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Google Cloud and Databricks announced a new partnership to deliver Databricks at global scale on Google Cloud. Enterprises can deploy or migrate Databricks Lakehouse to Google Cloud to combine the benefits of an open data cloud platform with greater analytics flexibility, unified infrastructure management, and optimized performance. And we are excited to announce that this partnership, which we announced in February, is fully available to the public today! With the general availability of Databricks on Google Cloud, those customers that were part of our extended public preview can now leverage a fully publicly generally available solution. And for those customers who were waiting for a publically available solution, they can now deploy Databricks via our Marketplace. The Google Cloud Marketplace enables customers to explore, launch, and manage production-grade solutions in just a few clicks.On top of the solution being generally available, we are also excited to announce that Databricks on Google Cloud is also now available in new regions in Europe, the Middle East, and Asia as we continue to extend the regional availability in North America.Start seeing results in your organization todayCustomers are already seeing great results from this solution. Here is what Harish Kumar, the Global Data Science Director at Reckitt, had to say at the Databricks on Google Cloud launch event:“Databricks on Google Cloud simplifies the process of driving any number of use cases on a scalable compute platform – there is no need to rebuild the architecture and we can reuse our datasets, we can reuse our PySpark scripts, we can reuse our modules – and hence reduce the planning cycles that are needed to deliver a solution for each business question or problem statement that we use.”What this launch means for usersOpennessOur partnership provides enterprises with an open approach that enables greater flexibility for data management and analytics strategies, which builds upon Google’s ongoing commitment to an open cloud. This openness ensures customers have interoperability and portability, including those that want to use multiple public clouds and open source technology, like Kubernetes, MLflow, Apache Spark, and Delta Lake for their analytics applications. FlexibilityIntegrations with Google Cloud Storage, Pub/Sub, BigQuery, Looker, and Google Kubernetes Engine allow Databricks users to quickly flip between services within Google Console to unify the experience and build the analytics applications they need to move the business forward.Data can be messy, siloed, and slow, and requires many departments across organizations to collaborate, including IT, analytics, and business users. With deep integrations between Google Cloud’s data analytics services and Databricks, enterprises can now store, process, and analyze any type or volume of data. SecurityGoogle Cloud’s security model, world-scale infrastructure, and unique capability to innovate will help keep your organization secure and compliant. Databricks on Google Cloud enables customers to rapidly provision Databricks on Google Cloud’s global network, with advanced security and data protection controls required for highly-regulated industries. Data analytics partnershipsOur joint partners and systems integrators in the analytics ecosystem are committed to ensuring seamless deployments, integrations, and expertise with Databricks on Google Cloud, including Accenture, Cognizant, Collibra, Confluent, Deloitte, Fishtown Analytics, Fivetran, Immuta, Informatica, Infoworks, Insight, MongoDB, Privacera, Qlik, SoftServe, Slalom, Tableau, TCS and Trifacta among others.Get started TodayWe’re looking forward to helping customers discover Databricks on Google Cloud and put all of these capabilities to work. Get started by exploring Databricks on Google Cloud or access it through the Google Cloud console.
Quelle: Google Cloud Platform

5. Mai 2021

da Agency

Retire your tech debt: Move vSphere 5.5+ to Google Cloud VMware Engine

It can happen so easily. You get a little behind on your payments. Then you start falling farther and farther behind until it becomes almost impossible to dig yourself out of debt. Tech debt, that is. IT incurs a lot of tech debt when it comes to keeping up infrastructure; most IT departments are already running as lean as they possibly can. Many VMware shops are in a particularly tough spot, especially if they’re still running on vSphere 5.5. If that describes you, it’s time to ask yourself how you intend to get out of this tech debt? General support for vSphere 5.5 ended back in September 2018, and technical guidance one year later. General support for 6.0 ended in March 2020, support for 6.5 ends November 15 of this year, and even the end of general support for vSphere 6.7 is only a couple of years away (November, 2022)! If you’re still running vSphere 5.5, moving to vSphere 7.0 is the right thing to do.But doing so is hard if you’ve fallen into a deep tech-debt hole.Traditionally, it means moving all your outdated vSphere systems through all the interim releases until you’ve migrated all your systems to the latest version. That involves upgrading hardware, software, and licenses, as well as all the additional work that goes along with the upgrades. Then, as soon as you’re done, the next upgrade cycle is already upon you. Making the task even more daunting, VMware HCX—the company’s application mobility service—will also stop supporting 5.5 soon, making migration even more complicated.If this paints an unsightly picture, don’t despair. You have the opportunity, right now, to easily retire your technical debt and be debt-free from here on out by migrating to Google Cloud VMware Engine. And you can migrate before you have to upgrade to the next vSphere release just to get migration support. Not only will you still be able to migrate to vSphere 7 using HCX, but even better, you don’t have to do the digging yourself.The cloud breaks the cycle of debtIf the effort and resources required to move was too steep a price before, now it’s a viable option with Google Cloud VMware Engine. With cloud-based infrastructure, you can not only migrate to the latest release of vSphere, but you can also take your workload—lock, stock, and barrel—out of your data center and put it into Google Cloud. Moving to Google Cloud VMware Engine makes the migration task fast and simple. Never again will you have to deal with spreadsheets to track how many watts of cooling you need for your data center, buy additional equipment, or manage upgrades.Migrating to the cloud is also the first step toward getting out of the business of managing your data center and into embracing an OpEx subscription model. And you can begin moving workloads to the cloud in increments, without having to worry about all the nuances — it’s all done for you.Work in a familiar environment and expand your toolsetOne of the biggest benefits of Google Cloud VMware Engine is that it offers the same, familiar VMware experience you have now. All the applications running on vSphere 5.5 can immediately run on a private cloud in Google Cloud VMware Engine with no changes. You’ll now be running on the latest release of vSphere 7, and when VMware releases patches, updates, and upgrades, Google Cloud keeps the infrastructure up to date for you. And as a VMware administrator, you can use the same tools that you’re familiar with on-premises.Migration doesn’t have to be a long, arduous processGoogle Cloud VMware Engine allows you to leverage your existing virtualized infrastructure to make migration fast and easy. Use familiar VMware tools to migrate your on-premises vSphere applications to vSphere in your own private cloud while maintaining continuity with all your existing tools, policies, and processes. It takes only a few clicks (see our demo video). Make sure you have your prerequisites, enable the Google Cloud VMware Engine API, and follow these 10 steps:Enable the VMware Engine node quota and assign at least three nodes to create your private cloud.Set your roles and permissions.Access the Google Cloud VMware Engine portal.Click ‘Create a private cloud’. This is fast — only about 30 minutes.Select the number of nodes (a minimum of three).Enter a CIDR range for the VMware management network.Enter a CIDR range for the HCX deployment network.Review your settings.Click Create.Connect an on-prem network to your VMware Engine private cloud or connect using a point-to-site VPN connection. Google Cloud VMware Engine supports multi-region networking with VPC global routing, which allows VPC subnets to be deployed in any region worldwide, greatly simplifying networking.When you use VMware HCX to migrate VMs from your on-premises environment to Google Cloud VMware Engine, VMware HCX abstracts vSphere resources running both on-prem and in the cloud and presents them to applications as one continuous resource to create a hybrid infrastructure.By partnering with Google Cloud, you can erase your tech debt and get out of the time-consuming, resource-draining business of data center management. Then, once your VMware-based workloads are running on Google Cloud VMware Engine, you can start modernizing your applications with Google Cloud services, including AI/ML, low-cost storage, and disaster recovery solutions. Check out the variety of pricing options for the service, from pre-pay with discounts up to 50% to pay-as-you-go and annual commitments.Related ArticleZero-footprint DR solution with Google Cloud VMware Engine and ActifioLearn how to use Actifio data management software plus Google Cloud VMware Engine to create a dynamic, low-cost DR site in the cloud.Read Article
Quelle: Google Cloud Platform

5. Mai 2021

da Agency

Diving into your documents with DocAI

We recently announced the GA of the Document AI Platform, Google’s solution for automating and validating documents to streamline document workflows. Important business data is not always readily available in computer-readable formats. This is what we consider dark formats such as pdfs, handwritten forms and images. The platform is a console for document processing where customers can quickly access all parsers, tools, and solutions. Workflow solutions, built on our specialized parsers with models for common enterprise document types such tax forms, invoices, receipts and more, Lending DocAI and Procurement DocAI are now also in GA.So why use it? Your business is most likely sitting on a treasure trove of unstructured data, or maybe you have document workflows that require several manual steps. DocAI can help you programmatically extract data for gathering insights with data analytics and help automate tedious and error-prone tasks. Use one of our client libraries to ingest your documents and produce structured data in our new unified document format.Unified document formatThe unified document format (document.proto) is the protocol used to represent all metadata about a document in a standardized, universal format. It is an efficient, standoff format—where the content is kept separate from the annotations. This gives full flexibility to losslessly represent any annotation or attribute of a document or its content whether annotated by humans or an algorithm.It was created to make building document-based workflow applications easy across tools, components, platforms, and languages inside and outside of DocAI. It is a protocol buffer based format, allowing efficient, flexible encodings—typically binary or json.The format currently allows the representation of rich OCR representations as well as extracted entities so let’s dive in.Document representation – read itThe form parsers return the raw representation of the document content. In many documents, the layout structure is often as important as the actual text. The layout elements include several types such as tokens, lines, paragraphs, blocks, form fields, tables and visual elements. The format allows the representation of rich OCR representations in a hierarchical structure. You can use the layout bounding poly coordinates to detect and highlight the tokens in a UI.We’ve drafted a set of notebooks to help you quickly get started with the service. I’ll walk through a sample document with our general specialized form parser notebook.Extracted data – understand itHere is where the core of the structured data appears. If you’re processing a generic form, DocAI will extract the relevant key value pairs. If you’re using one of our specialized parsers for a form type such as an invoice, receipt, utility statement, etc. the data extracted will be merged into a predefined schema. To help you with your document processing journey we also provide tools for classification and splitting multi-page, multi-form packets. You could imagine the use case of needing to classify and split individual forms in a large mortgage packet such as W2s, W9s, payslips, etc. The classifier will label the document/entity type and the splitter will intelligently understand where the logical boundaries of the different form types start and end.ExtractionNot only do you get the “question and answers” from your document, you also get entity normalization and confidence scores. In our specialized parsers, if a certain field is a monetary or date type, the API will also provide an appropriate entity type. This makes it much easier when integrating with other systems or a database with strict schema types.For data assurance, we provide a score between 0 and 1 on the platform’s confidence for that entity. We are able to inspect the confidence scores for both the keys and the associated values on a generic form.We understand that accuracy is critical for business processes so you can use Human-in-the-Loop AI to incorporate a customizable human review workflow with trusted reviewers within their own or partner organizations. You can configure the human review to trigger if the whole document or specific fields do not meet confidence score at your choosing. Including human participation in ML processes allows AI and humans to work together for the best possible results for customers.Last but not least, making it useful is up to you! We hope we have inspired you to try out Document AI in your app or service. By using the platform you can build tools that reduce manual steps to prevent human errors, integrate other Google services for robust data processing or track documents changes for an audit. You can head over to the DocAI Platform in the Google Cloud console or try out one or our codelabs.Related ArticleCustomers cut document processing time and costs with DocAI solutions, now generally availableDocument AI platform, Lending DocAI and Procurement DocAI are generally available.Read Article
Quelle: Google Cloud Platform

5. Mai 2021

da Agency

Debugging your Proxyless gRPC service mesh

Proxyless gRPC applications in a service mesh now support many of the same features as deployments with a sidecar Envoy proxy, but in the past it has been difficult to get application-level insight into problems with specific nodes in the mesh. Today, we are happy to announce new tools, examples, and documentation to make it easier to debug your Proxyless gRPC applications. Proxyless gRPC now includes an admin API to allow live debugging of nodes in your mesh, and support for the xDS CSDS protocol to dive deeper into per-node control plane configurations to identify and resolve any issues. Further, we provide documentation and sample code illustrating how to add OpenCesus instrumentation to your gRPC clients and servers to send metric and tracing data to Cloud Monitoring and Cloud Trace.As a network library, gRPC provides some predefined admin services to make debugging easier. For example, there is a channel tracing service named Channelz (see gRPC blog). With Channelz, you can access the metrics about the requests going through each channel, like how many RPCs have been sent, how many succeeded or failed, and much more. Each existing admin service is packaged as a separate library, and the documentation of the predefined admin services is usually scattered. It can be time consuming to get the dependency management, module initialization, and library import right for each one of them. Recently, gRPC introduced admin interface APIs, which provide a convenient way to create a gRPC server to expose admin services. With this, any new admin services that you may add in the future are automatically available via the admin interface just by upgrading your gRPC version.Debugging a large service mesh can be a complex task. Unexpected routing behaviors could be due to a misconfiguration, unhealthy backends or issues in the control or data plane. As part of the admin interface API, gRPC can now expose the xDS configuration, the service mesh configuration that Traffic Director, our fully-managed service mesh, sends to gRPC applications. This configuration is exposed via the CSDS service, which you can easily start by using the admin interface APIs. Our grpcdebug CLI tool prints human-readable output based on the information it fetches from a target gRPC application.You can now also instrument gRPC C++, Go, and Java clients and servers with the OpenCensus library to send metrics and tracing to Cloud Monitoring and Cloud Trace. While gRPC’s OpenCensus integration has been available for a long time, our user guide and example code demonstrate clearly how to configure OpenCensus instrumentation in the context of a service mesh and ensure that traces are compatible across both Proxyless gRPC and Envoy-sidecar applications. After instrumenting your Proxyless gRPC application, you’ll be able to view traces such as the following example of our gRPC Wallet mesh:For more information about using gRPC with Traffic Director and these new features, see the following links:Traffic Director with proxyless gRPC services overviewRelated ArticleTraffic Director and gRPC—proxyless services for your service meshWith the addition of xDS API support, you can now use Traffic Director with proxyless gRPC services.Read Article
Quelle: Google Cloud Platform