, the Google Distributed Cloud portfolio can now enable consistent operations from on-premises, to edge, to cloud. To learn more about GDC Virtual check out the Google Distributed Cloud website. Current Google Cloud customers can also learn about Google Distributed Cloud by adding Anthos Service Mesh and Config Management to their GKE clusters today!Related ArticleIntroducing Google Distributed Cloud—in your data center, at the edge, and in the cloudGoogle Distributed Cloud runs Anthos on dedicated hardware at the edge or hosted in your data center, enabling a new class of low-latency…Read Article
Quelle: Google Cloud Platform